Initial commit

preprocess/tools/note_transcription/model.py

@@ -0,0 +1,522 @@
+# https://github.com/RickyL-2000/ROSVOT
+import math
+import sys
+import traceback
+import json
+import time
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import librosa
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+
+from .utils.os_utils import safe_path
+from .utils.commons.hparams import set_hparams
+from .utils.commons.ckpt_utils import load_ckpt
+from .utils.commons.dataset_utils import pad_or_cut_xd
+from .utils.audio.mel import MelNet
+from .utils.audio.pitch_utils import (
+    norm_interp_f0,
+    denorm_f0,
+    f0_to_coarse,
+    boundary2Interval,
+    save_midi,
+    midi_to_hz,
+)
+from .utils.rosvot_utils import (
+    get_mel_len,
+    align_word,
+    regulate_real_note_itv,
+    regulate_ill_slur,
+    bd_to_durs,
+)
+from .modules.pe.rmvpe import RMVPE
+from .modules.rosvot.rosvot import MidiExtractor, WordbdExtractor
+
+
+@torch.no_grad()
+def infer_sample(
+    item: Dict[str, Any],
+    hparams: Dict[str, Any],
+    models: Dict[str, Any],
+    device: torch.device,
+    *,
+    save_dir: Optional[str] = None,
+    apply_rwbd: Optional[bool] = None,
+    # outputs
+    save_plot: bool = False,
+    no_save_midi: bool = True,
+    no_save_npy: bool = True,
+    verbose: bool = False,
+) -> Dict[str, Any]:
+    if "item_name" not in item or "wav_fn" not in item:
+        raise ValueError('item must contain keys: "item_name" and "wav_fn"')
+    
+    item_name = item["item_name"]
+    wav_src = item["wav_fn"]
+
+    # Decide RWBD usage
+    if apply_rwbd is None:
+        apply_rwbd_ = ("word_durs" not in item)
+    else:
+        apply_rwbd_ = bool(apply_rwbd)
+
+    # Models
+    model = models["model"]
+    mel_net = models["mel_net"]
+    pe = models.get("pe")
+    wbd_predictor = models.get("wbd_predictor")
+
+    if wbd_predictor is None and apply_rwbd_:
+         raise ValueError("apply_rwbd is True but wbd_predictor model is not provided in models")
+
+    # ---- Prepare Data  ----
+    if isinstance(wav_src, str):
+        wav, _ = librosa.core.load(wav_src, sr=hparams["audio_sample_rate"])
+    else:
+        wav = wav_src
+        if not isinstance(wav, np.ndarray):
+            wav = np.asarray(wav)
+    wav = wav.astype(np.float32)
+
+    # Calculate timestamps and alignment lengths
+    wav_len_samples = wav.shape[-1]
+    mel_len = get_mel_len(wav_len_samples, hparams["hop_size"])
+    
+    # Word boundary preparation
+    mel2word = None
+    word_durs_filtered = None
+    
+    if not apply_rwbd_:
+        if "word_durs" not in item:
+             raise ValueError('apply_rwbd=False but item has no "word_durs"')
+        
+        wd_raw = list(item["word_durs"]) 
+        min_word_dur = hparams.get("min_word_dur", 20) / 1000
+        word_durs_filtered = []
+        
+        for i, wd in enumerate(wd_raw):
+            if wd < min_word_dur:
+                if i == 0 and len(wd_raw) > 1:
+                    wd_raw[i + 1] += wd
+                elif len(word_durs_filtered) > 0:
+                    word_durs_filtered[-1] += wd
+            else:
+                word_durs_filtered.append(wd)
+
+        mel2word, _ = align_word(word_durs_filtered, mel_len, hparams["hop_size"], hparams["audio_sample_rate"])
+        mel2word = np.asarray(mel2word)
+        if mel2word.size > 0 and mel2word[0] == 0:
+             mel2word = mel2word + 1
+        
+        mel2word_len = int(np.sum(mel2word > 0))
+        real_len = min(mel_len, mel2word_len)
+    else:
+        real_len = min(mel_len, hparams["max_frames"])
+
+    T = math.ceil(min(real_len, hparams["max_frames"]) / hparams["frames_multiple"]) * hparams["frames_multiple"]
+
+    # ---- Input Tensors & Padding ----
+    target_samples = T * hparams["hop_size"]
+    wav_t = torch.from_numpy(wav).float().to(device).unsqueeze(0) # [1, L]
+    if wav_t.shape[-1] < target_samples:
+        wav_t = pad_or_cut_xd(wav_t, target_samples, 1)
+    
+    # ---- Pitch Extraction ----
+    if pe is not None:
+        f0s, uvs = pe.get_pitch_batch(
+            wav_t,
+            sample_rate=hparams["audio_sample_rate"],
+            hop_size=hparams["hop_size"],
+            lengths=[real_len], 
+            fmax=hparams["f0_max"],
+            fmin=hparams["f0_min"],
+        )
+        f0_1d, uv_1d = norm_interp_f0(f0s[0][:T])
+        f0_t = pad_or_cut_xd(torch.FloatTensor(f0_1d).to(device), T, 0).unsqueeze(0)
+        uv_t = pad_or_cut_xd(torch.FloatTensor(uv_1d).to(device), T, 0).long().unsqueeze(0)
+        pitch_coarse = f0_to_coarse(denorm_f0(f0_t, uv_t)).to(device)
+        f0_np = denorm_f0(f0_t, uv_t)[0].detach().cpu().numpy()[:real_len]
+    else:
+        f0_t = uv_t = pitch_coarse = None
+        f0_np = None
+
+    # ---- Mel Extraction ----
+    mel = mel_net(wav_t) # [1, T_padded, C]
+    mel = pad_or_cut_xd(mel, T, 1)
+    
+    # Construct non-padding mask
+    mel_nonpadding_mask = torch.zeros(1, T, device=device)
+    mel_nonpadding_mask[:, :real_len] = 1.0
+    
+    # Apply mask to mel (zero out padding)
+    mel = (mel.transpose(1, 2) * mel_nonpadding_mask.unsqueeze(1)).transpose(1, 2)
+    # Re-calculate non_padding bool mask
+    mel_nonpadding = mel.abs().sum(-1) > 0
+
+    # ---- Word Boundary ----
+    word_durs_used = None
+    if apply_rwbd_:
+        mel_input = mel[:, :, : hparams.get("wbd_use_mel_bins", 80)]
+        wbd_outputs = wbd_predictor(
+            mel=mel_input,
+            pitch=pitch_coarse,
+            uv=uv_t,
+            non_padding=mel_nonpadding,
+            train=False,
+        )
+        word_bd = wbd_outputs["word_bd_pred"] # [1, T]
+    else:
+        # Construct word_bd from provided durs
+        mel2word_t = pad_or_cut_xd(torch.LongTensor(mel2word).to(device), T, 0)
+        word_bd = torch.zeros_like(mel2word_t)
+        # Vectorized check
+        word_bd[1:] = (mel2word_t[1:] != mel2word_t[:-1]).long()
+        word_bd[real_len:] = 0
+        word_bd = word_bd.unsqueeze(0) # [1, T]
+        
+        word_durs_used = np.array(word_durs_filtered)
+
+    # ---- Main Inference ----
+    mel_input = mel[:, :, : hparams.get("use_mel_bins", 80)]
+    outputs = model(
+        mel=mel_input,
+        word_bd=word_bd,
+        pitch=pitch_coarse,
+        uv=uv_t,
+        non_padding=mel_nonpadding,
+        train=False,
+    )
+
+    note_lengths = outputs["note_lengths"].detach().cpu().numpy()
+    note_bd_pred = outputs["note_bd_pred"][0].detach().cpu().numpy()[:real_len]
+    note_pred = outputs["note_pred"][0].detach().cpu().numpy()[: note_lengths[0]]
+    note_bd_logits = torch.sigmoid(outputs["note_bd_logits"])[0].detach().cpu().numpy()[:real_len]
+
+    if note_pred.shape == (0,):
+        if verbose:
+            print(f"skip {item_name}: no notes detected")
+        return {
+            "item_name": item_name,
+            "pitches": [],
+            "note_durs": [],
+            "note2words": None,
+        }
+
+    # ---- Post-Processing & Regulation ----
+    note_itv_pred = boundary2Interval(note_bd_pred)
+    note2words = None
+
+    if apply_rwbd_:
+        word_bd_np = outputs['word_bd_pred'][0].detach().cpu().numpy()[:real_len]
+        word_durs_derived = np.array(bd_to_durs(word_bd_np)) * hparams['hop_size'] / hparams['audio_sample_rate']
+        word_durs_for_reg = word_durs_derived
+        word_bd_for_reg = word_bd_np
+    else:
+        word_bd_for_reg = word_bd[0].detach().cpu().numpy()[:real_len]
+        word_durs_for_reg = word_durs_used
+
+    should_regulate = hparams.get("infer_regulate_real_note_itv", True) and (not apply_rwbd_)
+    
+    if should_regulate and (word_durs_for_reg is not None):
+        try:
+            note_itv_pred_secs, note2words = regulate_real_note_itv(
+                note_itv_pred,
+                note_bd_pred,
+                word_bd_for_reg,
+                word_durs_for_reg,
+                hparams["hop_size"],
+                hparams["audio_sample_rate"],
+            )
+            note_pred, note_itv_pred_secs, note2words = regulate_ill_slur(note_pred, note_itv_pred_secs, note2words)
+        except Exception as err:
+            if verbose:
+                _, exc_value, exc_tb = sys.exc_info()
+                tb = traceback.extract_tb(exc_tb)[-1]
+                print(f"postprocess failed: {err}: {exc_value} in {tb[0]}:{tb[1]} '{tb[2]}' in {tb[3]}")
+            # Fallback
+            note_itv_pred_secs = note_itv_pred * hparams["hop_size"] / hparams["audio_sample_rate"]
+            note2words = None
+    else:
+        note_itv_pred_secs = note_itv_pred * hparams["hop_size"] / hparams["audio_sample_rate"]
+
+    # ---- Output ----
+    note_durs = [float((itv[1] - itv[0])) for itv in note_itv_pred_secs]
+    
+    out = {
+        "item_name": item_name,
+        "pitches": note_pred.tolist(),
+        "note_durs": note_durs,
+        "note2words": note2words.tolist() if note2words is not None else None,
+    }
+
+    # ---- Saving ----
+    if save_dir is not None:
+        save_dir_path = Path(save_dir)
+        save_dir_path.mkdir(parents=True, exist_ok=True)
+        fn = str(item_name)
+        
+        if not no_save_midi:
+            save_midi(note_pred, note_itv_pred_secs, safe_path(save_dir_path / "midi" / f"{fn}.mid"))
+        
+        if not no_save_npy:
+            np.save(safe_path(save_dir_path / "npy" / f"[note]{fn}.npy"), out, allow_pickle=True)
+
+        if save_plot:
+            fig = plt.figure()
+            if f0_np is not None:
+                plt.plot(f0_np, color="red", label="f0")
+
+            midi_pred = np.zeros(note_bd_pred.shape[0], dtype=np.float32)
+            itvs = np.round(note_itv_pred_secs * hparams["audio_sample_rate"] / hparams["hop_size"]).astype(int)
+            for i, itv in enumerate(itvs):
+                midi_pred[itv[0] : itv[1]] = note_pred[i]
+            plt.plot(midi_to_hz(midi_pred), color="blue", label="pred midi")
+            plt.plot(note_bd_logits * 100, color="green", label="note bd logits x100")
+            plt.legend()
+            plt.tight_layout()
+            plt.savefig(safe_path(save_dir_path / "plot" / f"[MIDI]{fn}.png"), format="png")
+            plt.close(fig)
+
+    return out
+
+
+def load_rosvot_models(ckpt, config="", wbd_ckpt="", wbd_config="", device="cuda:0", verbose=False, thr=0.85):
+    """
+    Load models once to reuse across multiple items.
+    """
+    dev = torch.device(device)
+    
+    # 1. Hparams
+    config_path = Path(ckpt).with_name("config.yaml") if config == "" else config
+    pe_ckpt = Path(ckpt).parent.parent / "rmvpe/model.pt"
+    hparams = set_hparams(
+        config=config_path,
+        print_hparams=verbose,
+        hparams_str=f"note_bd_threshold={thr}",
+    )
+    
+    # 2. Main Model
+    model = MidiExtractor(hparams)
+    load_ckpt(model, ckpt, verbose=verbose)
+    model.eval().to(dev)
+
+    # 3. MelNet
+    mel_net = MelNet(hparams)
+    mel_net.to(dev)
+
+    # 4. Pitch Extractor
+    pe = None
+    if hparams.get("use_pitch_embed", False):
+        pe = RMVPE(pe_ckpt, device=dev)
+        
+    # 5. Word Boundary Predictor (optional but we load if ckpt provided or needed)
+    wbd_predictor = None
+    if wbd_ckpt:
+        wbd_config_path = Path(wbd_ckpt).with_name("config.yaml") if wbd_config == "" else wbd_config
+        wbd_hparams = set_hparams(
+            config=wbd_config_path,
+            print_hparams=False,
+            hparams_str="",
+        )
+        hparams.update({
+            "wbd_use_mel_bins": wbd_hparams["use_mel_bins"],
+            "min_word_dur": wbd_hparams["min_word_dur"],
+        })
+        wbd_predictor = WordbdExtractor(wbd_hparams)
+        load_ckpt(wbd_predictor, wbd_ckpt, verbose=verbose)
+        wbd_predictor.eval().to(dev)
+
+    models = {
+        "model": model,
+        "mel_net": mel_net,
+        "pe": pe,
+        "wbd_predictor": wbd_predictor
+    }
+    return hparams, models
+
+
+class NoteTranscriber:
+    """Note transcription wrapper based on ROSVOT.
+
+    Loads ROSVOT and optional RWBD models once in ``__init__`` and
+    exposes a :py:meth:`process` API that turns an item dict into
+    aligned note metadata for downstream SVS.
+    """
+
+    def __init__(
+        self,
+        rosvot_model_path: str,
+        rwbd_model_path: str,
+        *,
+        rosvot_config_path: str = "",
+        rwbd_config_path: str = "",
+        device: str = "cuda:0",
+        thr: float = 0.85,
+        verbose: bool = True,
+    ):
+        """Initialize the note transcriber.
+
+        Args:
+            ckpt: Path to the main ROSVOT checkpoint.
+            config: Optional config YAML path for ROSVOT.
+            wbd_ckpt: Optional word-boundary checkpoint path.
+            wbd_config: Optional config YAML path for RWBD.
+            device: Torch device string, e.g. ``"cuda:0"`` / ``"cpu"``.
+            thr: Note boundary threshold.
+            verbose: Whether to print verbose logs.
+        """
+        self.verbose = verbose
+        self.device = torch.device(device)
+        self.hparams, self.models = load_rosvot_models(
+            ckpt=rosvot_model_path,
+            config=rosvot_config_path,
+            wbd_ckpt=rwbd_model_path,
+            wbd_config=rwbd_config_path,
+            device=device,
+            verbose=verbose,
+            thr=thr,
+        )
+
+        if self.verbose:
+            print(
+                "[note transcription] init success:",
+                f"device={self.device}",
+                f"rosvot_model_path={rosvot_model_path}",
+                f"rwbd_model_path={rwbd_model_path if rwbd_model_path else 'None'}",
+                f"thr={thr}",
+            )
+
+    def process(
+        self,
+        item: Dict[str, Any],
+        *,
+        segment_info: Optional[Dict[str, Any]] = None,
+        save_dir: Optional[str] = None,
+        apply_rwbd: Optional[bool] = None,
+        save_plot: bool = False,
+        no_save_midi: bool = True,
+        no_save_npy: bool = True,
+        verbose: Optional[bool] = None,
+    ) -> Dict[str, Any]:
+        """Run ROSVOT on a single item and post-process outputs.
+
+        Args:
+            item: Input metadata dict with at least ``item_name`` and ``wav_fn``.
+            segment_info: Optional segment metadata for sliced audio.
+            save_dir: Optional directory for debug artifacts (plots, midis).
+            apply_rwbd: Whether to run RWBD-based word boundary refinement.
+            save_plot: Whether to save diagnostic plots.
+            no_save_midi: If True, skip saving midi.
+            no_save_npy: If True, skip saving numpy intermediates.
+            verbose: Override instance-level verbose flag for this call.
+
+        Returns:
+            Dict with aligned note information for downstream SVS.
+        """
+        v = self.verbose if verbose is None else verbose
+        if v:
+            item_name = item.get("item_name", "")
+            wav_fn = item.get("wav_fn", "")
+            print(f"[note transcription] process: start: item_name={item_name} wav_fn={wav_fn}")
+            t0 = time.time()
+
+        rosvot_out = infer_sample(
+            item,
+            self.hparams,
+            self.models,
+            device=self.device,
+            save_dir=save_dir,
+            apply_rwbd=apply_rwbd,
+            save_plot=save_plot,
+            no_save_midi=no_save_midi,
+            no_save_npy=no_save_npy,
+            verbose=v,
+        )
+
+        out = self.post_process(
+            metadata=item,
+            segment_info=segment_info,
+            rosvot_out=rosvot_out,
+        )
+
+        if v:
+            dt = time.time() - t0
+            print(
+                "[note transcription] process: done:",
+                f"item_name={out.get('item_name','')}",
+                f"n_notes={len(out.get('note_pitch', []) or [])}",
+                f"time={dt:.3f}s",
+            )
+
+        return out
+
+    @staticmethod
+    def _normalize_note2words(note2words: list[int]) -> list[int]:
+        if not note2words:
+            return []
+        normalized = [note2words[0]]
+        for idx in range(1, len(note2words)):
+            if note2words[idx] < normalized[-1]:
+                normalized.append(normalized[-1])
+            else:
+                normalized.append(note2words[idx])
+        return normalized
+
+    @staticmethod
+    def _build_ep_types(note2words: list[int], align_words: list[str]) -> list[int]:
+        ep_types: list[int] = []
+        prev = -1
+        for i, w in zip(note2words, align_words):
+            if w == "<SP>":
+                ep_types.append(1)
+            else:
+                ep_types.append(2 if i != prev else 3)
+            prev = i
+        return ep_types
+
+    def post_process(
+        self,
+        *,
+        metadata: Dict[str, Any],
+        segment_info: Dict[str, Any],
+        rosvot_out: Dict[str, Any],
+    ) -> Dict[str, Any]:
+        """Build aligned note metadata using ROSVOT outputs."""
+        note2words_raw = rosvot_out.get("note2words") or []
+        note2words = self._normalize_note2words(note2words_raw)
+        align_words = [
+            metadata["words"][idx - 1]
+            for idx in note2words_raw
+            if 0 < idx <= len(metadata["words"])
+        ]
+        ep_types = self._build_ep_types(note2words, align_words) if align_words else []
+
+        return {
+            "item_name": rosvot_out.get("item_name", "") if not segment_info else segment_info["item_name"],
+            "wav_fn": metadata.get("wav_fn", "") if not segment_info else segment_info["wav_fn"],
+            "origin_wav_fn": metadata.get("origin_wav_fn", "") if not segment_info else segment_info["origin_wav_fn"],
+            "start_time_ms": "" if not segment_info else segment_info["start_time_ms"],
+            "end_time_ms": "" if not segment_info else segment_info["end_time_ms"],
+            "language": metadata.get("language", ""),
+            "note_text": align_words,
+            "note_dur": rosvot_out.get("note_durs", []),
+            "note_type": ep_types,
+            "note_pitch": rosvot_out.get("pitches", []),
+        }
+
+if __name__ == "__main__":
+
+    items = json.load(open("example/test/rosvot_input.json", "r"))
+    item = items[0]
+
+    m = NoteTranscriber(
+        rosvot_model_path="pretrained_models/rosvot/rosvot/model.pt", 
+        rwbd_model_path="pretrained_models/rosvot/rwbd/model.pt", 
+        device="cuda"
+    )
+    out = m.process(item)
+
+    print(out)

preprocess/tools/note_transcription/modules/__init__.py

@@ -0,0 +1 @@
+"""ROSVOT model submodules."""

preprocess/tools/note_transcription/modules/commons/__init__.py

@@ -0,0 +1 @@
+"""Common ROSVOT layers and utilities."""

preprocess/tools/note_transcription/modules/commons/conformer/__init__.py

@@ -0,0 +1 @@
+"""Conformer layers for ROSVOT."""

preprocess/tools/note_transcription/modules/commons/conformer/conformer.py

@@ -0,0 +1,96 @@
+from torch import nn
+from .espnet_positional_embedding import RelPositionalEncoding, ScaledPositionalEncoding, PositionalEncoding
+from .espnet_transformer_attn import RelPositionMultiHeadedAttention, MultiHeadedAttention
+from .layers import Swish, ConvolutionModule, EncoderLayer, MultiLayeredConv1d
+from ..layers import Embedding
+
+
+class ConformerLayers(nn.Module):
+    def __init__(self, hidden_size, num_layers, kernel_size=9, dropout=0.0, num_heads=4,
+                 use_last_norm=True, save_hidden=False):
+        super().__init__()
+        self.use_last_norm = use_last_norm
+        self.layers = nn.ModuleList()
+        positionwise_layer = MultiLayeredConv1d
+        positionwise_layer_args = (hidden_size, hidden_size * 4, 1, dropout)
+        self.pos_embed = RelPositionalEncoding(hidden_size, dropout)
+        self.encoder_layers = nn.ModuleList([EncoderLayer(
+            hidden_size,
+            RelPositionMultiHeadedAttention(num_heads, hidden_size, 0.0),
+            positionwise_layer(*positionwise_layer_args),
+            positionwise_layer(*positionwise_layer_args),
+            ConvolutionModule(hidden_size, kernel_size, Swish()),
+            dropout,
+        ) for _ in range(num_layers)])
+        if self.use_last_norm:
+            self.layer_norm = nn.LayerNorm(hidden_size)
+        else:
+            self.layer_norm = nn.Linear(hidden_size, hidden_size)
+        self.save_hidden = save_hidden
+        if save_hidden:
+            self.hiddens = []
+
+    def forward(self, x, padding_mask=None):
+        """
+
+        :param x: [B, T, H]
+        :param padding_mask: [B, T]
+        :return: [B, T, H]
+        """
+        self.hiddens = []
+        nonpadding_mask = x.abs().sum(-1) > 0
+        x = self.pos_embed(x)
+        for l in self.encoder_layers:
+            x, mask = l(x, nonpadding_mask[:, None, :])
+            if self.save_hidden:
+                self.hiddens.append(x[0])
+        x = x[0]
+        x = self.layer_norm(x) * nonpadding_mask.float()[:, :, None]
+        return x
+
+class FastConformerLayers(ConformerLayers):
+    def __init__(self, hidden_size, num_layers, kernel_size=9, dropout=0.0, num_heads=4,
+                 use_last_norm=True, save_hidden=False):
+        super(ConformerLayers, self).__init__()
+        self.use_last_norm = use_last_norm
+        self.layers = nn.ModuleList()
+        positionwise_layer = MultiLayeredConv1d
+        positionwise_layer_args = (hidden_size, hidden_size * 4, 1, dropout)
+        self.pos_embed = PositionalEncoding(hidden_size, dropout)
+        self.encoder_layers = nn.ModuleList([EncoderLayer(
+            hidden_size,
+            MultiHeadedAttention(num_heads, hidden_size, 0.0, flash=True),
+            positionwise_layer(*positionwise_layer_args),
+            positionwise_layer(*positionwise_layer_args),
+            ConvolutionModule(hidden_size, kernel_size, Swish()),
+            dropout,
+        ) for _ in range(num_layers)])
+        if self.use_last_norm:
+            self.layer_norm = nn.LayerNorm(hidden_size)
+        else:
+            self.layer_norm = nn.Linear(hidden_size, hidden_size)
+        self.save_hidden = save_hidden
+        if save_hidden:
+            self.hiddens = []
+
+class ConformerEncoder(ConformerLayers):
+    def __init__(self, hidden_size, dict_size, num_layers=None):
+        conformer_enc_kernel_size = 9
+        super().__init__(hidden_size, num_layers, conformer_enc_kernel_size)
+        self.embed = Embedding(dict_size, hidden_size, padding_idx=0)
+
+    def forward(self, x):
+        """
+
+        :param src_tokens: [B, T]
+        :return: [B x T x C]
+        """
+        x = self.embed(x)  # [B, T, H]
+        x = super(ConformerEncoder, self).forward(x)
+        return x
+
+
+class ConformerDecoder(ConformerLayers):
+    def __init__(self, hidden_size, num_layers):
+        conformer_dec_kernel_size = 9
+        super().__init__(hidden_size, num_layers, conformer_dec_kernel_size)

preprocess/tools/note_transcription/modules/commons/conformer/espnet_positional_embedding.py

@@ -0,0 +1,113 @@
+import math
+import torch
+
+
+class PositionalEncoding(torch.nn.Module):
+    """Positional encoding.
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+        reverse (bool): Whether to reverse the input position.
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000, reverse=False):
+        """Construct an PositionalEncoding object."""
+        super(PositionalEncoding, self).__init__()
+        self.d_model = d_model
+        self.reverse = reverse
+        self.xscale = math.sqrt(self.d_model)
+        self.dropout = torch.nn.Dropout(p=dropout_rate)
+        self.pe = None
+        self.extend_pe(torch.tensor(0.0).expand(1, max_len))
+
+    def extend_pe(self, x):
+        """Reset the positional encodings."""
+        if self.pe is not None:
+            if self.pe.size(1) >= x.size(1):
+                if self.pe.dtype != x.dtype or self.pe.device != x.device:
+                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+                return
+        pe = torch.zeros(x.size(1), self.d_model)
+        if self.reverse:
+            position = torch.arange(
+                x.size(1) - 1, -1, -1.0, dtype=torch.float32
+            ).unsqueeze(1)
+        else:
+            position = torch.arange(0, x.size(1), dtype=torch.float32).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, self.d_model, 2, dtype=torch.float32)
+            * -(math.log(10000.0) / self.d_model)
+        )
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.pe = pe.to(device=x.device, dtype=x.dtype)
+
+    def forward(self, x: torch.Tensor):
+        """Add positional encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+        """
+        self.extend_pe(x)
+        x = x * self.xscale + self.pe[:, : x.size(1)]
+        return self.dropout(x)
+
+
+class ScaledPositionalEncoding(PositionalEncoding):
+    """Scaled positional encoding module.
+    See Sec. 3.2  https://arxiv.org/abs/1809.08895
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000):
+        """Initialize class."""
+        super().__init__(d_model=d_model, dropout_rate=dropout_rate, max_len=max_len)
+        self.alpha = torch.nn.Parameter(torch.tensor(1.0))
+
+    def reset_parameters(self):
+        """Reset parameters."""
+        self.alpha.data = torch.tensor(1.0)
+
+    def forward(self, x):
+        """Add positional encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+        """
+        self.extend_pe(x)
+        x = x + self.alpha * self.pe[:, : x.size(1)]
+        return self.dropout(x)
+
+
+class RelPositionalEncoding(PositionalEncoding):
+    """Relative positional encoding module.
+    See : Appendix B in https://arxiv.org/abs/1901.02860
+    Args:
+        d_model (int): Embedding dimension.
+        dropout_rate (float): Dropout rate.
+        max_len (int): Maximum input length.
+    """
+
+    def __init__(self, d_model, dropout_rate, max_len=5000):
+        """Initialize class."""
+        super().__init__(d_model, dropout_rate, max_len, reverse=True)
+
+    def forward(self, x):
+        """Compute positional encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, `*`).
+        Returns:
+            torch.Tensor: Encoded tensor (batch, time, `*`).
+            torch.Tensor: Positional embedding tensor (1, time, `*`).
+        """
+        self.extend_pe(x)
+        x = x * self.xscale
+        pos_emb = self.pe[:, : x.size(1)]
+        return self.dropout(x), self.dropout(pos_emb)

preprocess/tools/note_transcription/modules/commons/conformer/espnet_transformer_attn.py

@@ -0,0 +1,198 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+# Copyright 2019 Shigeki Karita
+#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
+
+"""Multi-Head Attention layer definition."""
+
+from packaging import version
+import math
+
+import numpy
+import torch
+from torch import nn
+
+
+class MultiHeadedAttention(nn.Module):
+    """Multi-Head Attention layer.
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+    """
+
+    def __init__(self, n_head, n_feat, dropout_rate, flash=False):
+        """Construct an MultiHeadedAttention object."""
+        super(MultiHeadedAttention, self).__init__()
+        assert n_feat % n_head == 0
+        # We assume d_v always equals d_k
+        self.d_k = n_feat // n_head
+        self.h = n_head
+        self.linear_q = nn.Linear(n_feat, n_feat)
+        self.linear_k = nn.Linear(n_feat, n_feat)
+        self.linear_v = nn.Linear(n_feat, n_feat)
+        self.linear_out = nn.Linear(n_feat, n_feat)
+        self.attn = None
+        self.dropout = nn.Dropout(p=dropout_rate)
+        self.dropout_rate = dropout_rate
+        self.flash = flash
+
+    def forward_qkv(self, query, key, value):
+        """Transform query, key and value.
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+        Returns:
+            torch.Tensor: Transformed query tensor (#batch, n_head, time1, d_k).
+            torch.Tensor: Transformed key tensor (#batch, n_head, time2, d_k).
+            torch.Tensor: Transformed value tensor (#batch, n_head, time2, d_k).
+        """
+        n_batch = query.size(0)
+        q = self.linear_q(query).view(n_batch, -1, self.h, self.d_k)
+        k = self.linear_k(key).view(n_batch, -1, self.h, self.d_k)
+        v = self.linear_v(value).view(n_batch, -1, self.h, self.d_k)
+        q = q.transpose(1, 2)  # (batch, head, time1, d_k)
+        k = k.transpose(1, 2)  # (batch, head, time2, d_k)
+        v = v.transpose(1, 2)  # (batch, head, time2, d_k)
+
+        return q, k, v
+
+    def forward_attention(self, value, scores, mask):
+        """Compute attention context vector.
+        Args:
+            value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
+            scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
+            mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
+        Returns:
+            torch.Tensor: Transformed value (#batch, time1, d_model)
+                weighted by the attention score (#batch, time1, time2).
+        """
+        n_batch = value.size(0)
+        if mask is not None:
+            mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
+            min_value = float(
+                numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min
+            )
+            scores = scores.masked_fill(mask, min_value)
+            self.attn = torch.softmax(scores, dim=-1).masked_fill(
+                mask, 0.0
+            )  # (batch, head, time1, time2)
+        else:
+            self.attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)
+
+        p_attn = self.dropout(self.attn)
+        x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
+        x = (
+            x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
+        )  # (batch, time1, d_model)
+
+        return self.linear_out(x)  # (batch, time1, d_model)
+
+    def forward(self, query, key, value, mask):
+        """Compute scaled dot product attention.
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2).
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+        """
+        q, k, v = self.forward_qkv(query, key, value)
+        if version.parse(torch.__version__) >= version.parse("2.0") and self.flash:
+            n_batch = value.size(0)
+            x = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v, attn_mask=mask.unsqueeze(1) if mask is not None else None, dropout_p=self.dropout_rate)
+            x = (
+                x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
+            )  # (batch, time1, d_model)
+            return self.linear_out(x)
+        else:
+            scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.d_k)
+            return self.forward_attention(v, scores, mask)
+
+
+class RelPositionMultiHeadedAttention(MultiHeadedAttention):
+    """Multi-Head Attention layer with relative position encoding.
+    Paper: https://arxiv.org/abs/1901.02860
+    Args:
+        n_head (int): The number of heads.
+        n_feat (int): The number of features.
+        dropout_rate (float): Dropout rate.
+    """
+
+    def __init__(self, n_head, n_feat, dropout_rate):
+        """Construct an RelPositionMultiHeadedAttention object."""
+        super().__init__(n_head, n_feat, dropout_rate)
+        # linear transformation for positional ecoding
+        self.linear_pos = nn.Linear(n_feat, n_feat, bias=False)
+        # these two learnable bias are used in matrix c and matrix d
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        self.pos_bias_u = nn.Parameter(torch.Tensor(self.h, self.d_k))
+        self.pos_bias_v = nn.Parameter(torch.Tensor(self.h, self.d_k))
+        torch.nn.init.xavier_uniform_(self.pos_bias_u)
+        torch.nn.init.xavier_uniform_(self.pos_bias_v)
+
+    def rel_shift(self, x, zero_triu=False):
+        """Compute relative positinal encoding.
+        Args:
+            x (torch.Tensor): Input tensor (batch, time, size).
+            zero_triu (bool): If true, return the lower triangular part of the matrix.
+        Returns:
+            torch.Tensor: Output tensor.
+        """
+        zero_pad = torch.zeros((*x.size()[:3], 1), device=x.device, dtype=x.dtype)
+        x_padded = torch.cat([zero_pad, x], dim=-1)
+
+        x_padded = x_padded.view(*x.size()[:2], x.size(3) + 1, x.size(2))
+        x = x_padded[:, :, 1:].view_as(x)
+
+        if zero_triu:
+            ones = torch.ones((x.size(2), x.size(3)))
+            x = x * torch.tril(ones, x.size(3) - x.size(2))[None, None, :, :]
+
+        return x
+
+    def forward(self, query, key, value, pos_emb, mask):
+        """Compute 'Scaled Dot Product Attention' with rel. positional encoding.
+        Args:
+            query (torch.Tensor): Query tensor (#batch, time1, size).
+            key (torch.Tensor): Key tensor (#batch, time2, size).
+            value (torch.Tensor): Value tensor (#batch, time2, size).
+            pos_emb (torch.Tensor): Positional embedding tensor (#batch, time2, size).
+            mask (torch.Tensor): Mask tensor (#batch, 1, time2) or
+                (#batch, time1, time2).
+        Returns:
+            torch.Tensor: Output tensor (#batch, time1, d_model).
+        """
+        q, k, v = self.forward_qkv(query, key, value)
+        q = q.transpose(1, 2)  # (batch, time1, head, d_k)
+
+        n_batch_pos = pos_emb.size(0)
+        p = self.linear_pos(pos_emb).view(n_batch_pos, -1, self.h, self.d_k)
+        p = p.transpose(1, 2)  # (batch, head, time1, d_k)
+
+        # (batch, head, time1, d_k)
+        q_with_bias_u = (q + self.pos_bias_u).transpose(1, 2)
+        # (batch, head, time1, d_k)
+        q_with_bias_v = (q + self.pos_bias_v).transpose(1, 2)
+
+        # compute attention score
+        # first compute matrix a and matrix c
+        # as described in https://arxiv.org/abs/1901.02860 Section 3.3
+        # (batch, head, time1, time2)
+        matrix_ac = torch.matmul(q_with_bias_u, k.transpose(-2, -1))
+
+        # compute matrix b and matrix d
+        # (batch, head, time1, time2)
+        matrix_bd = torch.matmul(q_with_bias_v, p.transpose(-2, -1))
+        matrix_bd = self.rel_shift(matrix_bd)
+
+        scores = (matrix_ac + matrix_bd) / math.sqrt(
+            self.d_k
+        )  # (batch, head, time1, time2)
+
+        return self.forward_attention(v, scores, mask)

preprocess/tools/note_transcription/modules/commons/conformer/layers.py

@@ -0,0 +1,260 @@
+from torch import nn
+import torch
+
+from ..layers import LayerNorm
+
+
+class ConvolutionModule(nn.Module):
+    """ConvolutionModule in Conformer model.
+    Args:
+        channels (int): The number of channels of conv layers.
+        kernel_size (int): Kernerl size of conv layers.
+    """
+
+    def __init__(self, channels, kernel_size, activation=nn.ReLU(), bias=True):
+        """Construct an ConvolutionModule object."""
+        super(ConvolutionModule, self).__init__()
+        # kernerl_size should be a odd number for 'SAME' padding
+        assert (kernel_size - 1) % 2 == 0
+
+        self.pointwise_conv1 = nn.Conv1d(
+            channels,
+            2 * channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=bias,
+        )
+        self.depthwise_conv = nn.Conv1d(
+            channels,
+            channels,
+            kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+            groups=channels,
+            bias=bias,
+        )
+        self.norm = nn.BatchNorm1d(channels)
+        self.pointwise_conv2 = nn.Conv1d(
+            channels,
+            channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=bias,
+        )
+        self.activation = activation
+
+    def forward(self, x):
+        """Compute convolution module.
+        Args:
+            x (torch.Tensor): Input tensor (#batch, time, channels).
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, channels).
+        """
+        # exchange the temporal dimension and the feature dimension
+        x = x.transpose(1, 2)
+
+        # GLU mechanism
+        x = self.pointwise_conv1(x)  # (batch, 2*channel, dim)
+        x = nn.functional.glu(x, dim=1)  # (batch, channel, dim)
+
+        # 1D Depthwise Conv
+        x = self.depthwise_conv(x)
+        x = self.activation(self.norm(x))
+
+        x = self.pointwise_conv2(x)
+
+        return x.transpose(1, 2)
+
+
+class MultiLayeredConv1d(torch.nn.Module):
+    """Multi-layered conv1d for Transformer block.
+    This is a module of multi-leyered conv1d designed
+    to replace positionwise feed-forward network
+    in Transforner block, which is introduced in
+    `FastSpeech: Fast, Robust and Controllable Text to Speech`_.
+    .. _`FastSpeech: Fast, Robust and Controllable Text to Speech`:
+        https://arxiv.org/pdf/1905.09263.pdf
+    """
+
+    def __init__(self, in_chans, hidden_chans, kernel_size, dropout_rate):
+        """Initialize MultiLayeredConv1d module.
+        Args:
+            in_chans (int): Number of input channels.
+            hidden_chans (int): Number of hidden channels.
+            kernel_size (int): Kernel size of conv1d.
+            dropout_rate (float): Dropout rate.
+        """
+        super(MultiLayeredConv1d, self).__init__()
+        self.w_1 = torch.nn.Conv1d(
+            in_chans,
+            hidden_chans,
+            kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+        )
+        self.w_2 = torch.nn.Conv1d(
+            hidden_chans,
+            in_chans,
+            kernel_size,
+            stride=1,
+            padding=(kernel_size - 1) // 2,
+        )
+        self.dropout = torch.nn.Dropout(dropout_rate)
+
+    def forward(self, x):
+        """Calculate forward propagation.
+        Args:
+            x (torch.Tensor): Batch of input tensors (B, T, in_chans).
+        Returns:
+            torch.Tensor: Batch of output tensors (B, T, hidden_chans).
+        """
+        x = torch.relu(self.w_1(x.transpose(-1, 1))).transpose(-1, 1)
+        return self.w_2(self.dropout(x).transpose(-1, 1)).transpose(-1, 1)
+
+
+class Swish(torch.nn.Module):
+    """Construct an Swish object."""
+
+    def forward(self, x):
+        """Return Swich activation function."""
+        return x * torch.sigmoid(x)
+
+
+class EncoderLayer(nn.Module):
+    """Encoder layer module.
+    Args:
+        size (int): Input dimension.
+        self_attn (torch.nn.Module): Self-attention module instance.
+            `MultiHeadedAttention` or `RelPositionMultiHeadedAttention` instance
+            can be used as the argument.
+        feed_forward (torch.nn.Module): Feed-forward module instance.
+            `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
+            can be used as the argument.
+        feed_forward_macaron (torch.nn.Module): Additional feed-forward module instance.
+            `PositionwiseFeedForward`, `MultiLayeredConv1d`, or `Conv1dLinear` instance
+            can be used as the argument.
+        conv_module (torch.nn.Module): Convolution module instance.
+            `ConvlutionModule` instance can be used as the argument.
+        dropout_rate (float): Dropout rate.
+        normalize_before (bool): Whether to use layer_norm before the first block.
+        concat_after (bool): Whether to concat attention layer's input and output.
+            if True, additional linear will be applied.
+            i.e. x -> x + linear(concat(x, att(x)))
+            if False, no additional linear will be applied. i.e. x -> x + att(x)
+    """
+
+    def __init__(
+            self,
+            size,
+            self_attn,
+            feed_forward,
+            feed_forward_macaron,
+            conv_module,
+            dropout_rate,
+            normalize_before=True,
+            concat_after=False,
+    ):
+        """Construct an EncoderLayer object."""
+        super(EncoderLayer, self).__init__()
+        self.self_attn = self_attn
+        self.feed_forward = feed_forward
+        self.feed_forward_macaron = feed_forward_macaron
+        self.conv_module = conv_module
+        self.norm_ff = LayerNorm(size)  # for the FNN module
+        self.norm_mha = LayerNorm(size)  # for the MHA module
+        if feed_forward_macaron is not None:
+            self.norm_ff_macaron = LayerNorm(size)
+            self.ff_scale = 0.5
+        else:
+            self.ff_scale = 1.0
+        if self.conv_module is not None:
+            self.norm_conv = LayerNorm(size)  # for the CNN module
+            self.norm_final = LayerNorm(size)  # for the final output of the block
+        self.dropout = nn.Dropout(dropout_rate)
+        self.size = size
+        self.normalize_before = normalize_before
+        self.concat_after = concat_after
+        if self.concat_after:
+            self.concat_linear = nn.Linear(size + size, size)
+
+    def forward(self, x_input, mask, cache=None):
+        """Compute encoded features.
+        Args:
+            x_input (Union[Tuple, torch.Tensor]): Input tensor w/ or w/o pos emb.
+                - w/ pos emb: Tuple of tensors [(#batch, time, size), (1, time, size)].
+                - w/o pos emb: Tensor (#batch, time, size).
+            mask (torch.Tensor): Mask tensor for the input (#batch, time).
+            cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size).
+        Returns:
+            torch.Tensor: Output tensor (#batch, time, size).
+            torch.Tensor: Mask tensor (#batch, time).
+        """
+        if isinstance(x_input, tuple):
+            x, pos_emb = x_input[0], x_input[1]
+        else:
+            x, pos_emb = x_input, None
+
+        # whether to use macaron style
+        if self.feed_forward_macaron is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_ff_macaron(x)
+            x = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(x))
+            if not self.normalize_before:
+                x = self.norm_ff_macaron(x)
+
+        # multi-headed self-attention module
+        residual = x
+        if self.normalize_before:
+            x = self.norm_mha(x)
+
+        if cache is None:
+            x_q = x
+        else:
+            assert cache.shape == (x.shape[0], x.shape[1] - 1, self.size)
+            x_q = x[:, -1:, :]
+            residual = residual[:, -1:, :]
+            mask = None if mask is None else mask[:, -1:, :]
+
+        if pos_emb is not None:
+            x_att = self.self_attn(x_q, x, x, pos_emb, mask)
+        else:
+            x_att = self.self_attn(x_q, x, x, mask)
+
+        if self.concat_after:
+            x_concat = torch.cat((x, x_att), dim=-1)
+            x = residual + self.concat_linear(x_concat)
+        else:
+            x = residual + self.dropout(x_att)
+        if not self.normalize_before:
+            x = self.norm_mha(x)
+
+        # convolution module
+        if self.conv_module is not None:
+            residual = x
+            if self.normalize_before:
+                x = self.norm_conv(x)
+            x = residual + self.dropout(self.conv_module(x))
+            if not self.normalize_before:
+                x = self.norm_conv(x)
+
+        # feed forward module
+        residual = x
+        if self.normalize_before:
+            x = self.norm_ff(x)
+        x = residual + self.ff_scale * self.dropout(self.feed_forward(x))
+        if not self.normalize_before:
+            x = self.norm_ff(x)
+
+        if self.conv_module is not None:
+            x = self.norm_final(x)
+
+        if cache is not None:
+            x = torch.cat([cache, x], dim=1)
+
+        if pos_emb is not None:
+            return (x, pos_emb), mask
+
+        return x, mask

preprocess/tools/note_transcription/modules/commons/conv.py

@@ -0,0 +1,175 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .layers import LayerNorm, Embedding
+
+class LambdaLayer(nn.Module):
+    def __init__(self, lambd):
+        super(LambdaLayer, self).__init__()
+        self.lambd = lambd
+
+    def forward(self, x):
+        return self.lambd(x)
+
+def init_weights_func(m):
+    classname = m.__class__.__name__
+    if classname.find("Conv1d") != -1:
+        torch.nn.init.xavier_uniform_(m.weight)
+
+def get_norm_builder(norm_type, channels, ln_eps=1e-6):
+    if norm_type == 'bn':
+        norm_builder = lambda: nn.BatchNorm1d(channels)
+    elif norm_type == 'in':
+        norm_builder = lambda: nn.InstanceNorm1d(channels, affine=True)
+    elif norm_type == 'gn':
+        norm_builder = lambda: nn.GroupNorm(8, channels)
+    elif norm_type == 'ln':
+        norm_builder = lambda: LayerNorm(channels, dim=1, eps=ln_eps)
+    else:
+        norm_builder = lambda: nn.Identity()
+    return norm_builder
+
+def get_act_builder(act_type):
+    if act_type == 'gelu':
+        act_builder = lambda: nn.GELU()
+    elif act_type == 'relu':
+        act_builder = lambda: nn.ReLU(inplace=True)
+    elif act_type == 'leakyrelu':
+        act_builder = lambda: nn.LeakyReLU(negative_slope=0.01, inplace=True)
+    elif act_type == 'swish':
+        act_builder = lambda: nn.SiLU(inplace=True)
+    else:
+        act_builder = lambda: nn.Identity()
+    return act_builder
+
+class ResidualBlock(nn.Module):
+    """Implements conv->PReLU->norm n-times"""
+
+    def __init__(self, channels, kernel_size, dilation, n=2, norm_type='bn', dropout=0.0,
+                 c_multiple=2, ln_eps=1e-12, act_type='gelu'):
+        super(ResidualBlock, self).__init__()
+
+        norm_builder = get_norm_builder(norm_type, channels, ln_eps)
+        act_builder = get_act_builder(act_type)
+
+        self.blocks = [
+            nn.Sequential(
+                norm_builder(),
+                nn.Conv1d(channels, c_multiple * channels, kernel_size, dilation=dilation,
+                          padding=(dilation * (kernel_size - 1)) // 2),
+                LambdaLayer(lambda x: x * kernel_size ** -0.5),
+                act_builder(),
+                nn.Conv1d(c_multiple * channels, channels, 1, dilation=dilation),
+            )
+            for i in range(n)
+        ]
+
+        self.blocks = nn.ModuleList(self.blocks)
+        self.dropout = dropout
+
+    def forward(self, x):
+        nonpadding = (x.abs().sum(1) > 0).float()[:, None, :]
+        for b in self.blocks:
+            x_ = b(x)
+            if self.dropout > 0 and self.training:
+                x_ = F.dropout(x_, self.dropout, training=self.training)
+            x = x + x_
+            x = x * nonpadding
+        return x
+
+
+class ConvBlocks(nn.Module):
+    """Decodes the expanded phoneme encoding into spectrograms"""
+
+    def __init__(self, hidden_size, out_dims, dilations, kernel_size,
+                 norm_type='ln', layers_in_block=2, c_multiple=2,
+                 dropout=0.0, ln_eps=1e-5,
+                 init_weights=True, is_BTC=True, num_layers=None, post_net_kernel=3, act_type='gelu'):
+        super(ConvBlocks, self).__init__()
+        self.is_BTC = is_BTC
+        if num_layers is not None:
+            dilations = [1] * num_layers
+        self.res_blocks = nn.Sequential(
+            *[ResidualBlock(hidden_size, kernel_size, d,
+                            n=layers_in_block, norm_type=norm_type, c_multiple=c_multiple,
+                            dropout=dropout, ln_eps=ln_eps, act_type=act_type)
+              for d in dilations],
+        )
+        norm = get_norm_builder(norm_type, hidden_size, ln_eps)()
+        self.last_norm = norm
+        self.post_net1 = nn.Conv1d(hidden_size, out_dims, kernel_size=post_net_kernel,
+                                   padding=post_net_kernel // 2)
+        if init_weights:
+            self.apply(init_weights_func)
+
+    def forward(self, x, nonpadding=None):
+        """
+
+        :param x: [B, T, H]
+        :return:  [B, T, H]
+        """
+        if self.is_BTC:
+            x = x.transpose(1, 2)
+        if nonpadding is None:
+            nonpadding = (x.abs().sum(1) > 0).float()[:, None, :]
+        elif self.is_BTC:
+            nonpadding = nonpadding.transpose(1, 2)
+        x = self.res_blocks(x) * nonpadding
+        x = self.last_norm(x) * nonpadding
+        x = self.post_net1(x) * nonpadding
+        if self.is_BTC:
+            x = x.transpose(1, 2)
+        return x
+
+
+class TextConvEncoder(ConvBlocks):
+    def __init__(self, dict_size, hidden_size, out_dims, dilations, kernel_size,
+                 norm_type='ln', layers_in_block=2, c_multiple=2,
+                 dropout=0.0, ln_eps=1e-5, init_weights=True, num_layers=None, post_net_kernel=3):
+        super().__init__(hidden_size, out_dims, dilations, kernel_size,
+                         norm_type, layers_in_block, c_multiple,
+                         dropout, ln_eps, init_weights, num_layers=num_layers,
+                         post_net_kernel=post_net_kernel)
+        self.embed_tokens = Embedding(dict_size, hidden_size, 0)
+        self.embed_scale = math.sqrt(hidden_size)
+
+    def forward(self, txt_tokens):
+        """
+
+        :param txt_tokens: [B, T]
+        :return: {
+            'encoder_out': [B x T x C]
+        }
+        """
+        x = self.embed_scale * self.embed_tokens(txt_tokens)
+        return super().forward(x)
+
+
+class ConditionalConvBlocks(ConvBlocks):
+    def __init__(self, hidden_size, c_cond, c_out, dilations, kernel_size,
+                 norm_type='ln', layers_in_block=2, c_multiple=2,
+                 dropout=0.0, ln_eps=1e-5, init_weights=True, is_BTC=True, num_layers=None):
+        super().__init__(hidden_size, c_out, dilations, kernel_size,
+                         norm_type, layers_in_block, c_multiple,
+                         dropout, ln_eps, init_weights, is_BTC=False, num_layers=num_layers)
+        self.g_prenet = nn.Conv1d(c_cond, hidden_size, 3, padding=1)
+        self.is_BTC_ = is_BTC
+        if init_weights:
+            self.g_prenet.apply(init_weights_func)
+
+    def forward(self, x, cond, nonpadding=None):
+        if self.is_BTC_:
+            x = x.transpose(1, 2)
+            cond = cond.transpose(1, 2)
+            if nonpadding is not None:
+                nonpadding = nonpadding.transpose(1, 2)
+        if nonpadding is None:
+            nonpadding = x.abs().sum(1)[:, None]
+        x = x + self.g_prenet(cond)
+        x = x * nonpadding
+        x = super(ConditionalConvBlocks, self).forward(x)  # input needs to be BTC
+        if self.is_BTC_:
+            x = x.transpose(1, 2)
+        return x

preprocess/tools/note_transcription/modules/commons/layers.py

@@ -0,0 +1,85 @@
+import torch
+from torch import nn
+from torch.autograd import Function
+
+class LayerNorm(torch.nn.LayerNorm):
+    """Layer normalization module.
+    :param int nout: output dim size
+    :param int dim: dimension to be normalized
+    """
+
+    def __init__(self, nout, dim=-1, eps=1e-5):
+        """Construct an LayerNorm object."""
+        super(LayerNorm, self).__init__(nout, eps=eps)
+        self.dim = dim
+
+    def forward(self, x):
+        """Apply layer normalization.
+        :param torch.Tensor x: input tensor
+        :return: layer normalized tensor
+        :rtype torch.Tensor
+        """
+        if self.dim == -1:
+            return super(LayerNorm, self).forward(x)
+        return super(LayerNorm, self).forward(x.transpose(1, -1)).transpose(1, -1)
+
+
+class Reshape(nn.Module):
+    def __init__(self, *args):
+        super(Reshape, self).__init__()
+        self.shape = args
+
+    def forward(self, x):
+        return x.view(self.shape)
+
+
+class Permute(nn.Module):
+    def __init__(self, *args):
+        super(Permute, self).__init__()
+        self.args = args
+
+    def forward(self, x):
+        return x.permute(self.args)
+
+
+def Linear(in_features, out_features, bias=True, init_type='xavier'):
+    m = nn.Linear(in_features, out_features, bias)
+    if init_type == 'xavier':
+        nn.init.xavier_uniform_(m.weight)
+    elif init_type == 'kaiming':
+        nn.init.kaiming_normal_(m.weight, mode='fan_in')
+    if bias:
+        nn.init.constant_(m.bias, 0.)
+    return m
+
+
+def Embedding(num_embeddings, embedding_dim, padding_idx=None, init_type='normal'):
+    m = nn.Embedding(num_embeddings, embedding_dim, padding_idx=padding_idx)
+    if init_type == 'normal':
+        nn.init.normal_(m.weight, mean=0, std=embedding_dim ** -0.5)
+    elif init_type == 'kaiming':
+        nn.init.kaiming_normal_(m.weight, mode='fan_in')
+    if padding_idx is not None:
+        nn.init.constant_(m.weight[padding_idx], 0)
+    return m
+
+
+class GradientReverseFunction(Function):
+    @staticmethod
+    def forward(ctx, input, coeff=1.):
+        ctx.coeff = coeff
+        output = input * 1.0
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        return grad_output.neg() * ctx.coeff, None
+
+
+class GRL(nn.Module):
+    def __init__(self):
+        super(GRL, self).__init__()
+
+    def forward(self, *input):
+        return GradientReverseFunction.apply(*input)
+

preprocess/tools/note_transcription/modules/commons/rel_transformer.py

@@ -0,0 +1,378 @@
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from .layers import Embedding
+
+
+def convert_pad_shape(pad_shape):
+    l = pad_shape[::-1]
+    pad_shape = [item for sublist in l for item in sublist]
+    return pad_shape
+
+
+def shift_1d(x):
+    x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
+    return x
+
+
+def sequence_mask(length, max_length=None):
+    if max_length is None:
+        max_length = length.max()
+    x = torch.arange(max_length, dtype=length.dtype, device=length.device)
+    return x.unsqueeze(0) < length.unsqueeze(1)
+
+
+class Encoder(nn.Module):
+    def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0.,
+                 window_size=None, block_length=None, pre_ln=False, **kwargs):
+        super().__init__()
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.block_length = block_length
+        self.pre_ln = pre_ln
+
+        self.drop = nn.Dropout(p_dropout)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+        for i in range(self.n_layers):
+            self.attn_layers.append(
+                MultiHeadAttention(hidden_channels, hidden_channels, n_heads, window_size=window_size,
+                                   p_dropout=p_dropout, block_length=block_length))
+            self.norm_layers_1.append(LayerNorm(hidden_channels))
+            self.ffn_layers.append(
+                FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
+            self.norm_layers_2.append(LayerNorm(hidden_channels))
+        if pre_ln:
+            self.last_ln = LayerNorm(hidden_channels)
+
+    def forward(self, x, x_mask):
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        for i in range(self.n_layers):
+            x = x * x_mask
+            x_ = x
+            if self.pre_ln:
+                x = self.norm_layers_1[i](x)
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.drop(y)
+            x = x_ + y
+            if not self.pre_ln:
+                x = self.norm_layers_1[i](x)
+
+            x_ = x
+            if self.pre_ln:
+                x = self.norm_layers_2[i](x)
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.drop(y)
+            x = x_ + y
+            if not self.pre_ln:
+                x = self.norm_layers_2[i](x)
+        if self.pre_ln:
+            x = self.last_ln(x)
+        x = x * x_mask
+        return x
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0.,
+                 block_length=None, proximal_bias=False, proximal_init=False):
+        super().__init__()
+        assert channels % n_heads == 0
+
+        self.channels = channels
+        self.out_channels = out_channels
+        self.n_heads = n_heads
+        self.window_size = window_size
+        self.heads_share = heads_share
+        self.block_length = block_length
+        self.proximal_bias = proximal_bias
+        self.p_dropout = p_dropout
+        self.attn = None
+
+        self.k_channels = channels // n_heads
+        self.conv_q = nn.Conv1d(channels, channels, 1)
+        self.conv_k = nn.Conv1d(channels, channels, 1)
+        self.conv_v = nn.Conv1d(channels, channels, 1)
+        if window_size is not None:
+            n_heads_rel = 1 if heads_share else n_heads
+            rel_stddev = self.k_channels ** -0.5
+            self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+            self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
+        self.conv_o = nn.Conv1d(channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+        nn.init.xavier_uniform_(self.conv_q.weight)
+        nn.init.xavier_uniform_(self.conv_k.weight)
+        if proximal_init:
+            self.conv_k.weight.data.copy_(self.conv_q.weight.data)
+            self.conv_k.bias.data.copy_(self.conv_q.bias.data)
+        nn.init.xavier_uniform_(self.conv_v.weight)
+
+    def forward(self, x, c, attn_mask=None):
+        q = self.conv_q(x)
+        k = self.conv_k(c)
+        v = self.conv_v(c)
+
+        x, self.attn = self.attention(q, k, v, mask=attn_mask)
+
+        x = self.conv_o(x)
+        return x
+
+    def attention(self, query, key, value, mask=None):
+        # reshape [b, d, t] -> [b, n_h, t, d_k]
+        b, d, t_s, t_t = (*key.size(), query.size(2))
+        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
+        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
+
+        scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
+        if self.window_size is not None:
+            assert t_s == t_t, "Relative attention is only available for self-attention."
+            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
+            rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings)
+            rel_logits = self._relative_position_to_absolute_position(rel_logits)
+            scores_local = rel_logits / math.sqrt(self.k_channels)
+            scores = scores + scores_local
+        if self.proximal_bias:
+            assert t_s == t_t, "Proximal bias is only available for self-attention."
+            scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
+        if mask is not None:
+            scores = scores.masked_fill(mask == 0, -1e4)
+            if self.block_length is not None:
+                block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
+                scores = scores * block_mask + -1e4 * (1 - block_mask)
+        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
+        p_attn = self.drop(p_attn)
+        output = torch.matmul(p_attn, value)
+        if self.window_size is not None:
+            relative_weights = self._absolute_position_to_relative_position(p_attn)
+            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
+            output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
+        output = output.transpose(2, 3).contiguous().view(b, d, t_t)  # [b, n_h, t_t, d_k] -> [b, d, t_t]
+        return output, p_attn
+
+    def _matmul_with_relative_values(self, x, y):
+        """
+        x: [b, h, l, m]
+        y: [h or 1, m, d]
+        ret: [b, h, l, d]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0))
+        return ret
+
+    def _matmul_with_relative_keys(self, x, y):
+        """
+        x: [b, h, l, d]
+        y: [h or 1, m, d]
+        ret: [b, h, l, m]
+        """
+        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
+        return ret
+
+    def _get_relative_embeddings(self, relative_embeddings, length):
+        max_relative_position = 2 * self.window_size + 1
+        # Pad first before slice to avoid using cond ops.
+        pad_length = max(length - (self.window_size + 1), 0)
+        slice_start_position = max((self.window_size + 1) - length, 0)
+        slice_end_position = slice_start_position + 2 * length - 1
+        if pad_length > 0:
+            padded_relative_embeddings = F.pad(
+                relative_embeddings,
+                convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
+        else:
+            padded_relative_embeddings = relative_embeddings
+        used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position]
+        return used_relative_embeddings
+
+    def _relative_position_to_absolute_position(self, x):
+        """
+        x: [b, h, l, 2*l-1]
+        ret: [b, h, l, l]
+        """
+        batch, heads, length, _ = x.size()
+        # Concat columns of pad to shift from relative to absolute indexing.
+        x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
+
+        # Concat extra elements so to add up to shape (len+1, 2*len-1).
+        x_flat = x.view([batch, heads, length * 2 * length])
+        x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))
+
+        # Reshape and slice out the padded elements.
+        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1:]
+        return x_final
+
+    def _absolute_position_to_relative_position(self, x):
+        """
+        x: [b, h, l, l]
+        ret: [b, h, l, 2*l-1]
+        """
+        batch, heads, length, _ = x.size()
+        # padd along column
+        x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
+        x_flat = x.view([batch, heads, length ** 2 + length * (length - 1)])
+        # add 0's in the beginning that will skew the elements after reshape
+        x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
+        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
+        return x_final
+
+    def _attention_bias_proximal(self, length):
+        """Bias for self-attention to encourage attention to close positions.
+        Args:
+          length: an integer scalar.
+        Returns:
+          a Tensor with shape [1, 1, length, length]
+        """
+        r = torch.arange(length, dtype=torch.float32)
+        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
+        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
+
+
+class FFN(nn.Module):
+    def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.activation = activation
+
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2)
+        self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1)
+        self.drop = nn.Dropout(p_dropout)
+
+    def forward(self, x, x_mask):
+        x = self.conv_1(x * x_mask)
+        if self.activation == "gelu":
+            x = x * torch.sigmoid(1.702 * x)
+        else:
+            x = torch.relu(x)
+        x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        return x * x_mask
+
+
+class LayerNorm(nn.Module):
+    def __init__(self, channels, eps=1e-4):
+        super().__init__()
+        self.channels = channels
+        self.eps = eps
+
+        self.gamma = nn.Parameter(torch.ones(channels))
+        self.beta = nn.Parameter(torch.zeros(channels))
+
+    def forward(self, x):
+        n_dims = len(x.shape)
+        mean = torch.mean(x, 1, keepdim=True)
+        variance = torch.mean((x - mean) ** 2, 1, keepdim=True)
+
+        x = (x - mean) * torch.rsqrt(variance + self.eps)
+
+        shape = [1, -1] + [1] * (n_dims - 2)
+        x = x * self.gamma.view(*shape) + self.beta.view(*shape)
+        return x
+
+
+class ConvReluNorm(nn.Module):
+    def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
+        super().__init__()
+        self.in_channels = in_channels
+        self.hidden_channels = hidden_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.n_layers = n_layers
+        self.p_dropout = p_dropout
+        assert n_layers > 1, "Number of layers should be larger than 0."
+
+        self.conv_layers = nn.ModuleList()
+        self.norm_layers = nn.ModuleList()
+        self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
+        self.norm_layers.append(LayerNorm(hidden_channels))
+        self.relu_drop = nn.Sequential(
+            nn.ReLU(),
+            nn.Dropout(p_dropout))
+        for _ in range(n_layers - 1):
+            self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2))
+            self.norm_layers.append(LayerNorm(hidden_channels))
+        self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+        self.proj.weight.data.zero_()
+        self.proj.bias.data.zero_()
+
+    def forward(self, x, x_mask):
+        x_org = x
+        for i in range(self.n_layers):
+            x = self.conv_layers[i](x * x_mask)
+            x = self.norm_layers[i](x)
+            x = self.relu_drop(x)
+        x = x_org + self.proj(x)
+        return x * x_mask
+
+
+class RelTransformerEncoder(nn.Module):
+    def __init__(self,
+                 n_vocab,
+                 out_channels,
+                 hidden_channels,
+                 filter_channels,
+                 n_heads,
+                 n_layers,
+                 kernel_size,
+                 p_dropout=0.0,
+                 window_size=4,
+                 block_length=None,
+                 prenet=True,
+                 pre_ln=True,
+                 ):
+
+        super().__init__()
+
+        self.n_vocab = n_vocab
+        self.out_channels = out_channels
+        self.hidden_channels = hidden_channels
+        self.filter_channels = filter_channels
+        self.n_heads = n_heads
+        self.n_layers = n_layers
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.window_size = window_size
+        self.block_length = block_length
+        self.prenet = prenet
+        if n_vocab > 0:
+            self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0)
+
+        if prenet:
+            self.pre = ConvReluNorm(hidden_channels, hidden_channels, hidden_channels,
+                                    kernel_size=5, n_layers=3, p_dropout=0)
+        self.encoder = Encoder(
+            hidden_channels,
+            filter_channels,
+            n_heads,
+            n_layers,
+            kernel_size,
+            p_dropout,
+            window_size=window_size,
+            block_length=block_length,
+            pre_ln=pre_ln,
+        )
+
+    def forward(self, x, x_mask=None):
+        if self.n_vocab > 0:
+            x_lengths = (x > 0).long().sum(-1)
+            x = self.emb(x) * math.sqrt(self.hidden_channels)  # [b, t, h]
+        else:
+            x_lengths = (x.abs().sum(-1) > 0).long().sum(-1)
+        x = torch.transpose(x, 1, -1)  # [b, h, t]
+        x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
+
+        if self.prenet:
+            x = self.pre(x, x_mask)
+        x = self.encoder(x, x_mask)
+        return x.transpose(1, 2)

preprocess/tools/note_transcription/modules/commons/rnn.py

@@ -0,0 +1,261 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+class PreNet(nn.Module):
+    def __init__(self, in_dims, fc1_dims=256, fc2_dims=128, dropout=0.5):
+        super().__init__()
+        self.fc1 = nn.Linear(in_dims, fc1_dims)
+        self.fc2 = nn.Linear(fc1_dims, fc2_dims)
+        self.p = dropout
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = F.relu(x)
+        x = F.dropout(x, self.p, training=self.training)
+        x = self.fc2(x)
+        x = F.relu(x)
+        x = F.dropout(x, self.p, training=self.training)
+        return x
+
+
+class HighwayNetwork(nn.Module):
+    def __init__(self, size):
+        super().__init__()
+        self.W1 = nn.Linear(size, size)
+        self.W2 = nn.Linear(size, size)
+        self.W1.bias.data.fill_(0.)
+
+    def forward(self, x):
+        x1 = self.W1(x)
+        x2 = self.W2(x)
+        g = torch.sigmoid(x2)
+        y = g * F.relu(x1) + (1. - g) * x
+        return y
+
+
+class BatchNormConv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel, relu=True):
+        super().__init__()
+        self.conv = nn.Conv1d(in_channels, out_channels, kernel, stride=1, padding=kernel // 2, bias=False)
+        self.bnorm = nn.BatchNorm1d(out_channels)
+        self.relu = relu
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = F.relu(x) if self.relu is True else x
+        return self.bnorm(x)
+
+
+class ConvNorm(torch.nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
+                 padding=None, dilation=1, bias=True, w_init_gain='linear'):
+        super(ConvNorm, self).__init__()
+        if padding is None:
+            assert (kernel_size % 2 == 1)
+            padding = int(dilation * (kernel_size - 1) / 2)
+
+        self.conv = torch.nn.Conv1d(in_channels, out_channels,
+                                    kernel_size=kernel_size, stride=stride,
+                                    padding=padding, dilation=dilation,
+                                    bias=bias)
+
+        torch.nn.init.xavier_uniform_(
+            self.conv.weight, gain=torch.nn.init.calculate_gain(w_init_gain))
+
+    def forward(self, signal):
+        conv_signal = self.conv(signal)
+        return conv_signal
+
+
+class CBHG(nn.Module):
+    def __init__(self, K, in_channels, channels, proj_channels, num_highways):
+        super().__init__()
+
+        # List of all rnns to call `flatten_parameters()` on
+        self._to_flatten = []
+
+        self.bank_kernels = [i for i in range(1, K + 1)]
+        self.conv1d_bank = nn.ModuleList()
+        for k in self.bank_kernels:
+            conv = BatchNormConv(in_channels, channels, k)
+            self.conv1d_bank.append(conv)
+
+        self.maxpool = nn.MaxPool1d(kernel_size=2, stride=1, padding=1)
+
+        self.conv_project1 = BatchNormConv(len(self.bank_kernels) * channels, proj_channels[0], 3)
+        self.conv_project2 = BatchNormConv(proj_channels[0], proj_channels[1], 3, relu=False)
+
+        # Fix the highway input if necessary
+        if proj_channels[-1] != channels:
+            self.highway_mismatch = True
+            self.pre_highway = nn.Linear(proj_channels[-1], channels, bias=False)
+        else:
+            self.highway_mismatch = False
+
+        self.highways = nn.ModuleList()
+        for i in range(num_highways):
+            hn = HighwayNetwork(channels)
+            self.highways.append(hn)
+
+        self.rnn = nn.GRU(channels, channels, batch_first=True, bidirectional=True)
+        self._to_flatten.append(self.rnn)
+
+        # Avoid fragmentation of RNN parameters and associated warning
+        self._flatten_parameters()
+
+    def forward(self, x):
+        # Although we `_flatten_parameters()` on init, when using DataParallel
+        # the model gets replicated, making it no longer guaranteed that the
+        # weights are contiguous in GPU memory. Hence, we must call it again
+        self._flatten_parameters()
+
+        # Save these for later
+        residual = x
+        seq_len = x.size(-1)
+        conv_bank = []
+
+        # Convolution Bank
+        for conv in self.conv1d_bank:
+            c = conv(x)  # Convolution
+            conv_bank.append(c[:, :, :seq_len])
+
+        # Stack along the channel axis
+        conv_bank = torch.cat(conv_bank, dim=1)
+
+        # dump the last padding to fit residual
+        x = self.maxpool(conv_bank)[:, :, :seq_len]
+
+        # Conv1d projections
+        x = self.conv_project1(x)
+        x = self.conv_project2(x)
+
+        # Residual Connect
+        x = x + residual
+
+        # Through the highways
+        x = x.transpose(1, 2)
+        if self.highway_mismatch is True:
+            x = self.pre_highway(x)
+        for h in self.highways:
+            x = h(x)
+
+        # And then the RNN
+        x, _ = self.rnn(x)
+        return x
+
+    def _flatten_parameters(self):
+        """Calls `flatten_parameters` on all the rnns used by the WaveRNN. Used
+        to improve efficiency and avoid PyTorch yelling at us."""
+        [m.flatten_parameters() for m in self._to_flatten]
+
+
+class TacotronEncoder(nn.Module):
+    def __init__(self, embed_dims, num_chars, cbhg_channels, K, num_highways, dropout):
+        super().__init__()
+        self.embedding = nn.Embedding(num_chars, embed_dims)
+        self.pre_net = PreNet(embed_dims, embed_dims, embed_dims, dropout=dropout)
+        self.cbhg = CBHG(K=K, in_channels=cbhg_channels, channels=cbhg_channels,
+                         proj_channels=[cbhg_channels, cbhg_channels],
+                         num_highways=num_highways)
+        self.proj_out = nn.Linear(cbhg_channels * 2, cbhg_channels)
+
+    def forward(self, x):
+        x = self.embedding(x)
+        x = self.pre_net(x)
+        x.transpose_(1, 2)
+        x = self.cbhg(x)
+        x = self.proj_out(x)
+        return x
+
+
+class RNNEncoder(nn.Module):
+    def __init__(self, num_chars, embedding_dim, n_convolutions=3, kernel_size=5):
+        super(RNNEncoder, self).__init__()
+        self.embedding = nn.Embedding(num_chars, embedding_dim, padding_idx=0)
+        convolutions = []
+        for _ in range(n_convolutions):
+            conv_layer = nn.Sequential(
+                ConvNorm(embedding_dim,
+                         embedding_dim,
+                         kernel_size=kernel_size, stride=1,
+                         padding=int((kernel_size - 1) / 2),
+                         dilation=1, w_init_gain='relu'),
+                nn.BatchNorm1d(embedding_dim))
+            convolutions.append(conv_layer)
+        self.convolutions = nn.ModuleList(convolutions)
+
+        self.lstm = nn.LSTM(embedding_dim, int(embedding_dim / 2), 1,
+                            batch_first=True, bidirectional=True)
+
+    def forward(self, x):
+        input_lengths = (x > 0).sum(-1)
+        input_lengths = input_lengths.cpu().numpy()
+
+        x = self.embedding(x)
+        x = x.transpose(1, 2)  # [B, H, T]
+        for conv in self.convolutions:
+            x = F.dropout(F.relu(conv(x)), 0.5, self.training) + x
+        x = x.transpose(1, 2)  # [B, T, H]
+
+        # pytorch tensor are not reversible, hence the conversion
+        x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True, enforce_sorted=False)
+
+        self.lstm.flatten_parameters()
+        outputs, _ = self.lstm(x)
+        outputs, _ = nn.utils.rnn.pad_packed_sequence(outputs, batch_first=True)
+
+        return outputs
+
+
+class DecoderRNN(torch.nn.Module):
+    def __init__(self, hidden_size, decoder_rnn_dim, dropout):
+        super(DecoderRNN, self).__init__()
+        self.in_conv1d = nn.Sequential(
+            torch.nn.Conv1d(
+                in_channels=hidden_size,
+                out_channels=hidden_size,
+                kernel_size=9, padding=4,
+            ),
+            torch.nn.ReLU(),
+            torch.nn.Conv1d(
+                in_channels=hidden_size,
+                out_channels=hidden_size,
+                kernel_size=9, padding=4,
+            ),
+        )
+        self.ln = nn.LayerNorm(hidden_size)
+        if decoder_rnn_dim == 0:
+            decoder_rnn_dim = hidden_size * 2
+        self.rnn = torch.nn.LSTM(
+            input_size=hidden_size,
+            hidden_size=decoder_rnn_dim,
+            num_layers=1,
+            batch_first=True,
+            bidirectional=True,
+            dropout=dropout
+        )
+        self.rnn.flatten_parameters()
+        self.conv1d = torch.nn.Conv1d(
+            in_channels=decoder_rnn_dim * 2,
+            out_channels=hidden_size,
+            kernel_size=3,
+            padding=1,
+        )
+
+    def forward(self, x):
+        input_masks = x.abs().sum(-1).ne(0).data[:, :, None]
+        input_lengths = input_masks.sum([-1, -2])
+        input_lengths = input_lengths.cpu().numpy()
+
+        x = self.in_conv1d(x.transpose(1, 2)).transpose(1, 2)
+        x = self.ln(x)
+        x = nn.utils.rnn.pack_padded_sequence(x, input_lengths, batch_first=True, enforce_sorted=False)
+        self.rnn.flatten_parameters()
+        x, _ = self.rnn(x)  # [B, T, C]
+        x, _ = nn.utils.rnn.pad_packed_sequence(x, batch_first=True)
+        x = x * input_masks
+        pre_mel = self.conv1d(x.transpose(1, 2)).transpose(1, 2)  # [B, T, C]
+        pre_mel = pre_mel * input_masks
+        return pre_mel

preprocess/tools/note_transcription/modules/commons/transformer.py

@@ -0,0 +1,751 @@
+import math
+import torch
+from torch import nn
+from torch.nn import Parameter, Linear
+from .layers import LayerNorm, Embedding
+from ...utils.nn.seq_utils import (
+    get_incremental_state,
+    set_incremental_state,
+    softmax,
+    make_positions,
+)
+import torch.nn.functional as F
+
+DEFAULT_MAX_SOURCE_POSITIONS = 2000
+DEFAULT_MAX_TARGET_POSITIONS = 2000
+
+
+class SinusoidalPositionalEmbedding(nn.Module):
+    """This module produces sinusoidal positional embeddings of any length.
+
+    Padding symbols are ignored.
+    """
+
+    def __init__(self, embedding_dim, padding_idx, init_size=1024):
+        super().__init__()
+        self.embedding_dim = embedding_dim
+        self.padding_idx = padding_idx
+        self.weights = SinusoidalPositionalEmbedding.get_embedding(
+            init_size,
+            embedding_dim,
+            padding_idx,
+        )
+        self.register_buffer('_float_tensor', torch.FloatTensor(1))
+
+    @staticmethod
+    def get_embedding(num_embeddings, embedding_dim, padding_idx=None):
+        """Build sinusoidal embeddings.
+
+        This matches the implementation in tensor2tensor, but differs slightly
+        from the description in Section 3.5 of "Attention Is All You Need".
+        """
+        half_dim = embedding_dim // 2
+        emb = math.log(10000) / (half_dim - 1)
+        emb = torch.exp(torch.arange(half_dim, dtype=torch.float) * -emb)
+        emb = torch.arange(num_embeddings, dtype=torch.float).unsqueeze(1) * emb.unsqueeze(0)
+        emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)
+        if embedding_dim % 2 == 1:
+            # zero pad
+            emb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)
+        if padding_idx is not None:
+            emb[padding_idx, :] = 0
+        return emb
+
+    def forward(self, input, incremental_state=None, timestep=None, positions=None, **kwargs):
+        """Input is expected to be of size [bsz x seqlen]."""
+        bsz, seq_len = input.shape[:2]
+        max_pos = self.padding_idx + 1 + seq_len
+        if self.weights is None or max_pos > self.weights.size(0):
+            # recompute/expand embeddings if needed
+            self.weights = SinusoidalPositionalEmbedding.get_embedding(
+                max_pos,
+                self.embedding_dim,
+                self.padding_idx,
+            )
+        self.weights = self.weights.to(self._float_tensor)
+
+        if incremental_state is not None:
+            # positions is the same for every token when decoding a single step
+            pos = timestep.view(-1)[0] + 1 if timestep is not None else seq_len
+            return self.weights[self.padding_idx + pos, :].expand(bsz, 1, -1)
+
+        positions = make_positions(input, self.padding_idx) if positions is None else positions
+        return self.weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1).detach()
+
+    def max_positions(self):
+        """Maximum number of supported positions."""
+        return int(1e5)  # an arbitrary large number
+
+
+class TransformerFFNLayer(nn.Module):
+    def __init__(self, hidden_size, filter_size, padding="SAME", kernel_size=1, dropout=0., act='gelu'):
+        super().__init__()
+        self.kernel_size = kernel_size
+        self.dropout = dropout
+        self.act = act
+        if padding == 'SAME':
+            self.ffn_1 = nn.Conv1d(hidden_size, filter_size, kernel_size, padding=kernel_size // 2)
+        elif padding == 'LEFT':
+            self.ffn_1 = nn.Sequential(
+                nn.ConstantPad1d((kernel_size - 1, 0), 0.0),
+                nn.Conv1d(hidden_size, filter_size, kernel_size)
+            )
+        self.ffn_2 = Linear(filter_size, hidden_size)
+
+    def forward(self, x, incremental_state=None):
+        # x: T x B x C
+        if incremental_state is not None:
+            saved_state = self._get_input_buffer(incremental_state)
+            if 'prev_input' in saved_state:
+                prev_input = saved_state['prev_input']
+                x = torch.cat((prev_input, x), dim=0)
+            x = x[-self.kernel_size:]
+            saved_state['prev_input'] = x
+            self._set_input_buffer(incremental_state, saved_state)
+
+        x = self.ffn_1(x.permute(1, 2, 0)).permute(2, 0, 1)
+        x = x * self.kernel_size ** -0.5
+
+        if incremental_state is not None:
+            x = x[-1:]
+        if self.act == 'gelu':
+            x = F.gelu(x)
+        if self.act == 'relu':
+            x = F.relu(x)
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = self.ffn_2(x)
+        return x
+
+    def _get_input_buffer(self, incremental_state):
+        return get_incremental_state(
+            self,
+            incremental_state,
+            'f',
+        ) or {}
+
+    def _set_input_buffer(self, incremental_state, buffer):
+        set_incremental_state(
+            self,
+            incremental_state,
+            'f',
+            buffer,
+        )
+
+    def clear_buffer(self, incremental_state):
+        if incremental_state is not None:
+            saved_state = self._get_input_buffer(incremental_state)
+            if 'prev_input' in saved_state:
+                del saved_state['prev_input']
+            self._set_input_buffer(incremental_state, saved_state)
+
+
+class MultiheadAttention(nn.Module):
+    def __init__(self, embed_dim, num_heads, kdim=None, vdim=None, dropout=0., bias=True,
+                 add_bias_kv=False, add_zero_attn=False, self_attention=False,
+                 encoder_decoder_attention=False):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self.qkv_same_dim = self.kdim == embed_dim and self.vdim == embed_dim
+
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+        self.scaling = self.head_dim ** -0.5
+
+        self.self_attention = self_attention
+        self.encoder_decoder_attention = encoder_decoder_attention
+
+        assert not self.self_attention or self.qkv_same_dim, 'Self-attention requires query, key and ' \
+                                                             'value to be of the same size'
+
+        if self.qkv_same_dim:
+            self.in_proj_weight = Parameter(torch.Tensor(3 * embed_dim, embed_dim))
+        else:
+            self.k_proj_weight = Parameter(torch.Tensor(embed_dim, self.kdim))
+            self.v_proj_weight = Parameter(torch.Tensor(embed_dim, self.vdim))
+            self.q_proj_weight = Parameter(torch.Tensor(embed_dim, embed_dim))
+
+        if bias:
+            self.in_proj_bias = Parameter(torch.Tensor(3 * embed_dim))
+        else:
+            self.register_parameter('in_proj_bias', None)
+
+        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+        if add_bias_kv:
+            self.bias_k = Parameter(torch.Tensor(1, 1, embed_dim))
+            self.bias_v = Parameter(torch.Tensor(1, 1, embed_dim))
+        else:
+            self.bias_k = self.bias_v = None
+
+        self.add_zero_attn = add_zero_attn
+
+        self.reset_parameters()
+
+        self.enable_torch_version = False
+        if hasattr(F, "multi_head_attention_forward"):
+            self.enable_torch_version = True
+        else:
+            self.enable_torch_version = False
+        self.last_attn_probs = None
+
+    def reset_parameters(self):
+        if self.qkv_same_dim:
+            nn.init.xavier_uniform_(self.in_proj_weight)
+        else:
+            nn.init.xavier_uniform_(self.k_proj_weight)
+            nn.init.xavier_uniform_(self.v_proj_weight)
+            nn.init.xavier_uniform_(self.q_proj_weight)
+
+        nn.init.xavier_uniform_(self.out_proj.weight)
+        if self.in_proj_bias is not None:
+            nn.init.constant_(self.in_proj_bias, 0.)
+            nn.init.constant_(self.out_proj.bias, 0.)
+        if self.bias_k is not None:
+            nn.init.xavier_normal_(self.bias_k)
+        if self.bias_v is not None:
+            nn.init.xavier_normal_(self.bias_v)
+
+    def forward(
+            self,
+            query, key, value,
+            key_padding_mask=None,
+            incremental_state=None,
+            need_weights=True,
+            static_kv=False,
+            attn_mask=None,
+            before_softmax=False,
+            need_head_weights=False,
+            enc_dec_attn_constraint_mask=None,
+            reset_attn_weight=None
+    ):
+        """Input shape: Time x Batch x Channel
+
+        Args:
+            key_padding_mask (ByteTensor, optional): mask to exclude
+                keys that are pads, of shape `(batch, src_len)`, where
+                padding elements are indicated by 1s.
+            need_weights (bool, optional): return the attention weights,
+                averaged over heads (default: False).
+            attn_mask (ByteTensor, optional): typically used to
+                implement causal attention, where the mask prevents the
+                attention from looking forward in time (default: None).
+            before_softmax (bool, optional): return the raw attention
+                weights and values before the attention softmax.
+            need_head_weights (bool, optional): return the attention
+                weights for each head. Implies *need_weights*. Default:
+                return the average attention weights over all heads.
+        """
+        if need_head_weights:
+            need_weights = True
+
+        tgt_len, bsz, embed_dim = query.size()
+        assert embed_dim == self.embed_dim
+        assert list(query.size()) == [tgt_len, bsz, embed_dim]
+        if self.enable_torch_version and incremental_state is None and not static_kv and reset_attn_weight is None:
+            if self.qkv_same_dim:
+                return F.multi_head_attention_forward(query, key, value,
+                                                      self.embed_dim, self.num_heads,
+                                                      self.in_proj_weight,
+                                                      self.in_proj_bias, self.bias_k, self.bias_v,
+                                                      self.add_zero_attn, self.dropout,
+                                                      self.out_proj.weight, self.out_proj.bias,
+                                                      self.training, key_padding_mask, need_weights,
+                                                      attn_mask)
+            else:
+                return F.multi_head_attention_forward(query, key, value,
+                                                      self.embed_dim, self.num_heads,
+                                                      torch.empty([0]),
+                                                      self.in_proj_bias, self.bias_k, self.bias_v,
+                                                      self.add_zero_attn, self.dropout,
+                                                      self.out_proj.weight, self.out_proj.bias,
+                                                      self.training, key_padding_mask, need_weights,
+                                                      attn_mask, use_separate_proj_weight=True,
+                                                      q_proj_weight=self.q_proj_weight,
+                                                      k_proj_weight=self.k_proj_weight,
+                                                      v_proj_weight=self.v_proj_weight)
+
+        if incremental_state is not None:
+            saved_state = self._get_input_buffer(incremental_state)
+            if 'prev_key' in saved_state:
+                # previous time steps are cached - no need to recompute
+                # key and value if they are static
+                if static_kv:
+                    assert self.encoder_decoder_attention and not self.self_attention
+                    key = value = None
+        else:
+            saved_state = None
+
+        if self.self_attention:
+            # self-attention
+            q, k, v = self.in_proj_qkv(query)
+        elif self.encoder_decoder_attention:
+            # encoder-decoder attention
+            q = self.in_proj_q(query)
+            if key is None:
+                assert value is None
+                k = v = None
+            else:
+                k = self.in_proj_k(key)
+                v = self.in_proj_v(key)
+
+        else:
+            q = self.in_proj_q(query)
+            k = self.in_proj_k(key)
+            v = self.in_proj_v(value)
+        q *= self.scaling
+
+        if self.bias_k is not None:
+            assert self.bias_v is not None
+            k = torch.cat([k, self.bias_k.repeat(1, bsz, 1)])
+            v = torch.cat([v, self.bias_v.repeat(1, bsz, 1)])
+            if attn_mask is not None:
+                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [key_padding_mask, key_padding_mask.new_zeros(key_padding_mask.size(0), 1)], dim=1)
+
+        q = q.contiguous().view(tgt_len, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+        if k is not None:
+            k = k.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+        if v is not None:
+            v = v.contiguous().view(-1, bsz * self.num_heads, self.head_dim).transpose(0, 1)
+
+        if saved_state is not None:
+            # saved states are stored with shape (bsz, num_heads, seq_len, head_dim)
+            if 'prev_key' in saved_state:
+                prev_key = saved_state['prev_key'].view(bsz * self.num_heads, -1, self.head_dim)
+                if static_kv:
+                    k = prev_key
+                else:
+                    k = torch.cat((prev_key, k), dim=1)
+            if 'prev_value' in saved_state:
+                prev_value = saved_state['prev_value'].view(bsz * self.num_heads, -1, self.head_dim)
+                if static_kv:
+                    v = prev_value
+                else:
+                    v = torch.cat((prev_value, v), dim=1)
+            if 'prev_key_padding_mask' in saved_state and saved_state['prev_key_padding_mask'] is not None:
+                prev_key_padding_mask = saved_state['prev_key_padding_mask']
+                if static_kv:
+                    key_padding_mask = prev_key_padding_mask
+                else:
+                    key_padding_mask = torch.cat((prev_key_padding_mask, key_padding_mask), dim=1)
+
+            saved_state['prev_key'] = k.view(bsz, self.num_heads, -1, self.head_dim)
+            saved_state['prev_value'] = v.view(bsz, self.num_heads, -1, self.head_dim)
+            saved_state['prev_key_padding_mask'] = key_padding_mask
+
+            self._set_input_buffer(incremental_state, saved_state)
+
+        src_len = k.size(1)
+
+        # This is part of a workaround to get around fork/join parallelism
+        # not supporting Optional types.
+        if key_padding_mask is not None and key_padding_mask.shape == torch.Size([]):
+            key_padding_mask = None
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.size(0) == bsz
+            assert key_padding_mask.size(1) == src_len
+
+        if self.add_zero_attn:
+            src_len += 1
+            k = torch.cat([k, k.new_zeros((k.size(0), 1) + k.size()[2:])], dim=1)
+            v = torch.cat([v, v.new_zeros((v.size(0), 1) + v.size()[2:])], dim=1)
+            if attn_mask is not None:
+                attn_mask = torch.cat([attn_mask, attn_mask.new_zeros(attn_mask.size(0), 1)], dim=1)
+            if key_padding_mask is not None:
+                key_padding_mask = torch.cat(
+                    [key_padding_mask, torch.zeros(key_padding_mask.size(0), 1).type_as(key_padding_mask)], dim=1)
+
+        attn_weights = torch.bmm(q, k.transpose(1, 2))
+        attn_weights = self.apply_sparse_mask(attn_weights, tgt_len, src_len, bsz)
+
+        assert list(attn_weights.size()) == [bsz * self.num_heads, tgt_len, src_len]
+
+        if attn_mask is not None:
+            if len(attn_mask.shape) == 2:
+                attn_mask = attn_mask.unsqueeze(0)
+            elif len(attn_mask.shape) == 3:
+                attn_mask = attn_mask[:, None].repeat([1, self.num_heads, 1, 1]).reshape(
+                    bsz * self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights + attn_mask
+
+        if enc_dec_attn_constraint_mask is not None:  # bs x head x L_kv
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights.masked_fill(
+                enc_dec_attn_constraint_mask.unsqueeze(2).bool(),
+                -1e8,
+            )
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        if key_padding_mask is not None:
+            # don't attend to padding symbols
+            attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+            attn_weights = attn_weights.masked_fill(
+                key_padding_mask.unsqueeze(1).unsqueeze(2),
+                -1e8,
+            )
+            attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
+
+        attn_logits = attn_weights.view(bsz, self.num_heads, tgt_len, src_len)
+
+        if before_softmax:
+            return attn_weights, v
+
+        attn_weights_float = softmax(attn_weights, dim=-1)
+        attn_weights = attn_weights_float.type_as(attn_weights)
+        attn_probs = F.dropout(attn_weights_float.type_as(attn_weights), p=self.dropout, training=self.training)
+
+        if reset_attn_weight is not None:
+            if reset_attn_weight:
+                self.last_attn_probs = attn_probs.detach()
+            else:
+                assert self.last_attn_probs is not None
+                attn_probs = self.last_attn_probs
+        attn = torch.bmm(attn_probs, v)
+        assert list(attn.size()) == [bsz * self.num_heads, tgt_len, self.head_dim]
+        attn = attn.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
+        attn = self.out_proj(attn)
+
+        if need_weights:
+            attn_weights = attn_weights_float.view(bsz, self.num_heads, tgt_len, src_len).transpose(1, 0)
+            if not need_head_weights:
+                # average attention weights over heads
+                attn_weights = attn_weights.mean(dim=0)
+        else:
+            attn_weights = None
+
+        return attn, (attn_weights, attn_logits)
+
+    def in_proj_qkv(self, query):
+        return self._in_proj(query).chunk(3, dim=-1)
+
+    def in_proj_q(self, query):
+        if self.qkv_same_dim:
+            return self._in_proj(query, end=self.embed_dim)
+        else:
+            bias = self.in_proj_bias
+            if bias is not None:
+                bias = bias[:self.embed_dim]
+            return F.linear(query, self.q_proj_weight, bias)
+
+    def in_proj_k(self, key):
+        if self.qkv_same_dim:
+            return self._in_proj(key, start=self.embed_dim, end=2 * self.embed_dim)
+        else:
+            weight = self.k_proj_weight
+            bias = self.in_proj_bias
+            if bias is not None:
+                bias = bias[self.embed_dim:2 * self.embed_dim]
+            return F.linear(key, weight, bias)
+
+    def in_proj_v(self, value):
+        if self.qkv_same_dim:
+            return self._in_proj(value, start=2 * self.embed_dim)
+        else:
+            weight = self.v_proj_weight
+            bias = self.in_proj_bias
+            if bias is not None:
+                bias = bias[2 * self.embed_dim:]
+            return F.linear(value, weight, bias)
+
+    def _in_proj(self, input, start=0, end=None):
+        weight = self.in_proj_weight
+        bias = self.in_proj_bias
+        weight = weight[start:end, :]
+        if bias is not None:
+            bias = bias[start:end]
+        return F.linear(input, weight, bias)
+
+    def _get_input_buffer(self, incremental_state):
+        return get_incremental_state(
+            self,
+            incremental_state,
+            'attn_state',
+        ) or {}
+
+    def _set_input_buffer(self, incremental_state, buffer):
+        set_incremental_state(
+            self,
+            incremental_state,
+            'attn_state',
+            buffer,
+        )
+
+    def apply_sparse_mask(self, attn_weights, tgt_len, src_len, bsz):
+        return attn_weights
+
+    def clear_buffer(self, incremental_state=None):
+        if incremental_state is not None:
+            saved_state = self._get_input_buffer(incremental_state)
+            if 'prev_key' in saved_state:
+                del saved_state['prev_key']
+            if 'prev_value' in saved_state:
+                del saved_state['prev_value']
+            self._set_input_buffer(incremental_state, saved_state)
+
+
+class EncSALayer(nn.Module):
+    def __init__(self, c, num_heads, dropout, attention_dropout=0.1,
+                 relu_dropout=0.1, kernel_size=9, padding='SAME', act='gelu'):
+        super().__init__()
+        self.c = c
+        self.dropout = dropout
+        self.num_heads = num_heads
+        if num_heads > 0:
+            self.layer_norm1 = LayerNorm(c)
+            self.self_attn = MultiheadAttention(
+                self.c, num_heads, self_attention=True, dropout=attention_dropout, bias=False)
+        self.layer_norm2 = LayerNorm(c)
+        self.ffn = TransformerFFNLayer(
+            c, 4 * c, kernel_size=kernel_size, dropout=relu_dropout, padding=padding, act=act)
+
+    def forward(self, x, encoder_padding_mask=None, **kwargs):
+        layer_norm_training = kwargs.get('layer_norm_training', None)
+        if layer_norm_training is not None:
+            self.layer_norm1.training = layer_norm_training
+            self.layer_norm2.training = layer_norm_training
+        if self.num_heads > 0:
+            residual = x
+            x = self.layer_norm1(x)
+            x, _, = self.self_attn(
+                query=x,
+                key=x,
+                value=x,
+                key_padding_mask=encoder_padding_mask
+            )
+            x = F.dropout(x, self.dropout, training=self.training)
+            x = residual + x
+            x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
+
+        residual = x
+        x = self.layer_norm2(x)
+        x = self.ffn(x)
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = residual + x
+        x = x * (1 - encoder_padding_mask.float()).transpose(0, 1)[..., None]
+        return x
+
+
+class DecSALayer(nn.Module):
+    def __init__(self, c, num_heads, dropout, attention_dropout=0.1, relu_dropout=0.1,
+                 kernel_size=9, act='gelu'):
+        super().__init__()
+        self.c = c
+        self.dropout = dropout
+        self.layer_norm1 = LayerNorm(c)
+        self.self_attn = MultiheadAttention(
+            c, num_heads, self_attention=True, dropout=attention_dropout, bias=False
+        )
+        self.layer_norm2 = LayerNorm(c)
+        self.encoder_attn = MultiheadAttention(
+            c, num_heads, encoder_decoder_attention=True, dropout=attention_dropout, bias=False,
+        )
+        self.layer_norm3 = LayerNorm(c)
+        self.ffn = TransformerFFNLayer(
+            c, 4 * c, padding='LEFT', kernel_size=kernel_size, dropout=relu_dropout, act=act)
+
+    def forward(
+            self,
+            x,
+            encoder_out=None,
+            encoder_padding_mask=None,
+            incremental_state=None,
+            self_attn_mask=None,
+            self_attn_padding_mask=None,
+            attn_out=None,
+            reset_attn_weight=None,
+            **kwargs,
+    ):
+        layer_norm_training = kwargs.get('layer_norm_training', None)
+        if layer_norm_training is not None:
+            self.layer_norm1.training = layer_norm_training
+            self.layer_norm2.training = layer_norm_training
+            self.layer_norm3.training = layer_norm_training
+        residual = x
+        x = self.layer_norm1(x)
+        x, _ = self.self_attn(
+            query=x,
+            key=x,
+            value=x,
+            key_padding_mask=self_attn_padding_mask,
+            incremental_state=incremental_state,
+            attn_mask=self_attn_mask
+        )
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = residual + x
+
+        attn_logits = None
+        if encoder_out is not None or attn_out is not None:
+            residual = x
+            x = self.layer_norm2(x)
+        if encoder_out is not None:
+            x, attn = self.encoder_attn(
+                query=x,
+                key=encoder_out,
+                value=encoder_out,
+                key_padding_mask=encoder_padding_mask,
+                incremental_state=incremental_state,
+                static_kv=True,
+                enc_dec_attn_constraint_mask=get_incremental_state(self, incremental_state,
+                                                                   'enc_dec_attn_constraint_mask'),
+                reset_attn_weight=reset_attn_weight
+            )
+            attn_logits = attn[1]
+        elif attn_out is not None:
+            x = self.encoder_attn.in_proj_v(attn_out)
+        if encoder_out is not None or attn_out is not None:
+            x = F.dropout(x, self.dropout, training=self.training)
+            x = residual + x
+
+        residual = x
+        x = self.layer_norm3(x)
+        x = self.ffn(x, incremental_state=incremental_state)
+        x = F.dropout(x, self.dropout, training=self.training)
+        x = residual + x
+        return x, attn_logits
+
+    def clear_buffer(self, input, encoder_out=None, encoder_padding_mask=None, incremental_state=None):
+        self.encoder_attn.clear_buffer(incremental_state)
+        self.ffn.clear_buffer(incremental_state)
+
+    def set_buffer(self, name, tensor, incremental_state):
+        return set_incremental_state(self, incremental_state, name, tensor)
+
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(self, hidden_size, dropout, kernel_size=9, num_heads=2):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.dropout = dropout
+        self.num_heads = num_heads
+        self.op = EncSALayer(
+            hidden_size, num_heads, dropout=dropout,
+            attention_dropout=0.0, relu_dropout=dropout,
+            kernel_size=kernel_size)
+
+    def forward(self, x, **kwargs):
+        return self.op(x, **kwargs)
+
+
+class TransformerDecoderLayer(nn.Module):
+    def __init__(self, hidden_size, dropout, kernel_size=9, num_heads=2):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.dropout = dropout
+        self.num_heads = num_heads
+        self.op = DecSALayer(
+            hidden_size, num_heads, dropout=dropout,
+            attention_dropout=0.0, relu_dropout=dropout,
+            kernel_size=kernel_size)
+
+    def forward(self, x, **kwargs):
+        return self.op(x, **kwargs)
+
+    def clear_buffer(self, *args):
+        return self.op.clear_buffer(*args)
+
+    def set_buffer(self, *args):
+        return self.op.set_buffer(*args)
+
+
+class FFTBlocks(nn.Module):
+    def __init__(self, hidden_size, num_layers, ffn_kernel_size=9, dropout=0.0,
+                 num_heads=2, use_pos_embed=True, use_last_norm=True,
+                 use_pos_embed_alpha=True):
+        super().__init__()
+        self.num_layers = num_layers
+        embed_dim = self.hidden_size = hidden_size
+        self.dropout = dropout
+        self.use_pos_embed = use_pos_embed
+        self.use_last_norm = use_last_norm
+        if use_pos_embed:
+            self.max_source_positions = DEFAULT_MAX_TARGET_POSITIONS
+            self.padding_idx = 0
+            self.pos_embed_alpha = nn.Parameter(torch.Tensor([1])) if use_pos_embed_alpha else 1
+            self.embed_positions = SinusoidalPositionalEmbedding(
+                embed_dim, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS,
+            )
+
+        self.layers = nn.ModuleList([])
+        self.layers.extend([
+            TransformerEncoderLayer(self.hidden_size, self.dropout,
+                                    kernel_size=ffn_kernel_size, num_heads=num_heads)
+            for _ in range(self.num_layers)
+        ])
+        if self.use_last_norm:
+            self.layer_norm = nn.LayerNorm(embed_dim)
+        else:
+            self.layer_norm = None
+
+    def forward(self, x, padding_mask=None, attn_mask=None, return_hiddens=False):
+        """
+        :param x: [B, T, C]
+        :param padding_mask: [B, T]
+        :return: [B, T, C] or [L, B, T, C]
+        """
+        padding_mask = x.abs().sum(-1).eq(0).data if padding_mask is None else padding_mask
+        nonpadding_mask_TB = 1 - padding_mask.transpose(0, 1).float()[:, :, None]  # [T, B, 1]
+        if self.use_pos_embed:
+            positions = self.pos_embed_alpha * self.embed_positions(x[..., 0])
+            x = x + positions
+            x = F.dropout(x, p=self.dropout, training=self.training)
+        # B x T x C -> T x B x C
+        x = x.transpose(0, 1) * nonpadding_mask_TB
+        hiddens = []
+        for layer in self.layers:
+            x = layer(x, encoder_padding_mask=padding_mask, attn_mask=attn_mask) * nonpadding_mask_TB
+            hiddens.append(x)
+        if self.use_last_norm:
+            x = self.layer_norm(x) * nonpadding_mask_TB
+        if return_hiddens:
+            x = torch.stack(hiddens, 0)  # [L, T, B, C]
+            x = x.transpose(1, 2)  # [L, B, T, C]
+        else:
+            x = x.transpose(0, 1)  # [B, T, C]
+        return x
+
+
+class FastSpeechEncoder(FFTBlocks):
+    def __init__(self, dict_size, hidden_size=256, num_layers=4, kernel_size=9, num_heads=2,
+                 dropout=0.0):
+        super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads,
+                         use_pos_embed=False, dropout=dropout)  # use_pos_embed_alpha for compatibility
+        self.embed_tokens = Embedding(dict_size, hidden_size, 0)
+        self.embed_scale = math.sqrt(hidden_size)
+        self.padding_idx = 0
+        self.embed_positions = SinusoidalPositionalEmbedding(
+            hidden_size, self.padding_idx, init_size=DEFAULT_MAX_TARGET_POSITIONS,
+        )
+
+    def forward(self, txt_tokens, attn_mask=None):
+        """
+
+        :param txt_tokens: [B, T]
+        :return: {
+            'encoder_out': [B x T x C]
+        }
+        """
+        encoder_padding_mask = txt_tokens.eq(self.padding_idx).data
+        x = self.forward_embedding(txt_tokens)  # [B, T, H]
+        if self.num_layers > 0:
+            x = super(FastSpeechEncoder, self).forward(x, encoder_padding_mask, attn_mask=attn_mask)
+        return x
+
+    def forward_embedding(self, txt_tokens):
+        # embed tokens and positions
+        x = self.embed_scale * self.embed_tokens(txt_tokens)
+        positions = self.embed_positions(txt_tokens)
+        x = x + positions
+        x = F.dropout(x, p=self.dropout, training=self.training)
+        return x
+
+
+class FastSpeechDecoder(FFTBlocks):
+    def __init__(self, hidden_size=256, num_layers=4, kernel_size=9, num_heads=2):
+        super().__init__(hidden_size, num_layers, kernel_size, num_heads=num_heads)

preprocess/tools/note_transcription/modules/commons/wavenet.py

@@ -0,0 +1,109 @@
+import torch
+from torch import nn
+from packaging import version
+
+def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
+    n_channels_int = n_channels[0]
+    in_act = input_a + input_b
+    t_act = torch.tanh(in_act[:, :n_channels_int, :])
+    s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
+    acts = t_act * s_act
+    return acts
+
+jit_fused_add_tanh_sigmoid_multiply = fused_add_tanh_sigmoid_multiply
+
+def script_function():
+    if version.parse(torch.__version__) >= version.parse('2.0'):
+        global jit_fused_add_tanh_sigmoid_multiply
+        jit_fused_add_tanh_sigmoid_multiply = torch.jit.script(fused_add_tanh_sigmoid_multiply)
+
+
+class WN(torch.nn.Module):
+    def __init__(self, hidden_size, kernel_size, dilation_rate, n_layers, c_cond=0,
+                 p_dropout=0, share_cond_layers=False, is_BTC=False):
+        super(WN, self).__init__()
+        assert (kernel_size % 2 == 1)
+        assert (hidden_size % 2 == 0)
+        self.is_BTC = is_BTC
+        self.hidden_size = hidden_size
+        self.kernel_size = kernel_size
+        self.dilation_rate = dilation_rate
+        self.n_layers = n_layers
+        self.gin_channels = c_cond
+        self.p_dropout = p_dropout
+        self.share_cond_layers = share_cond_layers
+
+        self.in_layers = torch.nn.ModuleList()
+        self.res_skip_layers = torch.nn.ModuleList()
+        self.drop = nn.Dropout(p_dropout)
+
+        if c_cond != 0 and not share_cond_layers:
+            cond_layer = torch.nn.Conv1d(c_cond, 2 * hidden_size * n_layers, 1)
+            self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
+
+        for i in range(n_layers):
+            dilation = dilation_rate ** i
+            padding = int((kernel_size * dilation - dilation) / 2)
+            in_layer = torch.nn.Conv1d(hidden_size, 2 * hidden_size, kernel_size,
+                                       dilation=dilation, padding=padding)
+            in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
+            self.in_layers.append(in_layer)
+
+            # last one is not necessary
+            if i < n_layers - 1:
+                res_skip_channels = 2 * hidden_size
+            else:
+                res_skip_channels = hidden_size
+
+            res_skip_layer = torch.nn.Conv1d(hidden_size, res_skip_channels, 1)
+            res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
+            self.res_skip_layers.append(res_skip_layer)
+
+        script_function()
+
+    def forward(self, x, nonpadding=None, cond=None):
+        if self.is_BTC:
+            x = x.transpose(1, 2)
+            cond = cond.transpose(1, 2) if cond is not None else None
+            nonpadding = nonpadding.transpose(1, 2) if nonpadding is not None else None
+        if nonpadding is None:
+            nonpadding = 1
+        output = torch.zeros_like(x)
+        n_channels_tensor = torch.IntTensor([self.hidden_size])
+
+        if cond is not None and not self.share_cond_layers:
+            cond = self.cond_layer(cond)
+
+        for i in range(self.n_layers):
+            x_in = self.in_layers[i](x)
+            x_in = self.drop(x_in)
+            if cond is not None:
+                cond_offset = i * 2 * self.hidden_size
+                cond_l = cond[:, cond_offset:cond_offset + 2 * self.hidden_size, :]
+            else:
+                cond_l = torch.zeros_like(x_in)
+
+            if version.parse(torch.__version__) >= version.parse('2.0'):
+                acts = jit_fused_add_tanh_sigmoid_multiply(x_in, cond_l, n_channels_tensor)
+            else:
+                acts = fused_add_tanh_sigmoid_multiply(x_in, cond_l, n_channels_tensor)
+
+            res_skip_acts = self.res_skip_layers[i](acts)
+            if i < self.n_layers - 1:
+                x = (x + res_skip_acts[:, :self.hidden_size, :]) * nonpadding
+                output = output + res_skip_acts[:, self.hidden_size:, :]
+            else:
+                output = output + res_skip_acts
+        output = output * nonpadding
+        if self.is_BTC:
+            output = output.transpose(1, 2)
+        return output
+
+    def remove_weight_norm(self):
+        def remove_weight_norm(m):
+            try:
+                nn.utils.remove_weight_norm(m)
+            except ValueError:  # this module didn't have weight norm
+                return
+
+        self.apply(remove_weight_norm)

preprocess/tools/note_transcription/modules/pe/__init__.py

@@ -0,0 +1 @@
+"""Pitch extractor modules for ROSVOT."""

preprocess/tools/note_transcription/modules/pe/rmvpe/__init__.py

@@ -0,0 +1,6 @@
+from .constants import *
+from .model import E2E0
+from .utils import to_local_average_f0, to_viterbi_f0
+from .inference import RMVPE
+from .spec import MelSpectrogram
+from .extractor import extract

preprocess/tools/note_transcription/modules/pe/rmvpe/constants.py

@@ -0,0 +1,9 @@
+SAMPLE_RATE = 16000
+
+N_CLASS = 360
+
+N_MELS = 128
+MEL_FMIN = 30
+MEL_FMAX = 8000
+WINDOW_LENGTH = 1024
+CONST = 1997.3794084376191

preprocess/tools/note_transcription/modules/pe/rmvpe/deepunet.py

@@ -0,0 +1,173 @@
+import torch
+import torch.nn as nn
+from .constants import N_MELS
+
+
+class ConvBlockRes(nn.Module):
+    def __init__(self, in_channels, out_channels, momentum=0.01):
+        super(ConvBlockRes, self).__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(in_channels=in_channels,
+                      out_channels=out_channels,
+                      kernel_size=(3, 3),
+                      stride=(1, 1),
+                      padding=(1, 1),
+                      bias=False),
+            nn.BatchNorm2d(out_channels, momentum=momentum),
+            nn.ReLU(),
+
+            nn.Conv2d(in_channels=out_channels,
+                      out_channels=out_channels,
+                      kernel_size=(3, 3),
+                      stride=(1, 1),
+                      padding=(1, 1),
+                      bias=False),
+            nn.BatchNorm2d(out_channels, momentum=momentum),
+            nn.ReLU(),
+        )
+        if in_channels != out_channels:
+            self.shortcut = nn.Conv2d(in_channels, out_channels, (1, 1))
+            self.is_shortcut = True
+        else:
+            self.is_shortcut = False
+
+    def forward(self, x):
+        if self.is_shortcut:
+            return self.conv(x) + self.shortcut(x)
+        else:
+            return self.conv(x) + x
+
+
+class ResEncoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01):
+        super(ResEncoderBlock, self).__init__()
+        self.n_blocks = n_blocks
+        self.conv = nn.ModuleList()
+        self.conv.append(ConvBlockRes(in_channels, out_channels, momentum))
+        for i in range(n_blocks - 1):
+            self.conv.append(ConvBlockRes(out_channels, out_channels, momentum))
+        self.kernel_size = kernel_size
+        if self.kernel_size is not None:
+            self.pool = nn.AvgPool2d(kernel_size=kernel_size)
+
+    def forward(self, x):
+        for i in range(self.n_blocks):
+            x = self.conv[i](x)
+        if self.kernel_size is not None:
+            return x, self.pool(x)
+        else:
+            return x
+
+
+class ResDecoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, n_blocks=1, momentum=0.01):
+        super(ResDecoderBlock, self).__init__()
+        out_padding = (0, 1) if stride == (1, 2) else (1, 1)
+        self.n_blocks = n_blocks
+        self.conv1 = nn.Sequential(
+            nn.ConvTranspose2d(in_channels=in_channels,
+                               out_channels=out_channels,
+                               kernel_size=(3, 3),
+                               stride=stride,
+                               padding=(1, 1),
+                               output_padding=out_padding,
+                               bias=False),
+            nn.BatchNorm2d(out_channels, momentum=momentum),
+            nn.ReLU(),
+        )
+        self.conv2 = nn.ModuleList()
+        self.conv2.append(ConvBlockRes(out_channels * 2, out_channels, momentum))
+        for i in range(n_blocks-1):
+            self.conv2.append(ConvBlockRes(out_channels, out_channels, momentum))
+
+    def forward(self, x, concat_tensor):
+        x = self.conv1(x)
+        x = torch.cat((x, concat_tensor), dim=1)
+        for i in range(self.n_blocks):
+            x = self.conv2[i](x)
+        return x
+
+
+class Encoder(nn.Module):
+    def __init__(self, in_channels, in_size, n_encoders, kernel_size, n_blocks, out_channels=16, momentum=0.01):
+        super(Encoder, self).__init__()
+        self.n_encoders = n_encoders
+        self.bn = nn.BatchNorm2d(in_channels, momentum=momentum)
+        self.layers = nn.ModuleList()
+        self.latent_channels = []
+        for i in range(self.n_encoders):
+            self.layers.append(ResEncoderBlock(in_channels, out_channels, kernel_size, n_blocks, momentum=momentum))
+            self.latent_channels.append([out_channels, in_size])
+            in_channels = out_channels
+            out_channels *= 2
+            in_size //= 2
+        self.out_size = in_size
+        self.out_channel = out_channels
+
+    def forward(self, x):
+        concat_tensors = []
+        x = self.bn(x)
+        for i in range(self.n_encoders):
+            _, x = self.layers[i](x)
+            concat_tensors.append(_)
+        return x, concat_tensors
+
+
+class Intermediate(nn.Module):
+    def __init__(self, in_channels, out_channels, n_inters, n_blocks, momentum=0.01):
+        super(Intermediate, self).__init__()
+        self.n_inters = n_inters
+        self.layers = nn.ModuleList()
+        self.layers.append(ResEncoderBlock(in_channels, out_channels, None, n_blocks, momentum))
+        for i in range(self.n_inters-1):
+            self.layers.append(ResEncoderBlock(out_channels, out_channels, None, n_blocks, momentum))
+
+    def forward(self, x):
+        for i in range(self.n_inters):
+            x = self.layers[i](x)
+        return x
+
+
+class Decoder(nn.Module):
+    def __init__(self, in_channels, n_decoders, stride, n_blocks, momentum=0.01):
+        super(Decoder, self).__init__()
+        self.layers = nn.ModuleList()
+        self.n_decoders = n_decoders
+        for i in range(self.n_decoders):
+            out_channels = in_channels // 2
+            self.layers.append(ResDecoderBlock(in_channels, out_channels, stride, n_blocks, momentum))
+            in_channels = out_channels
+
+    def forward(self, x, concat_tensors):
+        for i in range(self.n_decoders):
+            x = self.layers[i](x, concat_tensors[-1-i])
+        return x
+
+
+class TimbreFilter(nn.Module):
+    def __init__(self, latent_rep_channels):
+        super(TimbreFilter, self).__init__()
+        self.layers = nn.ModuleList()
+        for latent_rep in latent_rep_channels:
+            self.layers.append(ConvBlockRes(latent_rep[0], latent_rep[0]))
+
+    def forward(self, x_tensors):
+        out_tensors = []
+        for i, layer in enumerate(self.layers):
+            out_tensors.append(layer(x_tensors[i]))
+        return out_tensors
+
+
+class DeepUnet0(nn.Module):
+    def __init__(self, kernel_size, n_blocks, en_de_layers=5, inter_layers=4, in_channels=1, en_out_channels=16):
+        super(DeepUnet0, self).__init__()
+        self.encoder = Encoder(in_channels, N_MELS, en_de_layers, kernel_size, n_blocks, en_out_channels)
+        self.intermediate = Intermediate(self.encoder.out_channel // 2, self.encoder.out_channel, inter_layers, n_blocks)
+        self.tf = TimbreFilter(self.encoder.latent_channels)
+        self.decoder = Decoder(self.encoder.out_channel, en_de_layers, kernel_size, n_blocks)
+
+    def forward(self, x):
+        x, concat_tensors = self.encoder(x)
+        x = self.intermediate(x)
+        x = self.decoder(x, concat_tensors)
+        return x

preprocess/tools/note_transcription/modules/pe/rmvpe/extractor.py

@@ -0,0 +1,183 @@
+import math
+import os
+
+from tqdm import tqdm
+import librosa
+import numpy as np
+import torch
+from torch.utils.data import Dataset, DataLoader, DistributedSampler
+import torch.multiprocessing as mp
+from torch.distributed import init_process_group
+import torch.distributed as dist
+
+from .inference import RMVPE
+from ....utils.commons.dataset_utils import batch_by_size, build_dataloader
+# import utils
+from ....utils.audio import get_wav_num_frames
+
+"""
+A convenient API for batch inference
+update: add ddp
+"""
+
+class RMVPEInferDataset(Dataset):
+    def __init__(self, wav_fns: list, id_and_sizes=None, sr=24000, hop_size=128, num_workers=0):
+        if id_and_sizes is None:
+            id_and_sizes = []
+            if type(wav_fns[0]) == str:  # wav_paths
+                for idx, wav_path in enumerate(wav_fns):
+                    total_frames = get_wav_num_frames(wav_path, sr)
+                    id_and_sizes.append((idx, round(total_frames / hop_size)))
+            else:  # numpy arrays, mono wavs
+                for idx, wav in enumerate(wav_fns):
+                    id_and_sizes.append((idx, round(wav.shape[-1] / hop_size)))
+        self.wav_fns = wav_fns
+        self.id_and_sizes = id_and_sizes
+        self.sr = sr
+        self.num_workers = num_workers
+
+    def __getitem__(self, idx):
+        if type(self.wav_fns[idx]) == str:
+            wav_fn = self.wav_fns[idx]
+            wav, _ = librosa.core.load(wav_fn, sr=self.sr)
+        else:
+            wav = self.wav_fns[idx]
+        return idx, wav
+
+    def collater(self, samples: list):
+        return samples
+
+    def __len__(self):
+        return len(self.wav_fns)
+
+    def ordered_indices(self):
+        """Return an ordered list of indices. Batches will be constructed based
+        on this order."""
+        return np.arange(len(self))
+
+    def num_tokens(self, index):
+        return self.id_and_sizes[index][1]
+
+@torch.no_grad()
+def extract(wav_fns: list, id_and_sizes=None, ckpt=None, sr=24000, hop_size=128, bsz=128, max_tokens=100000,
+             fmax=900, fmin=50, ds_workers=0):
+    all_gpu_ids = [int(x) for x in os.environ.get("CUDA_VISIBLE_DEVICES", "").split(",") if x != '']
+    num_gpus = len(all_gpu_ids)
+    dist_config = {
+        "dist_backend": "nccl",
+        "dist_url": "tcp://localhost:54189",
+        "world_size": 1
+    }
+    # https://discuss.pytorch.org/t/how-to-fix-a-sigsegv-in-pytorch-when-using-distributed-training-e-g-ddp/113518/10#:~:text=Using%20start%20and%20join%20avoids
+    # https://github.com/pytorch/pytorch/issues/40403#issuecomment-648515174
+    # mp.set_start_method('spawn')
+    if num_gpus > 1:
+        result_queue = mp.Queue()
+        for rank in range(num_gpus):
+            mp.Process(target=extract_worker, args=(rank, wav_fns, id_and_sizes, ckpt, sr, hop_size, bsz, max_tokens, fmax,
+                                                    fmin, dist_config, num_gpus, ds_workers, result_queue,)).start()
+        f0_res = [None] * len(wav_fns)
+        for _ in range(num_gpus):
+            f0_res_dict = result_queue.get()
+            for idx in f0_res_dict:
+                f0_res[idx] = f0_res_dict[idx]
+            del f0_res_dict
+    else:
+        # f0_res = extract_one_process(wav_fns, id_and_sizes, ckpt, sr, hop_size, bsz, max_tokens, fmax, fmin)
+        f0_res_dict = extract_worker(0, wav_fns, id_and_sizes, ckpt, sr, hop_size, bsz, max_tokens, fmax,
+                                     fmin, dist_config, num_gpus, ds_workers, None)
+        f0_res = [None] * len(wav_fns)
+        for idx in f0_res_dict:
+            f0_res[idx] = f0_res_dict[idx]
+    return f0_res
+
+@torch.no_grad()
+def extract_worker(rank, wav_fns: list, id_and_sizes=None, ckpt=None, sr=24000, hop_size=128, bsz=128, max_tokens=100000,
+                   fmax=900, fmin=50, dist_config=None, num_gpus=1, ds_workers=0, q=None):
+    # print(f"rank: {rank}")
+    if num_gpus > 1:
+        init_process_group(backend=dist_config['dist_backend'], init_method=dist_config['dist_url'],
+                           world_size=dist_config['world_size'] * num_gpus, rank=rank)
+    dataset = RMVPEInferDataset(wav_fns, id_and_sizes, sr, hop_size, num_workers=ds_workers)
+    # ds_sampler = DistributedSampler(dataset, shuffle=False) if num_gpus > 1 else None
+    # loader = DataLoader(dataset, sampler=ds_sampler, collate_fn=dataset.collator, batch_size=1, num_workers=40, drop_last=False)
+    loader = build_dataloader(dataset, shuffle=False, max_tokens=max_tokens, max_sentences=bsz, use_ddp=num_gpus > 1)
+    loader = tqdm(loader, desc=f'| Processing f0 in [n_ranks={num_gpus}; max_tokens={max_tokens}; max_sentences={bsz}]') if rank == 0 else loader
+
+    device = torch.device(f"cuda:{int(rank)}")
+    model = RMVPE(ckpt, device=device)
+    f0_res_dict = {}
+    for batch in loader:
+        if batch is None or len(batch) == 0:
+            continue
+        idxs = [item[0] for item in batch]
+        wavs = [item[1] for item in batch]
+        lengths = [(wav.shape[0] + hop_size - 1) // hop_size for wav in wavs]
+        with torch.no_grad():
+            f0s, uvs = model.get_pitch_batch(
+                wavs, sample_rate=sr,
+                hop_size=hop_size,
+                lengths=lengths,
+                fmax=fmax,
+                fmin=fmin
+            )
+        for i, idx in enumerate(idxs):
+            f0_res_dict[idx] = f0s[i]
+    if q is not None:
+        q.put(f0_res_dict)
+    else:
+        return f0_res_dict
+
+# old version
+def extract_one_process(wav_fns: list, id_and_sizes=None, ckpt=None, sr=24000, hop_size=128, bsz=128, max_tokens=100000,
+             fmax=900, fmin=50, device='cuda'):
+    assert ckpt is not None
+    rmvpe = RMVPE(ckpt, device=device)
+    if id_and_sizes is None:
+        id_and_sizes = []
+        if type(wav_fns[0]) == str:    # wav_paths
+            for idx, wav_path in enumerate(wav_fns):
+                total_frames = get_wav_num_frames(wav_path, sr)
+                id_and_sizes.append((idx, round(total_frames / hop_size)))
+        else:                       # numpy arrays, mono wavs
+            for idx, wav in enumerate(wav_fns):
+                id_and_sizes.append((idx, round(wav.shape[-1] / hop_size)))
+    get_size = lambda x: x[1]
+    bs = batch_by_size(id_and_sizes, get_size, max_tokens=max_tokens, max_sentences=bsz)
+    for i in range(len(bs)):
+        bs[i] = [bs[i][j][0] for j in range(len(bs[i]))]
+
+    f0_res = [None] * len(wav_fns)
+    for batch in tqdm(bs, total=len(bs), desc=f'| Processing f0 in [max_tokens={max_tokens}; max_sentences={bsz}]'):
+        wavs, mel_lengths, lengths = [], [], []
+        for idx in batch:
+            if type(wav_fns[idx]) == str:
+                wav_fn = wav_fns[idx]
+                wav, _ = librosa.core.load(wav_fn, sr=sr)
+            else:
+                wav = wav_fns[idx]
+            wavs.append(wav)
+            mel_lengths.append(math.ceil((wav.shape[0] + 1) / hop_size))
+            lengths.append((wav.shape[0] + hop_size - 1) // hop_size)
+
+        with torch.no_grad():
+            f0s, uvs = rmvpe.get_pitch_batch(
+                wavs, sample_rate=sr,
+                hop_size=hop_size,
+                lengths=lengths,
+                fmax=fmax,
+                fmin=fmin
+            )
+
+        for i, idx in enumerate(batch):
+            f0_res[idx] = f0s[i]
+
+    if rmvpe is not None:
+        rmvpe.release_cuda()
+        torch.cuda.empty_cache()
+        rmvpe = None
+
+    return f0_res
+
+
+

preprocess/tools/note_transcription/modules/pe/rmvpe/inference.py

@@ -0,0 +1,134 @@
+import math
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torchaudio.transforms import Resample
+import pyworld as pw
+
+from ....utils.audio.pitch_utils import interp_f0, resample_align_curve
+from .constants import *
+from .model import E2E0
+from .spec import MelSpectrogram
+from .utils import to_local_average_f0, to_viterbi_f0
+
+
+class RMVPE:
+    def __init__(self, model_path, hop_length=160, device=None):
+        self.resample_kernel = {}
+        if device is None:
+            self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        else:
+            self.device = device
+        self.model = E2E0(4, 1, (2, 2)).eval().to(self.device)
+        ckpt = torch.load(model_path, map_location=self.device)
+        self.model.load_state_dict(ckpt['model'], strict=False)
+        self.mel_extractor = MelSpectrogram(
+            N_MELS, SAMPLE_RATE, WINDOW_LENGTH, hop_length, None, MEL_FMIN, MEL_FMAX
+        ).to(self.device)
+        self.hop_length = hop_length
+
+    @torch.no_grad()
+    def mel2hidden(self, mel):
+        n_frames = mel.shape[-1]
+        mel = F.pad(mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode='constant')
+        hidden = self.model(mel)
+        return hidden[:, :n_frames]
+
+    def decode(self, hidden, thred=0.03, use_viterbi=False):
+        if use_viterbi:
+            f0 = to_viterbi_f0(hidden, thred=thred)
+        else:
+            f0 = to_local_average_f0(hidden, thred=thred)
+        return f0
+
+    def postprocess(self, f0, fmin=50, fmax=1000, audio=None, min_gap=2):
+        if audio is not None:
+            # this doesn't work. deprecated
+            t = np.arange(0, f0.shape[0] * self.hop_length / 16000, self.hop_length / 16000)
+            f0 = pw.stonemask(audio.astype(np.float64), f0.astype(np.float64), t, 16000).astype(float)
+        f0[f0 < fmin] = 0
+        f0[f0 > fmax] = 0
+        # eliminate glitch
+        # min_gap: if successive positive f0 positions < min_gap, zero these positions
+        # eg: if min_gap=2, [0, 500, 500, 0] => [0, 0, 0, 0]
+        for idx in range(f0.shape[0] - min_gap - 1):
+            if f0[idx] == 0 and f0[idx + min_gap + 1] == 0 and np.sum(f0[idx: idx + min_gap + 2]) > 0:
+                f0[idx: idx + min_gap + 2] = 0
+        return f0
+
+    def infer_from_audio(self, audio, sample_rate=16000, thred=0.03, use_viterbi=False):
+        audio = torch.from_numpy(audio).float().unsqueeze(0).to(self.device)
+        if sample_rate == 16000:
+            audio_res = audio
+        else:
+            key_str = str(sample_rate)
+            if key_str not in self.resample_kernel:
+                self.resample_kernel[key_str] = Resample(sample_rate, 16000, lowpass_filter_width=128)
+            self.resample_kernel[key_str] = self.resample_kernel[key_str].to(self.device)
+            audio_res = self.resample_kernel[key_str](audio)
+        mel = self.mel_extractor(audio_res, center=True)
+        hidden = self.mel2hidden(mel)
+        f0 = self.decode(hidden, thred=thred, use_viterbi=use_viterbi).squeeze(0)
+        return f0
+
+    def get_pitch(self, waveform, sample_rate, hop_size, length, interp_uv=False, fmin=50, fmax=1000):
+        f0 = self.infer_from_audio(waveform, sample_rate=sample_rate)
+        f0 = self.postprocess(f0, fmin, fmax)
+        uv = f0 == 0
+        time_step = hop_size / sample_rate
+        f0_res = resample_align_curve(f0, 0.01, time_step, length)
+        uv_res = resample_align_curve(uv.astype(np.float32), 0.01, time_step, length) > 0.5
+        if not interp_uv:
+            f0_res[uv_res] = 0
+        return f0_res, uv_res
+
+    def infer_from_audio_batch(self, audios, sample_rate=16000, thred=0.03, use_viterbi=False):
+        from ....utils.commons.dataset_utils import collate_1d_or_2d
+        if isinstance(audios, list):
+            audios = [torch.from_numpy(audio).float() for audio in audios]
+            sizes = [math.ceil((audio.shape[0] + 1) / self.hop_length) for audio in audios]
+            audios = collate_1d_or_2d(audios, 0.0).to(self.device)
+        elif isinstance(audios, torch.Tensor):
+            sizes = None
+            if audios.device != self.device:
+                audios = audios.to(self.device)
+        else:
+            raise NotImplementedError
+        if sample_rate == 16000:
+            audios_res = audios
+        else:
+            key_str = str(sample_rate)
+            if key_str not in self.resample_kernel:
+                self.resample_kernel[key_str] = Resample(sample_rate, 16000, lowpass_filter_width=128)
+            self.resample_kernel[key_str] = self.resample_kernel[key_str].to(self.device)
+            audios_res = self.resample_kernel[key_str](audios)
+        mels = self.mel_extractor(audios_res, center=True)
+        hiddens = self.mel2hidden(mels)
+        f0 = self.decode(hiddens, thred=thred, use_viterbi=use_viterbi)
+        f0s = []
+        for i in range(f0.shape[0]):
+            f = f0[i, :sizes[i]] if sizes is not None else f0[i, :]
+            f0s.append(f)
+        return f0s
+
+    def get_pitch_batch(self, waveforms, sample_rate, hop_size, lengths, interp_uv=False, fmin=50, fmax=1000):
+        # hop_size, sample_rate: tgt params
+        f0s = self.infer_from_audio_batch(waveforms, sample_rate=sample_rate)
+        f0s_res, uvs_res = [], []
+        for idx, f0 in enumerate(f0s):
+            f0 = self.postprocess(f0, fmin, fmax, min_gap=6)
+            uv = f0 == 0
+            length = lengths[idx]
+            time_step = hop_size / sample_rate
+            f0_res = resample_align_curve(f0, 0.01, time_step, length)
+            uv_res = resample_align_curve(uv.astype(np.float32), 0.01, time_step, length) > 0.5
+            if not interp_uv:
+                f0_res[uv_res] = 0
+            f0s_res.append(f0_res)
+            uvs_res.append(uv_res)
+        return f0s_res, uvs_res
+
+    def release_cuda(self):
+        self.model = self.model.cpu()
+        self.mel_extractor = self.mel_extractor.cpu()

preprocess/tools/note_transcription/modules/pe/rmvpe/model.py

@@ -0,0 +1,32 @@
+from torch import nn
+
+from .constants import *
+from .deepunet import DeepUnet0
+from .seq import BiGRU
+
+
+class E2E0(nn.Module):
+    def __init__(self, n_blocks, n_gru, kernel_size, en_de_layers=5, inter_layers=4, in_channels=1,
+                 en_out_channels=16):
+        super(E2E0, self).__init__()
+        self.unet = DeepUnet0(kernel_size, n_blocks, en_de_layers, inter_layers, in_channels, en_out_channels)
+        self.cnn = nn.Conv2d(en_out_channels, 3, (3, 3), padding=(1, 1))
+        if n_gru:
+            self.fc = nn.Sequential(
+                BiGRU(3 * N_MELS, 256, n_gru),
+                nn.Linear(512, N_CLASS),
+                nn.Dropout(0.25),
+                nn.Sigmoid()
+            )
+        else:
+            self.fc = nn.Sequential(
+                nn.Linear(3 * N_MELS, N_CLASS),
+                nn.Dropout(0.25),
+                nn.Sigmoid()
+            )
+
+    def forward(self, mel):
+        mel = mel.transpose(-1, -2).unsqueeze(1)
+        x = self.cnn(self.unet(mel)).transpose(1, 2).flatten(-2)
+        x = self.fc(x)
+        return x

preprocess/tools/note_transcription/modules/pe/rmvpe/seq.py

@@ -0,0 +1,10 @@
+import torch.nn as nn
+
+
+class BiGRU(nn.Module):
+    def __init__(self, input_features, hidden_features, num_layers):
+        super(BiGRU, self).__init__()
+        self.gru = nn.GRU(input_features, hidden_features, num_layers=num_layers, batch_first=True, bidirectional=True)
+
+    def forward(self, x):
+        return self.gru(x)[0]

preprocess/tools/note_transcription/modules/pe/rmvpe/spec.py

@@ -0,0 +1,72 @@
+import torch
+import numpy as np
+import torch.nn.functional as F
+from librosa.filters import mel
+
+
+class MelSpectrogram(torch.nn.Module):
+    def __init__(
+            self,
+            n_mel_channels,
+            sampling_rate,
+            win_length,
+            hop_length,
+            n_fft=None,
+            mel_fmin=0,
+            mel_fmax=None,
+            clamp=1e-5
+    ):
+        super().__init__()
+        n_fft = win_length if n_fft is None else n_fft
+        self.hann_window = {}
+        mel_basis = mel(
+            sr=sampling_rate,
+            n_fft=n_fft,
+            n_mels=n_mel_channels,
+            fmin=mel_fmin,
+            fmax=mel_fmax,
+            htk=True)
+        mel_basis = torch.from_numpy(mel_basis).float()
+        self.register_buffer("mel_basis", mel_basis)
+        self.n_fft = win_length if n_fft is None else n_fft
+        self.hop_length = hop_length
+        self.win_length = win_length
+        self.sampling_rate = sampling_rate
+        self.n_mel_channels = n_mel_channels
+        self.clamp = clamp
+
+    def forward(self, audio, keyshift=0, speed=1, center=True):
+        factor = 2 ** (keyshift / 12)
+        n_fft_new = int(np.round(self.n_fft * factor))
+        win_length_new = int(np.round(self.win_length * factor))
+        hop_length_new = int(np.round(self.hop_length * speed))
+
+        keyshift_key = str(keyshift) + '_' + str(audio.device)
+        if keyshift_key not in self.hann_window:
+            self.hann_window[keyshift_key] = torch.hann_window(win_length_new).to(audio.device)
+        if center:
+            pad_left = win_length_new // 2
+            pad_right = (win_length_new + 1) // 2
+            audio = F.pad(audio, (pad_left, pad_right))
+
+        fft = torch.stft(
+            audio,
+            n_fft=n_fft_new,
+            hop_length=hop_length_new,
+            win_length=win_length_new,
+            window=self.hann_window[keyshift_key],
+            center=False,
+            return_complex=True
+        )
+        magnitude = fft.abs()
+
+        if keyshift != 0:
+            size = self.n_fft // 2 + 1
+            resize = magnitude.size(1)
+            if resize < size:
+                magnitude = F.pad(magnitude, (0, 0, 0, size - resize))
+            magnitude = magnitude[:, :size, :] * self.win_length / win_length_new
+
+        mel_output = torch.matmul(self.mel_basis, magnitude)
+        log_mel_spec = torch.log(torch.clamp(mel_output, min=self.clamp))
+        return log_mel_spec

preprocess/tools/note_transcription/modules/pe/rmvpe/utils.py

@@ -0,0 +1,43 @@
+import librosa
+import numpy as np
+import torch
+
+from .constants import *
+
+
+def to_local_average_f0(hidden, center=None, thred=0.03):
+    idx = torch.arange(N_CLASS, device=hidden.device)[None, None, :]  # [B=1, T=1, N]
+    idx_cents = idx * 20 + CONST  # [B=1, N]
+    if center is None:
+        center = torch.argmax(hidden, dim=2, keepdim=True)  # [B, T, 1]
+    start = torch.clip(center - 4, min=0)  # [B, T, 1]
+    end = torch.clip(center + 5, max=N_CLASS)  # [B, T, 1]
+    idx_mask = (idx >= start) & (idx < end)  # [B, T, N]
+    weights = hidden * idx_mask  # [B, T, N]
+    product_sum = torch.sum(weights * idx_cents, dim=2)  # [B, T]
+    weight_sum = torch.sum(weights, dim=2)  # [B, T]
+    cents = product_sum / (weight_sum + (weight_sum == 0))  # avoid dividing by zero, [B, T]
+    f0 = 10 * 2 ** (cents / 1200)
+    uv = hidden.max(dim=2)[0] < thred  # [B, T]
+    f0 = f0 * ~uv
+    return f0.cpu().numpy()
+
+
+def to_viterbi_f0(hidden, thred=0.03):
+    # Create viterbi transition matrix
+    if not hasattr(to_viterbi_f0, 'transition'):
+        xx, yy = np.meshgrid(range(N_CLASS), range(N_CLASS))
+        transition = np.maximum(30 - abs(xx - yy), 0)
+        transition = transition / transition.sum(axis=1, keepdims=True)
+        to_viterbi_f0.transition = transition
+
+    # Convert to probability
+    prob = hidden.squeeze(0).cpu().numpy()
+    prob = prob.T
+    prob = prob / prob.sum(axis=0)
+
+    # Perform viterbi decoding
+    path = librosa.sequence.viterbi(prob, to_viterbi_f0.transition).astype(np.int64)
+    center = torch.from_numpy(path).unsqueeze(0).unsqueeze(-1).to(hidden.device)
+
+    return to_local_average_f0(hidden, center=center, thred=thred)

preprocess/tools/note_transcription/modules/rosvot/__init__.py

@@ -0,0 +1 @@
+"""Core ROSVOT model components."""

preprocess/tools/note_transcription/modules/rosvot/rosvot.py

@@ -0,0 +1,295 @@
+from copy import deepcopy
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+from ...utils.commons.hparams import hparams
+from ...utils.commons.gpu_mem_track import MemTracker
+from ..commons.layers import Embedding
+from ..commons.conv import ResidualBlock, ConvBlocks
+from ..commons.conformer.conformer import ConformerLayers
+from .unet import Unet
+
+def regulate_boundary(bd_logits, threshold, min_gap=18, ref_bd=None, ref_bd_min_gap=8, non_padding=None):
+    # this doesn't preserve gradient
+    device = bd_logits.device
+    bd_logits = torch.sigmoid(bd_logits).data.cpu()
+    # bd_logits[0] = bd_logits[-1] = 1e-5     # avoid itv invalid problem
+    bd = (bd_logits > threshold).long()
+    bd_res = torch.zeros_like(bd).long()
+    for i in range(bd.shape[0]):
+        bd_i = bd[i]
+        last_bd_idx = -1
+        start = -1
+        for j in range(bd_i.shape[0]):
+            if bd_i[j] == 1:
+                if 0 <= start < j:
+                    continue
+                elif start < 0:
+                    start = j
+            else:
+                if 0 <= start < j:
+                    if j - 1 > start:
+                        bd_idx = start + int(torch.argmax(bd_logits[i, start: j]).item())
+                    else:
+                        bd_idx = start
+                    if bd_idx - last_bd_idx < min_gap and last_bd_idx > 0:
+                        bd_idx = round((bd_idx + last_bd_idx) / 2)
+                        bd_res[i, last_bd_idx] = 0
+                    bd_res[i, bd_idx] = 1
+                    last_bd_idx = bd_idx
+                    start = -1
+
+    # assert ref_bd_min_gap <= min_gap // 2
+    if ref_bd is not None and ref_bd_min_gap > 0:
+        ref = ref_bd.data.cpu()
+        for i in range(bd_res.shape[0]):
+            ref_bd_i = ref[i]
+            ref_bd_i_js = []
+            for j in range(ref_bd_i.shape[0]):
+                if ref_bd_i[j] == 1:
+                    ref_bd_i_js.append(j)
+                    seg_sum = torch.sum(bd_res[i, max(0, j - ref_bd_min_gap): j + ref_bd_min_gap])
+                    if seg_sum == 0:
+                        bd_res[i, j] = 1
+                    elif seg_sum == 1 and bd_res[i, j] != 1:
+                        bd_res[i, max(0, j - ref_bd_min_gap): j + ref_bd_min_gap] = \
+                            ref_bd_i[max(0, j - ref_bd_min_gap): j + ref_bd_min_gap]
+                    elif seg_sum > 1:
+                        for k in range(1, ref_bd_min_gap+1):
+                            if bd_res[i, max(0, j - k)] == 1 and ref_bd_i[max(0, j - k)] != 1:
+                                bd_res[i, max(0, j - k)] = 0
+                                break
+                            if bd_res[i, min(bd_res.shape[1] - 1, j + k)] == 1 and ref_bd_i[min(bd_res.shape[1] - 1, j + k)] != 1:
+                                bd_res[i, min(bd_res.shape[1] - 1, j + k)] = 0
+                                break
+                        bd_res[i, j] = 1
+            # final check
+            assert torch.sum(bd_res[i, ref_bd_i_js]) == len(ref_bd_i_js), \
+                f"{torch.sum(bd_res[i, ref_bd_i_js])} {len(ref_bd_i_js)}"
+
+    bd_res = bd_res.to(device)
+
+    # force valid begin and end
+    bd_res[:, 0] = 0
+    if non_padding is not None:
+        for i in range(bd_res.shape[0]):
+            bd_res[i, sum(non_padding[i]) - 1:] = 0
+    else:
+        bd_res[:, -1] = 0
+
+    return bd_res
+
+class BackboneNet(nn.Module):
+    def __init__(self, hparams):
+        super().__init__()
+        self.hidden_size = hidden_size = hparams['hidden_size']
+        self.dropout = hparams.get('dropout', 0.0)
+        updown_rates = [2, 2, 2]
+        channel_multiples = [1, 1, 1]
+        if hparams.get('updown_rates', None) is not None:
+            updown_rates = [int(i) for i in hparams.get('updown_rates', None).split('-')]
+        if hparams.get('channel_multiples', None) is not None:
+            channel_multiples = [float(i) for i in hparams.get('channel_multiples', None).split('-')]
+        assert len(updown_rates) == len(channel_multiples)
+        # convs
+        if hparams.get('bkb_net', 'conv') == 'conv':
+            self.net = Unet(hidden_size, down_layers=len(updown_rates), mid_layers=hparams.get('bkb_layers', 12),
+                            up_layers=len(updown_rates), kernel_size=3, updown_rates=updown_rates,
+                            channel_multiples=channel_multiples, dropout=0, is_BTC=True,
+                            constant_channels=False, mid_net=None, use_skip_layer=hparams.get('unet_skip_layer', False))
+        # conformer
+        elif hparams.get('bkb_net', 'conv') == 'conformer':
+            mid_net = ConformerLayers(
+                hidden_size, num_layers=hparams.get('bkb_layers', 12), kernel_size=hparams.get('conformer_kernel', 9),
+                dropout=self.dropout, num_heads=4)
+            self.net = Unet(hidden_size, down_layers=len(updown_rates), up_layers=len(updown_rates), kernel_size=3,
+                            updown_rates=updown_rates, channel_multiples=channel_multiples, dropout=0,
+                            is_BTC=True, constant_channels=False, mid_net=mid_net,
+                            use_skip_layer=hparams.get('unet_skip_layer', False))
+
+    def forward(self, x):
+        return self.net(x)
+
+class PitchDecoder(nn.Module):
+    def __init__(self, hparams):
+        super().__init__()
+        self.hidden_size = hidden_size = hparams['hidden_size']
+        self.dropout = hparams.get('dropout', 0.0)
+        self.note_bd_out = nn.Linear(hidden_size, 1)
+        self.note_bd_temperature = max(1e-7, hparams.get('note_bd_temperature', 1.0))
+
+        # note prediction
+        self.pitch_attn_num_head = hparams.get('pitch_attn_num_head', 1)
+        self.multihead_dot_attn = nn.Linear(hidden_size, self.pitch_attn_num_head)
+        self.post = ConvBlocks(hidden_size, out_dims=hidden_size, dilations=None, kernel_size=3,
+                               layers_in_block=1, c_multiple=1, dropout=self.dropout, num_layers=1,
+                               post_net_kernel=3, act_type='leakyrelu')
+        self.pitch_out = nn.Linear(hidden_size, hparams.get('note_num', 100) + 4)
+        self.note_num = hparams.get('note_num', 100)
+        self.note_start = hparams.get('note_start', 30)
+        self.pitch_temperature = max(1e-7, hparams.get('note_pitch_temperature', 1.0))
+
+    def forward(self, feat, note_bd, train=True):
+        bsz, T, _ = feat.shape
+
+        attn = torch.sigmoid(self.multihead_dot_attn(feat))  # [B, T, C] -> [B, T, num_head]
+        attn = F.dropout(attn, self.dropout, train)
+        attn_feat = feat.unsqueeze(3) * attn.unsqueeze(2)  # [B, T, C, 1] x [B, T, 1, num_head] -> [B, T, C, num_head]
+        attn_feat = torch.mean(attn_feat, dim=-1)  # [B, T, C, num_head] -> [B, T, C]
+        mel2note = torch.cumsum(note_bd, 1)
+        note_length = torch.max(torch.sum(note_bd, dim=1)).item() + 1  # max length
+        note_lengths = torch.sum(note_bd, dim=1) + 1  # [B]
+        # print('note_length', note_length)
+
+        attn = torch.mean(attn, dim=-1, keepdim=True)  # [B, T, num_head] -> [B, T, 1]
+        denom = mel2note.new_zeros(bsz, note_length, dtype=attn.dtype).scatter_add_(
+            dim=1, index=mel2note, src=attn.squeeze(-1)
+        )  # [B, T] -> [B, note_length] count the note frames of each note (with padding excluded)
+        frame2note = mel2note.unsqueeze(-1).repeat(1, 1, self.hidden_size)  # [B, T] -> [B, T, C], with padding included
+        note_aggregate = frame2note.new_zeros(bsz, note_length, self.hidden_size, dtype=attn_feat.dtype).scatter_add_(
+            dim=1, index=frame2note, src=attn_feat
+        )  # [B, T, C] -> [B, note_length, C]
+        note_aggregate = note_aggregate / (denom.unsqueeze(-1) + 1e-5)
+        note_aggregate = F.dropout(note_aggregate, self.dropout, train)
+        note_logits = self.post(note_aggregate)
+        note_logits = self.pitch_out(note_logits) / self.pitch_temperature
+        # note_logits = torch.clamp(note_logits, min=-16., max=16.)     # don't know need it or not
+
+        note_pred = torch.softmax(note_logits, dim=-1)  # [B, note_length, note_num]
+        note_pred = torch.argmax(note_pred, dim=-1)  # [B, note_length]
+        # for some reason, note idx maybe 130 (why?)
+        note_pred[note_pred > self.note_num] = 0
+        note_pred[note_pred < self.note_start] = 0
+
+        return note_lengths, note_logits, note_pred
+
+class MidiExtractor(nn.Module):
+    def __init__(self, hparams):
+        super(MidiExtractor, self).__init__()
+        self.hparams = deepcopy(hparams)
+        self.hidden_size = hidden_size = hparams['hidden_size']
+        self.dropout = hparams.get('dropout', 0.0)
+        self.note_bd_threshold = hparams.get('note_bd_threshold', 0.5)
+        self.note_bd_min_gap = round(hparams.get('note_bd_min_gap', 100) * hparams['audio_sample_rate'] / 1000 / hparams['hop_size'])
+        self.note_bd_ref_min_gap = round(hparams.get('note_bd_ref_min_gap', 50) * hparams['audio_sample_rate'] / 1000 / hparams['hop_size'])
+
+        self.mel_proj = nn.Conv1d(hparams['use_mel_bins'], hidden_size, kernel_size=3, padding=1)
+        self.mel_encoder = ConvBlocks(hidden_size, out_dims=hidden_size, dilations=None, kernel_size=3,
+                                      layers_in_block=2, c_multiple=1, dropout=self.dropout, num_layers=1,
+                                      post_net_kernel=3, act_type='leakyrelu')
+        self.use_pitch = hparams.get('use_pitch_embed', True)
+        if self.use_pitch:
+            self.pitch_embed = Embedding(300, hidden_size, 0, 'kaiming')
+            self.uv_embed = Embedding(3, hidden_size, 0, 'kaiming')
+        self.use_wbd = hparams.get('use_wbd', True)
+        if self.use_wbd:
+            self.word_bd_embed = Embedding(3, hidden_size, 0, 'kaiming')
+        self.cond_encoder = ConvBlocks(hidden_size, out_dims=hidden_size, dilations=None, kernel_size=3,
+                                       layers_in_block=1, c_multiple=1, dropout=self.dropout, num_layers=1,
+                                       post_net_kernel=3, act_type='leakyrelu')
+
+        # backbone
+        self.net = BackboneNet(hparams)
+
+        # note bd prediction
+        self.note_bd_out = nn.Linear(hidden_size, 1)
+        self.note_bd_temperature = max(1e-7, hparams.get('note_bd_temperature', 1.0))
+
+        # note prediction
+        self.pitch_decoder = PitchDecoder(hparams)
+
+        self.reset_parameters()
+
+    def run_encoder(self, mel=None, word_bd=None, pitch=None, uv=None, non_padding=None):
+        mel_embed = self.mel_proj(mel.transpose(1, 2)).transpose(1, 2)
+        mel_embed = self.mel_encoder(mel_embed)
+        pitch_embed = word_bd_embed = 0
+        if self.use_pitch and pitch is not None and uv is not None:
+            pitch_embed = self.pitch_embed(pitch) + self.uv_embed(uv)  # [B, T, C]
+        if self.use_wbd and word_bd is not None:
+            word_bd_embed = self.word_bd_embed(word_bd)
+        feat = self.cond_encoder(mel_embed + pitch_embed + word_bd_embed)
+
+        return feat
+
+    def forward(self, mel=None, word_bd=None, note_bd=None, pitch=None, uv=None, non_padding=None, train=True):
+        ret = {}
+        bsz, T, _ = mel.shape
+
+        feat = self.run_encoder(mel, word_bd, pitch, uv, non_padding)
+        feat = self.net(feat)   # [B, T, C]
+
+        # note bd prediction
+        note_bd_logits = self.note_bd_out(F.dropout(feat, self.dropout, train)).squeeze(-1) / self.note_bd_temperature
+        note_bd_logits = torch.clamp(note_bd_logits, min=-16., max=16.)
+        ret['note_bd_logits'] = note_bd_logits  # [B, T]
+        if note_bd is None or not train:
+            note_bd = regulate_boundary(note_bd_logits, self.note_bd_threshold, self.note_bd_min_gap,
+                                        word_bd, self.note_bd_ref_min_gap, non_padding)
+            ret['note_bd_pred'] = note_bd   # [B, T]
+
+        # note pitch prediction
+        note_lengths, note_logits, note_pred = self.pitch_decoder(feat, note_bd, train)
+        ret['note_lengths'], ret['note_logits'], ret['note_pred'] = note_lengths, note_logits, note_pred
+
+        return ret
+
+    def reset_parameters(self):
+        nn.init.kaiming_normal_(self.pitch_decoder.multihead_dot_attn.weight, mode='fan_in')
+        nn.init.kaiming_normal_(self.note_bd_out.weight, mode='fan_in')
+        nn.init.kaiming_normal_(self.pitch_decoder.pitch_out.weight, mode='fan_in')
+        nn.init.kaiming_normal_(self.mel_proj.weight, mode='fan_in')
+        nn.init.constant_(self.pitch_decoder.multihead_dot_attn.bias, 0.0)
+        nn.init.constant_(self.note_bd_out.bias, 0.0)
+        nn.init.constant_(self.pitch_decoder.pitch_out.bias, 0.0)
+
+
+class WordbdExtractor(MidiExtractor):
+    def __init__(self, hparams):
+        super().__init__(hparams)
+        self.use_wbd = False
+        self.word_bd_embed = None
+        self.note_bd_out = self.note_bd_temperature = self.pitch_decoder = None
+
+        self.word_bd_threshold = hparams.get('word_bd_threshold', 0.5)
+        self.word_bd_min_gap = round(
+            hparams.get('word_bd_min_gap', 100) * hparams['audio_sample_rate'] / 1000 / hparams['hop_size'])
+
+        self.word_bd_out = nn.Linear(self.hidden_size, 1)
+        self.word_bd_temperature = max(1e-7, hparams.get('word_bd_temperature', 1.0))
+        nn.init.kaiming_normal_(self.word_bd_out.weight, mode='fan_in')
+        nn.init.constant_(self.word_bd_out.bias, 0.0)
+
+    def forward(self, mel=None, pitch=None, uv=None, non_padding=None, train=True):
+        # gpu_tracker.track()
+        ret = {}
+        bsz, T, _ = mel.shape
+
+        feat = self.run_encoder(mel=mel, pitch=pitch, uv=uv, non_padding=non_padding)
+        feat = self.net(feat)  # [B, T, C]
+
+        word_bd_logits = self.word_bd_out(F.dropout(feat, self.dropout, train)).squeeze(-1) / self.word_bd_temperature
+        word_bd_logits = torch.clamp(word_bd_logits, min=-16., max=16.)
+        ret['word_bd_logits'] = word_bd_logits  # [B, T]
+
+        if not train:
+            word_bd = regulate_boundary(word_bd_logits, self.word_bd_threshold, self.word_bd_min_gap,
+                                        non_padding=non_padding)
+            ret['word_bd_pred'] = word_bd   # [B, T]
+
+        return ret
+
+    def reset_parameters(self):
+        if self.use_pitch:
+            nn.init.kaiming_normal_(self.pitch_embed.weight, mode='fan_in')
+            nn.init.kaiming_normal_(self.uv_embed.weight, mode='fan_in')
+        nn.init.kaiming_normal_(self.mel_proj.weight, mode='fan_in')
+        if self.use_pitch:
+            nn.init.constant_(self.pitch_embed.weight[self.pitch_embed.padding_idx], 0.0)
+            nn.init.constant_(self.uv_embed.weight[self.uv_embed.padding_idx], 0.0)
+
+

preprocess/tools/note_transcription/modules/rosvot/unet.py

@@ -0,0 +1,172 @@
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..commons.layers import LayerNorm, Embedding
+from ..commons.conv import ConvBlocks, ResidualBlock, get_norm_builder, get_act_builder
+
+class UnetDown(nn.Module):
+    def __init__(self, hidden_size, n_layers, kernel_size, down_rates, channel_multiples=None, dropout=0.0,
+                 is_BTC=True, constant_channels=False):
+        super(UnetDown, self).__init__()
+        assert n_layers == len(down_rates)   # downs, down sample rate
+        down_rates = [int(i) for i in down_rates]
+        self.n_layers = n_layers
+        self.hidden_size = hidden_size
+        self.is_BTC = is_BTC
+        channel_multiples = channel_multiples if channel_multiples is not None else down_rates
+        self.layers = nn.ModuleList()
+        self.downs = nn.ModuleList()
+        in_channels = hidden_size
+        for i in range(self.n_layers):
+            out_channels = int(in_channels * channel_multiples[i]) if not constant_channels else in_channels
+            self.layers.append(nn.Sequential(
+                ResidualBlock(in_channels, kernel_size, dilation=1, n=1, norm_type='ln', dropout=dropout,
+                              c_multiple=1, ln_eps=1e-5, act_type='leakyrelu'),
+                nn.Conv1d(in_channels, out_channels, kernel_size, padding=(kernel_size - 1) // 2),
+                ResidualBlock(out_channels, kernel_size, dilation=1, n=1, norm_type='ln',
+                              dropout=dropout, c_multiple=1, ln_eps=1e-5, act_type='leakyrelu')
+            ))
+            self.downs.append(nn.Sequential(
+                nn.AvgPool1d(down_rates[i])
+            ))
+            in_channels = out_channels
+        self.last_norm = get_norm_builder('ln', out_channels)()
+        self.post_net = nn.Conv1d(out_channels, out_channels, kernel_size=kernel_size,
+                                   padding=kernel_size // 2)
+
+    def forward(self, x, **kwargs):
+        # x [B, T, C]
+        if self.is_BTC:
+            x = x.transpose(1, 2)   # [B, C, T]
+        skip_xs = []
+        for i in range(self.n_layers):
+            skip_x = self.layers[i](x)
+            x = self.downs[i](skip_x)
+            if self.is_BTC:
+                skip_xs.append(skip_x.transpose(1, 2))  # [B, T, C]
+            else:
+                skip_xs.append(skip_x)
+        x = self.post_net(self.last_norm(x))
+        if self.is_BTC:
+            x = x.transpose(1, 2)
+        return x, skip_xs
+
+class UnetMid(nn.Module):
+    def __init__(self, hidden_size, kernel_size, n_layers=None, in_dims=None, out_dims=None,
+                 dropout=0.0, is_BTC=True, net=None):
+        super(UnetMid, self).__init__()
+        in_dims = in_dims if in_dims is not None else hidden_size
+        out_dims = out_dims if out_dims is not None else hidden_size
+        self.pre = nn.Conv1d(in_dims, hidden_size, kernel_size, padding=kernel_size // 2)
+        self.post = nn.Conv1d(hidden_size, out_dims, kernel_size, padding=kernel_size // 2)
+        self.is_BTC = is_BTC
+        if net is not None:
+            self.net = net
+        else:
+            self.net = ConvBlocks(hidden_size, out_dims=hidden_size, dilations=None, kernel_size=kernel_size,
+                                  layers_in_block=2, c_multiple=2, dropout=dropout, num_layers=n_layers,
+                                  post_net_kernel=3, act_type='leakyrelu', is_BTC=is_BTC)
+
+    def forward(self, x, cond=None, **kwargs):
+        # x [B, T, C]
+        if self.is_BTC:
+            x = self.pre(x.transpose(1, 2)).transpose(1, 2)
+        else:
+            x = self.pre(x)
+        if cond is None:
+            cond = 0
+        x = self.net(x + cond)
+        if self.is_BTC:
+            x = self.post(x.transpose(1, 2)).transpose(1, 2)
+        else:
+            x = self.post(x)
+        return x
+
+class UnetUp(nn.Module):
+    def __init__(self, hidden_size, n_layers, kernel_size, up_rates, channel_multiples=None, dropout=0.0,
+                 is_BTC=True, constant_channels=False, use_skip_layer=False, skip_scale=1.0):
+        super(UnetUp, self).__init__()
+        assert n_layers == len(up_rates)  # this is reversed in up module, from the output to the interface with middle
+        up_rates = [int(i) for i in up_rates]
+        self.n_layers = n_layers
+        self.hidden_size = hidden_size
+        self.is_BTC = is_BTC
+        self.skip_scale = skip_scale
+        channel_multiples = channel_multiples if channel_multiples is not None else up_rates
+        # in_channels = int(np.cumprod(channel_multiples)[-1] * hidden_size) if not constant_channels else hidden_size
+        self.in_channels_lst = (np.cumprod([1] + channel_multiples) * hidden_size).astype(int) if not constant_channels \
+            else [hidden_size for _ in range(self.n_layers + 1)]
+        in_channels = self.in_channels_lst[-1]
+        self.ups = nn.ModuleList()
+        self.skip_layers = nn.ModuleList()
+        self.layers = nn.ModuleList()
+        for i in range(self.n_layers-1, -1, -1):
+            out_channels = self.in_channels_lst[i] if not constant_channels else in_channels
+            self.ups.append(nn.Sequential(
+                nn.ConvTranspose1d(in_channels, in_channels, kernel_size=kernel_size, stride=up_rates[i],
+                                   padding=kernel_size//2, output_padding=up_rates[i]-1),
+                get_norm_builder('ln', in_channels)(),
+                get_act_builder('leakyrelu')()
+            ))
+            self.layers.append(nn.Sequential(
+                # ResidualBlock(in_channels*2, kernel_size, dilation=1, n=1, norm_type='ln', dropout=dropout,
+                #               c_multiple=1, ln_eps=1e-5, act_type='leakyrelu'),
+                nn.Conv1d(in_channels*2, out_channels, kernel_size, padding=(kernel_size - 1) // 2),
+                ResidualBlock(out_channels, kernel_size, dilation=1, n=1, norm_type='ln',
+                              dropout=dropout, c_multiple=1, ln_eps=1e-5, act_type='leakyrelu')
+            ))
+            if use_skip_layer:
+                self.skip_layers.append(
+                    ResidualBlock(in_channels, kernel_size, dilation=1, n=1, norm_type='ln', dropout=dropout,
+                                  c_multiple=1, ln_eps=1e-5, act_type='leakyrelu')
+                )
+            else:
+                self.skip_layers.append(nn.Identity())
+
+            in_channels = out_channels
+        self.out_channels = out_channels
+        self.last_norm = get_norm_builder('ln', out_channels)()
+        self.post_net = nn.Conv1d(out_channels, out_channels, kernel_size=kernel_size,
+                                  padding=kernel_size // 2)
+
+    def forward(self, x, skips, **kwargs):
+        # x [B, T, C]
+        if self.is_BTC:
+            x = x.transpose(1, 2)   # [B, C, T]
+        for i in range(self.n_layers):
+            x = self.ups[i](x)
+            skip_x = skips[self.n_layers - i - 1] if not self.is_BTC \
+                else skips[self.n_layers - i - 1].transpose(1, 2)  # [B, T, C] -> [B, C, T]
+            skip_x = self.skip_layers[i](skip_x) * self.skip_scale
+            x = torch.cat((x, skip_x), dim=1)     # [B, C, T]
+            x = self.layers[i](x)
+        x = self.post_net(self.last_norm(x))
+        if self.is_BTC:
+            x = x.transpose(1, 2)
+        return x
+
+class Unet(nn.Module):
+    def __init__(self, hidden_size, down_layers, up_layers, kernel_size,
+                 updown_rates, mid_layers=None, channel_multiples=None, dropout=0.0,
+                 is_BTC=True, constant_channels=False, mid_net=None, use_skip_layer=False, skip_scale=1.0):
+        super(Unet, self).__init__()
+        assert len(updown_rates) == down_layers == up_layers, f"{len(updown_rates)}, {down_layers}, {up_layers}"
+        if channel_multiples is not None:
+            assert len(channel_multiples) == len(updown_rates)
+        else:
+            channel_multiples = updown_rates
+        self.down = UnetDown(hidden_size, down_layers, kernel_size, updown_rates,
+                             channel_multiples, dropout, is_BTC, constant_channels)
+        down_out_dims = int(np.cumprod(channel_multiples)[-1] * hidden_size) if not constant_channels else hidden_size
+        self.mid = UnetMid(hidden_size, kernel_size, mid_layers,
+                           in_dims=down_out_dims, out_dims=down_out_dims, dropout=dropout, is_BTC=is_BTC, net=mid_net)
+        self.up = UnetUp(hidden_size, up_layers, kernel_size, updown_rates,
+                         channel_multiples, dropout, is_BTC, constant_channels, use_skip_layer, skip_scale)
+
+    def forward(self, x, mid_cond=None, **kwargs):
+        x, skips = self.down(x)
+        x = self.mid(x, mid_cond)
+        x = self.up(x, skips)
+        return x

preprocess/tools/note_transcription/utils/__init__.py

@@ -0,0 +1,15 @@
+
+def seed_everything(seed: int, seed_cudnn=False):
+    import random, os
+    import numpy as np
+    import torch
+
+    random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    if seed_cudnn:
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = True
+

preprocess/tools/note_transcription/utils/audio/__init__.py

@@ -0,0 +1,100 @@
+import librosa
+import numpy as np
+import wave
+import soundfile as sf
+
+
+def librosa_pad_lr(x, fsize, fshift, pad_sides=1):
+    '''compute right padding (final frame) or both sides padding (first and final frames)
+    '''
+    assert pad_sides in (1, 2)
+    # return int(fsize // 2)
+    pad = (x.shape[0] // fshift + 1) * fshift - x.shape[0]
+    if pad_sides == 1:
+        return 0, pad
+    else:
+        return pad // 2, pad // 2 + pad % 2
+
+
+def amp_to_db(x):
+    return 20 * np.log10(np.maximum(1e-5, x))
+
+
+def db_to_amp(x):
+    return 10.0 ** (x * 0.05)
+
+
+def normalize(S, min_level_db):
+    return (S - min_level_db) / -min_level_db
+
+
+def denormalize(D, min_level_db):
+    return (D * -min_level_db) + min_level_db
+
+
+def librosa_wav2spec(wav_path,
+                     fft_size=1024,
+                     hop_size=256,
+                     win_length=1024,
+                     window="hann",
+                     num_mels=80,
+                     fmin=80,
+                     fmax=-1,
+                     eps=1e-6,
+                     sample_rate=22050,
+                     loud_norm=False,
+                     trim_long_sil=False):
+    import pyloudnorm as pyln
+    if isinstance(wav_path, str):
+        if trim_long_sil:
+            from .vad import trim_long_silences
+            wav, _, _ = trim_long_silences(wav_path, sample_rate)
+        else:
+            wav, _ = librosa.core.load(wav_path, sr=sample_rate)
+    else:
+        wav = wav_path
+    wav_orig = np.copy(wav)
+
+    if loud_norm:
+        meter = pyln.Meter(sample_rate)  # create BS.1770 meter
+        loudness = meter.integrated_loudness(wav)
+        wav = pyln.normalize.loudness(wav, loudness, -22.0)
+        if np.abs(wav).max() > 1:
+            wav = wav / np.abs(wav).max()
+
+    # get amplitude spectrogram
+    x_stft = librosa.stft(wav, n_fft=fft_size, hop_length=hop_size,
+                          win_length=win_length, window=window, pad_mode="constant")
+    linear_spc = np.abs(x_stft)  # (n_bins, T)
+
+    # get mel basis
+    fmin = 0 if fmin == -1 else fmin
+    fmax = sample_rate / 2 if fmax == -1 else fmax
+    mel_basis = librosa.filters.mel(sr=sample_rate, n_fft=fft_size, n_mels=num_mels, fmin=fmin, fmax=fmax)
+
+    # calculate mel spec
+    mel = mel_basis @ linear_spc
+    mel = np.log10(np.maximum(eps, mel))  # (n_mel_bins, T)
+    l_pad, r_pad = librosa_pad_lr(wav, fft_size, hop_size, 1)
+    wav = np.pad(wav, (l_pad, r_pad), mode='constant', constant_values=0.0)
+    wav = wav[:mel.shape[1] * hop_size]
+
+    # log linear spec
+    linear_spc = np.log10(np.maximum(eps, linear_spc))
+    return {'wav': wav, 'mel': mel.T, 'linear': linear_spc.T, 'mel_basis': mel_basis, 'wav_orig': wav_orig}
+
+def get_wav_num_frames(path, sr=None):
+    try:
+        with wave.open(path, 'rb') as f:
+            sr_ = f.getframerate()
+            if sr is None:
+                sr = sr_
+            return int(f.getnframes() / (sr_ / sr))
+    except wave.Error:
+        wav_file, sr_ = sf.read(path, dtype='float32')
+        if sr is None:
+                sr = sr_
+        return int(len(wav_file) / (sr_ / sr))
+    except:
+        wav_file, sr_ = librosa.core.load(path, sr=sr)
+        return len(wav_file)

preprocess/tools/note_transcription/utils/audio/align.py

@@ -0,0 +1,90 @@
+import re
+
+import torch
+import numpy as np
+
+from ..text.text_encoder import is_sil_phoneme
+
+
+def get_mel2ph(tg_fn, ph, mel, hop_size, audio_sample_rate, min_sil_duration=0):
+    from textgrid import TextGrid
+    ph_list = ph.split(" ")
+    itvs = TextGrid.fromFile(tg_fn)[1]
+    itvs_ = []
+    for i in range(len(itvs)):
+        if itvs[i].maxTime - itvs[i].minTime < min_sil_duration and i > 0 and is_sil_phoneme(itvs[i].mark):
+            itvs_[-1].maxTime = itvs[i].maxTime
+        else:
+            itvs_.append(itvs[i])
+    itvs.intervals = itvs_
+    itv_marks = [itv.mark for itv in itvs]
+    tg_len = len([x for x in itvs if not is_sil_phoneme(x.mark)])
+    ph_len = len([x for x in ph_list if not is_sil_phoneme(x)])
+    assert tg_len == ph_len, (tg_len, ph_len, itv_marks, ph_list, tg_fn)
+    mel2ph = np.zeros([mel.shape[0]], int)
+    i_itv = 0
+    i_ph = 0
+    while i_itv < len(itvs):
+        itv = itvs[i_itv]
+        ph = ph_list[i_ph]
+        itv_ph = itv.mark
+        start_frame = int(itv.minTime * audio_sample_rate / hop_size + 0.5)
+        end_frame = int(itv.maxTime * audio_sample_rate / hop_size + 0.5)
+        if is_sil_phoneme(itv_ph) and not is_sil_phoneme(ph):
+            mel2ph[start_frame:end_frame] = i_ph
+            i_itv += 1
+        elif not is_sil_phoneme(itv_ph) and is_sil_phoneme(ph):
+            i_ph += 1
+        else:
+            if not ((is_sil_phoneme(itv_ph) and is_sil_phoneme(ph)) \
+                    or re.sub(r'\d+', '', itv_ph.lower()) == re.sub(r'\d+', '', ph.lower())):
+                print(f"| WARN: {tg_fn} phs are not same: ", itv_ph, ph, itv_marks, ph_list)
+            mel2ph[start_frame:end_frame] = i_ph + 1
+            i_ph += 1
+            i_itv += 1
+    mel2ph[-1] = mel2ph[-2]
+    assert not np.any(mel2ph == 0)
+    T_t = len(ph_list)
+    dur = mel2token_to_dur(mel2ph, T_t)
+    return mel2ph.tolist(), dur.tolist()
+
+
+def split_audio_by_mel2ph(audio, mel2ph, hop_size, audio_num_mel_bins):
+    if isinstance(audio, torch.Tensor):
+        audio = audio.numpy()
+    if isinstance(mel2ph, torch.Tensor):
+        mel2ph = mel2ph.numpy()
+    assert len(audio.shape) == 1, len(mel2ph.shape) == 1
+    split_locs = []
+    for i in range(1, len(mel2ph)):
+        if mel2ph[i] != mel2ph[i - 1]:
+            split_loc = i * hop_size
+            split_locs.append(split_loc)
+
+    new_audio = []
+    for i in range(len(split_locs) - 1):
+        new_audio.append(audio[split_locs[i]:split_locs[i + 1]])
+        new_audio.append(np.zeros([0.5 * audio_num_mel_bins]))
+    return np.concatenate(new_audio)
+
+
+def mel2token_to_dur(mel2token, T_txt=None, max_dur=None):
+    is_torch = isinstance(mel2token, torch.Tensor)
+    has_batch_dim = True
+    if not is_torch:
+        mel2token = torch.LongTensor(mel2token)
+    if T_txt is None:
+        T_txt = mel2token.max()
+    if len(mel2token.shape) == 1:
+        mel2token = mel2token[None, ...]
+        has_batch_dim = False
+    B, _ = mel2token.shape
+    dur = mel2token.new_zeros(B, T_txt + 1).scatter_add(1, mel2token, torch.ones_like(mel2token))
+    dur = dur[:, 1:]
+    if max_dur is not None:
+        dur = dur.clamp(max=max_dur)
+    if not is_torch:
+        dur = dur.numpy()
+    if not has_batch_dim:
+        dur = dur[0]
+    return dur

preprocess/tools/note_transcription/utils/audio/io.py

@@ -0,0 +1,22 @@
+import subprocess
+
+import numpy as np
+from scipy.io import wavfile
+
+
+def save_wav(wav, path, sr, norm=False):
+    if norm:
+        wav = wav / np.abs(wav).max()
+    wav = wav * 32767
+    wavfile.write(path[:-4] + '.wav', sr, wav.astype(np.int16))
+    if path[-4:] == '.mp3':
+        to_mp3(path[:-4])
+
+
+def to_mp3(out_path):
+    if out_path[-4:] == '.wav':
+        out_path = out_path[:-4]
+    subprocess.check_call(
+        f'ffmpeg -threads 1 -loglevel error -i "{out_path}.wav" -vn -b:a 192k -y -hide_banner -async 1 "{out_path}.mp3"',
+        shell=True, stdin=subprocess.PIPE)
+    subprocess.check_call(f'rm -f "{out_path}.wav"', shell=True)

preprocess/tools/note_transcription/utils/audio/mel.py

@@ -0,0 +1,139 @@
+import math
+import numpy as np
+import torch
+import torch.utils.data
+from librosa.filters import mel as librosa_mel_fn
+from scipy.io.wavfile import read
+import torch
+import torch.nn as nn
+
+MAX_WAV_VALUE = 32768.0
+
+
+def load_wav(full_path):
+    sampling_rate, data = read(full_path)
+    return data, sampling_rate
+
+
+def dynamic_range_compression(x, C=1, clip_val=1e-5):
+    return np.log10(np.clip(x, a_min=clip_val, a_max=None) * C)
+
+
+def dynamic_range_decompression(x, C=1):
+    return np.exp(x) / C
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    return torch.log10(torch.clamp(x, min=clip_val) * C)
+
+
+def dynamic_range_decompression_torch(x, C=1):
+    return torch.exp(x) / C
+
+
+def spectral_normalize_torch(magnitudes):
+    output = dynamic_range_compression_torch(magnitudes)
+    return output
+
+
+def spectral_de_normalize_torch(magnitudes):
+    output = dynamic_range_decompression_torch(magnitudes)
+    return output
+
+
+class MelNet(nn.Module):
+    def __init__(self, hparams, device='cpu') -> None:
+        super().__init__()
+        self.n_fft = hparams['fft_size']
+        self.num_mels = hparams['audio_num_mel_bins']
+        self.sampling_rate = hparams['audio_sample_rate']
+        self.hop_size = hparams['hop_size']
+        self.win_size = hparams['win_size']
+        self.fmin = hparams['fmin']
+        self.fmax = hparams['fmax']
+        self.device = device
+
+        mel = librosa_mel_fn(sr=self.sampling_rate, n_fft=self.n_fft, n_mels=self.num_mels, fmin=self.fmin,
+                             fmax=self.fmax)
+        self.mel_basis = torch.from_numpy(mel).float().to(self.device)
+        self.hann_window = torch.hann_window(self.win_size).to(self.device)
+
+    def to(self, device, **kwagrs):
+        super().to(device=device, **kwagrs)
+        self.mel_basis = self.mel_basis.to(device)
+        self.hann_window = self.hann_window.to(device)
+        self.device = device
+
+    def forward(self, y, center=False, complex=False):
+        if isinstance(y, np.ndarray):
+            y = torch.FloatTensor(y)
+        if len(y.shape) == 1:
+            y = y.unsqueeze(0)
+        y = y.clamp(min=-1., max=1.).to(self.device)
+
+        pad_length = math.ceil(y.shape[1] / self.hop_size) * self.hop_size - y.shape[1]
+        y = torch.nn.functional.pad(y.unsqueeze(1),
+                                    [int((self.n_fft - self.hop_size) / 2),
+                                     int((self.n_fft - self.hop_size) / 2 + pad_length)],
+                                    mode='reflect')
+        y = y.squeeze(1)
+
+        spec = torch.stft(y, self.n_fft, hop_length=self.hop_size, win_length=self.win_size, window=self.hann_window,
+                          center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=True)
+        if not complex:
+            spec = torch.view_as_real(spec)
+            spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))     # [B, n_fft, T]
+            spec = torch.matmul(self.mel_basis, spec)
+            spec = spectral_normalize_torch(spec)
+            spec = spec.transpose(1, 2)     # [B, T, n_fft]
+        else:
+            B, C, T, _ = spec.shape
+            spec = spec.transpose(1, 2)  # [B, T, n_fft, 2]
+        return spec
+
+
+## below can be used in one gpu, but not ddp
+mel_basis = {}
+hann_window = {}
+
+
+def mel_spectrogram(y, hparams, center=False, complex=False):  # y should be a tensor with shape (b,wav_len)
+    # hop_size: 512  # For 22050Hz, 275 ~= 12.5 ms (0.0125 * sample_rate)
+    # win_size: 2048  # For 22050Hz, 1100 ~= 50 ms (If None, win_size: fft_size) (0.05 * sample_rate)
+    # fmin: 55  # Set this to 55 if your speaker is male! if female, 95 should help taking off noise. (To test depending on dataset. Pitch info: male~[65, 260], female~[100, 525])
+    # fmax: 10000  # To be increased/reduced depending on data.
+    # fft_size: 2048  # Extra window size is filled with 0 paddings to match this parameter
+    # n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax,
+    n_fft = hparams['fft_size']
+    num_mels = hparams['audio_num_mel_bins']
+    sampling_rate = hparams['audio_sample_rate']
+    hop_size = hparams['hop_size']
+    win_size = hparams['win_size']
+    fmin = hparams['fmin']
+    fmax = hparams['fmax']
+    if isinstance(y, np.ndarray):
+        y = torch.FloatTensor(y)
+    if len(y.shape) == 1:
+        y = y.unsqueeze(0)
+    y = y.clamp(min=-1., max=1.)
+    global mel_basis, hann_window
+    if fmax not in mel_basis:
+        mel = librosa_mel_fn(sampling_rate, n_fft, num_mels, fmin, fmax)
+        mel_basis[str(fmax) + '_' + str(y.device)] = torch.from_numpy(mel).float().to(y.device)
+        hann_window[str(y.device)] = torch.hann_window(win_size).to(y.device)
+
+    y = torch.nn.functional.pad(y.unsqueeze(1), [int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)],
+                                mode='reflect')
+    y = y.squeeze(1)
+
+    spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[str(y.device)],
+                      center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=complex)
+
+    if not complex:
+        spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+        spec = torch.matmul(mel_basis[str(fmax) + '_' + str(y.device)], spec)
+        spec = spectral_normalize_torch(spec)
+    else:
+        B, C, T, _ = spec.shape
+        spec = spec.transpose(1, 2)  # [B, T, n_fft, 2]
+    return spec

preprocess/tools/note_transcription/utils/audio/pitch_extractors.py

@@ -0,0 +1,60 @@
+import math
+import numpy as np
+
+PITCH_EXTRACTOR = {}
+
+
+def register_pitch_extractor(name):
+    def register_pitch_extractor_(cls):
+        PITCH_EXTRACTOR[name] = cls
+        return cls
+
+    return register_pitch_extractor_
+
+
+def get_pitch_extractor(name):
+    return PITCH_EXTRACTOR[name]
+
+
+def extract_pitch_simple(wav):
+    from ..commons.hparams import hparams
+    return extract_pitch(hparams['pitch_extractor'], wav,
+                         hparams['hop_size'], hparams['audio_sample_rate'],
+                         f0_min=hparams['f0_min'], f0_max=hparams['f0_max'])
+
+
+def extract_pitch(extractor_name, wav_data, hop_size, audio_sample_rate, f0_min=75, f0_max=800, **kwargs):
+    return get_pitch_extractor(extractor_name)(wav_data, hop_size, audio_sample_rate, f0_min, f0_max, **kwargs)
+
+
+@register_pitch_extractor('parselmouth')
+def parselmouth_pitch(wav_data, hop_size, audio_sample_rate, f0_min, f0_max,
+                      voicing_threshold=0.6, *args, **kwargs):
+    import parselmouth
+    time_step = hop_size / audio_sample_rate * 1000
+    n_mel_frames = int(len(wav_data) // hop_size)
+    f0_pm = parselmouth.Sound(wav_data, audio_sample_rate).to_pitch_ac(
+        time_step=time_step / 1000, voicing_threshold=voicing_threshold,
+        pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
+    pad_size = (n_mel_frames - len(f0_pm) + 1) // 2
+    f0 = np.pad(f0_pm, [[pad_size, n_mel_frames - len(f0_pm) - pad_size]], mode='constant')
+    return f0
+
+@register_pitch_extractor('pyworld')
+def pyworld_pitch(wav_data, hop_size, audio_sample_rate, f0_min, f0_max,
+                      voicing_threshold=0.6, *args, **kwargs):
+    import pyworld as pw
+    # f0, _ = pw.harvest(wav_data.astype(np.double), audio_sample_rate, f0_floor=f0_min, f0_ceil=f0_max,
+    #                    frame_period=hop_size * 1000 / audio_sample_rate)
+    f0, _ = pw.dio(wav_data.astype(np.double), audio_sample_rate, f0_floor=f0_min, f0_ceil=f0_max, frame_period=hop_size * 1000 / audio_sample_rate)
+    f0[f0 < f0_min] = 0.0
+    f0[f0 > f0_max] = 0.0
+    n_mel_frames = math.ceil(len(wav_data) / hop_size)
+    if n_mel_frames > len(f0):
+        pad_size = (n_mel_frames - len(f0) + 1) // 2
+        f0 = np.pad(f0, [[pad_size, n_mel_frames - len(f0) - pad_size]], mode='constant')
+    elif n_mel_frames < len(f0):
+        left_del = (len(f0) - n_mel_frames + 1) // 2
+        right_del = len(f0) - n_mel_frames - left_del
+        f0 = f0[left_del: (-right_del if right_del > 0 else len(f0))]
+    return f0

preprocess/tools/note_transcription/utils/audio/pitch_utils.py

@@ -0,0 +1,303 @@
+import numpy as np
+import torch
+import pretty_midi
+
+def to_lf0(f0):
+    f0[f0 < 1.0e-5] = 1.0e-6
+    lf0 = f0.log() if isinstance(f0, torch.Tensor) else np.log(f0)
+    lf0[f0 < 1.0e-5] = - 1.0E+10
+    return lf0
+
+
+def to_f0(lf0):
+    f0 = np.where(lf0 <= 0, 0.0, np.exp(lf0))
+    return f0.flatten()
+
+
+def f0_to_coarse(f0, f0_bin=256, f0_max=900.0, f0_min=50.0):
+    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
+    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
+    is_torch = isinstance(f0, torch.Tensor)
+    f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
+    f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
+
+    f0_mel[f0_mel <= 1] = 1
+    f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
+    f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(int)
+    assert f0_coarse.max() <= f0_bin-1 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min(), f0.min(), f0.max())
+    return f0_coarse
+
+
+def coarse_to_f0(f0_coarse, f0_bin=256, f0_max=900.0, f0_min=50.0):
+    f0_mel_min = 1127 * np.log(1 + f0_min / 700)
+    f0_mel_max = 1127 * np.log(1 + f0_max / 700)
+    uv = f0_coarse == 1
+    f0 = f0_mel_min + (f0_coarse - 1) * (f0_mel_max - f0_mel_min) / (f0_bin - 2)
+    f0 = ((f0 / 1127).exp() - 1) * 700
+    f0[uv] = 0
+    return f0
+
+
+def norm_f0(f0, uv, pitch_norm='log', f0_mean=400, f0_std=100):
+    is_torch = isinstance(f0, torch.Tensor)
+    if pitch_norm == 'standard':
+        f0 = (f0 - f0_mean) / f0_std
+    if pitch_norm == 'log':
+        f0 = torch.log2(f0 + 1e-8) if is_torch else np.log2(f0 + 1e-8)
+    if uv is not None:
+        f0[uv > 0] = 0
+    return f0
+
+
+def norm_interp_f0(f0, pitch_norm='log', f0_mean=None, f0_std=None):
+    is_torch = isinstance(f0, torch.Tensor)
+    if is_torch:
+        device = f0.device
+        f0 = f0.data.cpu().numpy()
+    uv = f0 == 0
+    f0 = norm_f0(f0, uv, pitch_norm, f0_mean, f0_std)
+    if sum(uv) == len(f0):
+        f0[uv] = 0
+    elif sum(uv) > 0:
+        f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
+    if is_torch:
+        uv = torch.FloatTensor(uv)
+        f0 = torch.FloatTensor(f0)
+        f0 = f0.to(device)
+        uv = uv.to(device)
+    return f0, uv
+
+
+def denorm_f0(f0, uv, pitch_norm='log', f0_mean=400, f0_std=100, pitch_padding=None, min=50, max=900):
+    is_torch = isinstance(f0, torch.Tensor)
+    if pitch_norm == 'standard':
+        f0 = f0 * f0_std + f0_mean
+    if pitch_norm == 'log':
+        f0 = 2 ** f0
+    f0 = f0.clamp(min=min, max=max) if is_torch else np.clip(f0, a_min=min, a_max=max)
+    if uv is not None:
+        f0[uv > 0] = 0
+    if pitch_padding is not None:
+        f0[pitch_padding] = 0
+    return f0
+
+def interp_f0(f0, uv=None):
+    if uv is None:
+        uv = f0 == 0
+    f0 = norm_f0(f0, uv)
+    if uv.any() and not uv.all():
+        f0[uv] = np.interp(np.where(uv)[0], np.where(~uv)[0], f0[~uv])
+    return denorm_f0(f0, uv=None), uv
+
+def resample_align_curve(points: np.ndarray, original_timestep: float, target_timestep: float, align_length=-1):
+    t_max = (len(points) - 1) * original_timestep
+    curve_interp = np.interp(
+        np.arange(0, t_max, target_timestep),
+        original_timestep * np.arange(len(points)),
+        points
+    ).astype(points.dtype)
+    if align_length > 0:
+        delta_l = align_length - len(curve_interp)
+        if delta_l < 0:
+            curve_interp = curve_interp[:align_length]
+        elif delta_l > 0:
+            curve_interp = np.concatenate((curve_interp, np.full(delta_l, fill_value=curve_interp[-1])), axis=0)
+    return curve_interp
+
+def midi_to_hz(midi):
+    if type(midi) == np.ndarray:
+        non_mask = midi == 0
+        freq_hz = 440.0 * 2.0 ** ((midi - 69.0) / 12.0)
+        freq_hz[non_mask] = 0
+    else:
+        freq_hz = 440.0 * 2.0 ** ((midi - 69.0) / 12.0)
+    return freq_hz
+
+def hz_to_midi(hz):
+    if type(hz) == torch.Tensor:
+        non_mask = hz == 0
+        midi = 69.0 + 12.0 * (torch.log2(hz) - torch.log2(torch.Tensor(440.0)))
+        midi[non_mask] = 0
+    elif type(hz) == np.ndarray:
+        non_mask = hz == 0
+        midi = 69.0 + 12.0 * (np.log2(hz) - np.log2(440.0))
+        midi[non_mask] = 0
+    else:
+        midi = 69.0 + 12.0 * (np.log2(hz) - np.log2(440.0))
+        if hz == 0:
+            midi = 0
+    return midi
+
+def boundary2Interval(bd):
+    # bd has a shape of [T] with T frames
+    is_torch = isinstance(bd, torch.Tensor)
+    if is_torch:
+        device = bd.device
+        bd = bd.data.cpu().numpy()
+    assert len(bd.shape) == 1
+    # force valid begin and end
+    # bd[0] = 0     # took care of in regulate_boundary()
+    # bd[-1] = 0
+    ret = np.zeros(shape=(bd.sum() + 1, 2), dtype=int)
+    ret_idx = 0
+    ret[0, 0] = 0
+    for i, u in enumerate(bd):
+        if i == 0:
+            continue
+        if u == 1:
+            ret[ret_idx, 1] = i
+            ret[ret_idx+1, 0] = i
+            ret_idx += 1
+    ret[-1, 1] = bd.shape[0] - 1
+    if is_torch:
+        ret = torch.LongTensor(ret).to(device)
+    return ret
+
+def validate_pitch_and_itv(notes, note_itv):
+    # notes [T]
+    # note_itv [T, 2]
+    assert notes.shape[0] == note_itv.shape[0]
+    res_notes = []
+    res_note_itv = []
+    for idx in range(notes.shape[0]):
+        pitch, itv = notes[idx], note_itv[idx]
+        if itv[0] >= itv[1]:
+            raise RuntimeError("The note duration should be positive")
+        if pitch == 0:
+            continue
+        res_notes.append(pitch)
+        res_note_itv.append([itv[0], itv[1]])
+    res_notes = np.array(res_notes)
+    res_note_itv = np.array(res_note_itv)
+    return res_notes, res_note_itv
+
+def save_midi(notes, note_itv, midi_path):
+    # notes [T]
+    # note_itv [T, 2]
+    notes, note_itv = validate_pitch_and_itv(notes, note_itv)
+    if notes.shape == (0,):
+        return None
+    assert notes.shape[0] == note_itv.shape[0]
+    piano_chord = pretty_midi.PrettyMIDI()
+    piano_program = pretty_midi.instrument_name_to_program('Acoustic Grand Piano')
+    piano = pretty_midi.Instrument(program=piano_program)
+    for idx in range(notes.shape[0]):
+        pitch, itv = notes[idx], note_itv[idx]
+        note = pretty_midi.Note(velocity=120, pitch=pitch, start=itv[0], end=itv[1])
+        piano.notes.append(note)
+    piano_chord.remove_invalid_notes()
+    piano_chord.instruments.append(piano)
+    piano_chord.write(midi_path)
+    return piano_chord
+
+def midi2NoteInterval(mid):
+    assert type(mid) == pretty_midi.PrettyMIDI
+    if len(mid.instruments) == 0 or len(mid.instruments[0].notes) == 0:
+        return None
+    ret = np.zeros(shape=(len(mid.instruments[0].notes), 2))
+    for i, note in enumerate(mid.instruments[0].notes):
+        ret[i, 0] = note.start
+        ret[i, 1] = note.end
+    return ret
+
+def midi2NotePitch(mid):
+    assert type(mid) == pretty_midi.PrettyMIDI
+    if len(mid.instruments) == 0 or len(mid.instruments[0].notes) == 0:
+        return None
+    ret = np.zeros(shape=len(mid.instruments[0].notes))
+    for i, note in enumerate(mid.instruments[0].notes):
+        ret[i] = note.pitch
+    return ret
+
+def midi_onset_eval(mid_gt, mid_pred):
+    import mir_eval
+    interval_true = midi2NoteInterval(mid_gt)
+    if interval_true is None:
+        raise RuntimeError('Midi ground truth is None')
+    interval_pred = midi2NoteInterval(mid_pred)
+    if interval_pred is None:
+        return 0, 0, 0
+    onset_p, onset_r, onset_f = mir_eval.transcription.onset_precision_recall_f1(
+        interval_true, interval_pred, onset_tolerance=0.05, strict=False, beta=1.0)
+    return onset_p, onset_r, onset_f
+
+def midi_offset_eval(mid_gt, mid_pred):
+    import mir_eval
+    interval_true = midi2NoteInterval(mid_gt)
+    if interval_true is None:
+        raise RuntimeError('Midi ground truth is None')
+    interval_pred = midi2NoteInterval(mid_pred)
+    if interval_pred is None:
+        return 0, 0, 0
+    offset_p, offset_r, offset_f = mir_eval.transcription.offset_precision_recall_f1(
+        interval_true, interval_pred, offset_ratio=0.2, offset_min_tolerance=0.05, strict=False, beta=1.0)
+    return offset_p, offset_r, offset_f
+
+def midi_pitch_eval(mid_gt, mid_pred, offset_ratio=0.2):
+    import mir_eval
+    interval_true = midi2NoteInterval(mid_gt)
+    pitch_true = midi_to_hz(midi2NotePitch(mid_gt))
+    if interval_true is None or pitch_true is None:
+        raise RuntimeError('Midi ground truth is None')
+    interval_pred = midi2NoteInterval(mid_pred)
+    pitch_pred = midi2NotePitch(mid_pred)
+    if interval_pred is None:
+        return 0, 0, 0, 0
+    if pitch_pred is None:
+        pitch_pred = np.zeros(interval_pred.shape[0])
+    pitch_pred = midi_to_hz(pitch_pred)
+    overlap_p, overlap_r, overlap_f, avg_overlap_ratio = mir_eval.transcription.precision_recall_f1_overlap(
+        interval_true, pitch_true, interval_pred, pitch_pred, onset_tolerance=0.05, pitch_tolerance=50.0,
+        offset_ratio=offset_ratio, offset_min_tolerance=0.05, strict=False, beta=1.0)
+    return overlap_p, overlap_r, overlap_f, avg_overlap_ratio
+
+def midi_COn_eval(mid_gt, mid_pred):
+    return midi_onset_eval(mid_gt, mid_pred)
+
+def midi_COnP_eval(mid_gt, mid_pred):
+    return midi_pitch_eval(mid_gt, mid_pred, offset_ratio=None)
+
+def midi_COnPOff_eval(mid_gt, mid_pred):
+    return midi_pitch_eval(mid_gt, mid_pred)
+
+def midi_melody_eval(mid_gt, mid_pred, hop_size=256, sample_rate=48000):
+    interval_true = midi2NoteInterval(mid_gt)
+    pitch_true = midi_to_hz(midi2NotePitch(mid_gt))
+    if interval_true is None or pitch_true is None:
+        raise RuntimeError('Midi ground truth is None')
+    interval_pred = midi2NoteInterval(mid_pred)
+    pitch_pred = midi2NotePitch(mid_pred)
+    if interval_pred is None:
+        return 0, 0, 0, 0
+    if pitch_pred is None:
+        pitch_pred = np.zeros(interval_pred.shape[0])
+    pitch_pred = midi_to_hz(pitch_pred)
+
+    vr, vfa, rpa, rca, oa = melody_eval_pitch_and_itv(
+        pitch_true, interval_true, pitch_pred, interval_pred, hop_size, sample_rate)
+
+    return vr, vfa, rpa, rca, oa
+
+def melody_eval_pitch_and_itv(pitch_true, interval_true, pitch_pred, interval_pred, hop_size=256, sample_rate=48000):
+    import mir_eval
+    t_gt = np.arange(0, interval_true[-1][1], hop_size / sample_rate)
+    freq_gt = np.zeros_like(t_gt)
+    for idx in range(len(pitch_true)):
+        freq_gt[min(len(freq_gt) - 1, round(interval_true[idx][0] * sample_rate / hop_size)): round(
+            interval_true[idx][1] * sample_rate / hop_size)] = pitch_true[idx]
+
+    t_pred = np.arange(0, interval_pred[-1][1], hop_size / sample_rate)
+    freq_pred = np.zeros_like(t_pred)
+    for idx in range(len(pitch_pred)):
+        freq_pred[min(len(freq_pred) - 1, round(interval_pred[idx][0] * sample_rate / hop_size)): round(
+            interval_pred[idx][1] * sample_rate / hop_size)] = pitch_pred[idx]
+
+    ref_voicing, ref_cent, est_voicing, est_cent = mir_eval.melody.to_cent_voicing(t_gt, freq_gt,
+                                                                                   t_pred, freq_pred)
+    vr, vfa = mir_eval.melody.voicing_measures(ref_voicing,
+                                               est_voicing)  # voicing recall, voicing false alarm
+    rpa = mir_eval.melody.raw_pitch_accuracy(ref_voicing, ref_cent, est_voicing, est_cent)
+    rca = mir_eval.melody.raw_chroma_accuracy(ref_voicing, ref_cent, est_voicing, est_cent)
+    oa = mir_eval.melody.overall_accuracy(ref_voicing, ref_cent, est_voicing, est_cent)
+
+    return vr, vfa, rpa, rca, oa

preprocess/tools/note_transcription/utils/audio/vad.py

@@ -0,0 +1,78 @@
+from skimage.transform import resize
+import struct
+import webrtcvad
+from scipy.ndimage.morphology import binary_dilation
+import librosa
+import numpy as np
+import pyloudnorm as pyln
+import warnings
+
+warnings.filterwarnings("ignore", message="Possible clipped samples in output")
+
+int16_max = (2 ** 15) - 1
+
+
+def trim_long_silences(path, sr=None, return_raw_wav=False, norm=True, vad_max_silence_length=12):
+    """
+    Ensures that segments without voice in the waveform remain no longer than a
+    threshold determined by the VAD parameters in params.py.
+    :param wav: the raw waveform as a numpy array of floats
+    :param vad_max_silence_length: Maximum number of consecutive silent frames a segment can have.
+    :return: the same waveform with silences trimmed away (length <= original wav length)
+    """
+
+    ## Voice Activation Detection
+    # Window size of the VAD. Must be either 10, 20 or 30 milliseconds.
+    # This sets the granularity of the VAD. Should not need to be changed.
+    sampling_rate = 16000
+    wav_raw, sr = librosa.core.load(path, sr=sr)
+
+    if norm:
+        meter = pyln.Meter(sr)  # create BS.1770 meter
+        loudness = meter.integrated_loudness(wav_raw)
+        wav_raw = pyln.normalize.loudness(wav_raw, loudness, -20.0)
+        if np.abs(wav_raw).max() > 1.0:
+            wav_raw = wav_raw / np.abs(wav_raw).max()
+
+    wav = librosa.resample(wav_raw, sr, sampling_rate, res_type='kaiser_best')
+
+    vad_window_length = 30  # In milliseconds
+    # Number of frames to average together when performing the moving average smoothing.
+    # The larger this value, the larger the VAD variations must be to not get smoothed out.
+    vad_moving_average_width = 8
+
+    # Compute the voice detection window size
+    samples_per_window = (vad_window_length * sampling_rate) // 1000
+
+    # Trim the end of the audio to have a multiple of the window size
+    wav = wav[:len(wav) - (len(wav) % samples_per_window)]
+
+    # Convert the float waveform to 16-bit mono PCM
+    pcm_wave = struct.pack("%dh" % len(wav), *(np.round(wav * int16_max)).astype(np.int16))
+
+    # Perform voice activation detection
+    voice_flags = []
+    vad = webrtcvad.Vad(mode=3)
+    for window_start in range(0, len(wav), samples_per_window):
+        window_end = window_start + samples_per_window
+        voice_flags.append(vad.is_speech(pcm_wave[window_start * 2:window_end * 2],
+                                         sample_rate=sampling_rate))
+    voice_flags = np.array(voice_flags)
+
+    # Smooth the voice detection with a moving average
+    def moving_average(array, width):
+        array_padded = np.concatenate((np.zeros((width - 1) // 2), array, np.zeros(width // 2)))
+        ret = np.cumsum(array_padded, dtype=float)
+        ret[width:] = ret[width:] - ret[:-width]
+        return ret[width - 1:] / width
+
+    audio_mask = moving_average(voice_flags, vad_moving_average_width)
+    audio_mask = np.round(audio_mask).astype(np.bool)
+
+    # Dilate the voiced regions
+    audio_mask = binary_dilation(audio_mask, np.ones(vad_max_silence_length + 1))
+    audio_mask = np.repeat(audio_mask, samples_per_window)
+    audio_mask = resize(audio_mask, (len(wav_raw),)) > 0
+    if return_raw_wav:
+        return wav_raw, audio_mask, sr
+    return wav_raw[audio_mask], audio_mask, sr

preprocess/tools/note_transcription/utils/commons/base_task.py

@@ -0,0 +1,235 @@
+import logging
+import os
+import random
+import subprocess
+import sys
+from datetime import datetime
+import numpy as np
+import torch.utils.data
+from torch import nn
+from torch.utils.tensorboard import SummaryWriter
+from .dataset_utils import data_loader
+from .hparams import hparams
+from .meters import AvgrageMeter
+from .tensor_utils import tensors_to_scalars
+from .trainer import Trainer
+
+torch.multiprocessing.set_sharing_strategy(os.getenv('TORCH_SHARE_STRATEGY', 'file_system'))
+
+log_format = '%(asctime)s %(message)s'
+logging.basicConfig(stream=sys.stdout, level=logging.INFO,
+                    format=log_format, datefmt='%m/%d %I:%M:%S %p')
+
+
+class BaseTask(nn.Module):
+    def __init__(self, *args, **kwargs):
+        super(BaseTask, self).__init__()
+        self.current_epoch = 0
+        self.global_step = 0
+        self.trainer = None
+        self.use_ddp = False
+        self.gradient_clip_norm = hparams['clip_grad_norm']
+        self.gradient_clip_val = hparams.get('clip_grad_value', 0)
+        self.model = None
+        self.training_losses_meter = None
+        self.logger: SummaryWriter = None
+
+    ######################
+    # build model, dataloaders, optimizer, scheduler and tensorboard
+    ######################
+    def build_model(self):
+        raise NotImplementedError
+
+    @data_loader
+    def train_dataloader(self):
+        raise NotImplementedError
+
+    @data_loader
+    def test_dataloader(self):
+        raise NotImplementedError
+
+    @data_loader
+    def val_dataloader(self):
+        raise NotImplementedError
+
+    def build_scheduler(self, optimizer):
+        return None
+
+    def build_optimizer(self, model):
+        raise NotImplementedError
+
+    def configure_optimizers(self):
+        optm = self.build_optimizer(self.model)
+        self.scheduler = self.build_scheduler(optm)
+        if isinstance(optm, (list, tuple)):
+            return optm
+        return [optm]
+
+    def build_tensorboard(self, save_dir, name, **kwargs):
+        log_dir = os.path.join(save_dir, name)
+        os.makedirs(log_dir, exist_ok=True)
+        self.logger = SummaryWriter(log_dir=log_dir, **kwargs)
+
+    ######################
+    # training
+    ######################
+    def on_train_start(self):
+        pass
+
+    def on_train_end(self):
+        pass
+
+    def on_epoch_start(self):
+        self.training_losses_meter = {'total_loss': AvgrageMeter()}
+
+    def on_epoch_end(self):
+        loss_outputs = {k: round(v.avg, 4) for k, v in self.training_losses_meter.items()}
+        print(f"Epoch {self.current_epoch} ended. Steps: {self.global_step}. {loss_outputs}")
+
+    def _training_step(self, sample, batch_idx, optimizer_idx):
+        """
+
+        :param sample:
+        :param batch_idx:
+        :return: total loss: torch.Tensor, loss_log: dict
+        """
+        raise NotImplementedError
+
+    def training_step(self, sample, batch_idx, optimizer_idx=-1):
+        """
+
+        :param sample:
+        :param batch_idx:
+        :param optimizer_idx:
+        :return: {'loss': torch.Tensor, 'progress_bar': dict, 'tb_log': dict}
+        """
+        loss_ret = self._training_step(sample, batch_idx, optimizer_idx)
+        if loss_ret is None:
+            return {'loss': None}
+        total_loss, log_outputs = loss_ret
+        log_outputs = tensors_to_scalars(log_outputs)
+        for k, v in log_outputs.items():
+            if k not in self.training_losses_meter:
+                self.training_losses_meter[k] = AvgrageMeter()
+            if not np.isnan(v):
+                self.training_losses_meter[k].update(v)
+        self.training_losses_meter['total_loss'].update(total_loss.item())
+
+        if optimizer_idx >= 0:
+            log_outputs[f'lr_{optimizer_idx}'] = self.trainer.optimizers[optimizer_idx].param_groups[0]['lr']
+
+        progress_bar_log = log_outputs
+        tb_log = {f'tr/{k}': v for k, v in log_outputs.items()}
+        return {
+            'loss': total_loss,
+            'progress_bar': progress_bar_log,
+            'tb_log': tb_log
+        }
+
+    def on_before_optimization(self, opt_idx):
+        if self.gradient_clip_norm > 0:
+            torch.nn.utils.clip_grad_norm_(self.parameters(), self.gradient_clip_norm)
+        if self.gradient_clip_val > 0:
+            torch.nn.utils.clip_grad_value_(self.parameters(), self.gradient_clip_val)
+
+    def on_after_optimization(self, epoch, batch_idx, optimizer, optimizer_idx):
+        if self.scheduler is not None:
+            # self.scheduler.step(self.global_step // hparams['accumulate_grad_batches'])
+            # the code above causes EPOCH_DEPRECATION_WARNING, changed it and changed the optimizer init with
+            # step_size divided by accumulate_grad_batches
+            self.scheduler.step()
+
+    ######################
+    # validation
+    ######################
+    def validation_start(self):
+        pass
+
+    def validation_step(self, sample, batch_idx):
+        """
+
+        :param sample:
+        :param batch_idx:
+        :return: output: {"losses": {...}, "total_loss": float, ...} or (total loss: torch.Tensor, loss_log: dict)
+        """
+        raise NotImplementedError
+
+    def validation_end(self, outputs):
+        """
+
+        :param outputs:
+        :return: loss_output: dict
+        """
+        all_losses_meter = {'total_loss': AvgrageMeter()}
+        for output in outputs:
+            if len(output) == 0 or output is None:
+                continue
+            if isinstance(output, dict):
+                assert 'losses' in output, 'Key "losses" should exist in validation output.'
+                n = output.pop('nsamples', 1)
+                losses = tensors_to_scalars(output['losses'])
+                total_loss = output.get('total_loss', sum(losses.values()))
+            else:
+                assert len(output) == 2, 'Validation output should only consist of two elements: (total_loss, losses)'
+                n = 1
+                total_loss, losses = output
+                losses = tensors_to_scalars(losses)
+            if isinstance(total_loss, torch.Tensor):
+                total_loss = total_loss.item()
+            for k, v in losses.items():
+                if k not in all_losses_meter:
+                    all_losses_meter[k] = AvgrageMeter()
+                all_losses_meter[k].update(v, n)
+            all_losses_meter['total_loss'].update(total_loss, n)
+        loss_output = {k: round(v.avg, 4) for k, v in all_losses_meter.items()}
+        print(f"| Validation results@{self.global_step}: {loss_output}")
+        return {
+            'tb_log': {f'val/{k}': v for k, v in loss_output.items()},
+            'val_loss': loss_output['total_loss']
+        }
+
+    ######################
+    # testing
+    ######################
+    def test_start(self):
+        pass
+
+    def test_step(self, sample, batch_idx):
+        return self.validation_step(sample, batch_idx)
+
+    def test_end(self, outputs):
+        return self.validation_end(outputs)
+
+    ######################
+    # start training/testing
+    ######################
+    @classmethod
+    def start(cls):
+        os.environ['MASTER_PORT'] = str(random.randint(15000, 30000))
+        random.seed(hparams['seed'])
+        np.random.seed(hparams['seed'])
+        work_dir = hparams['work_dir']
+        trainer = Trainer(
+            work_dir=work_dir,
+            val_check_interval=hparams['val_check_interval'],
+            tb_log_interval=hparams['tb_log_interval'],
+            max_updates=hparams['max_updates'],
+            num_sanity_val_steps=hparams['num_sanity_val_steps'] if not hparams['validate'] else 10000,
+            accumulate_grad_batches=hparams['accumulate_grad_batches'],
+            print_nan_grads=hparams['print_nan_grads'],
+            resume_from_checkpoint=hparams.get('resume_from_checkpoint', 0),
+            amp=hparams['amp'],
+            monitor_key=hparams['valid_monitor_key'],
+            monitor_mode=hparams['valid_monitor_mode'],
+            num_ckpt_keep=hparams['num_ckpt_keep'],
+            save_best=hparams['save_best'],
+            seed=hparams['seed'],
+            debug=hparams['debug']
+        )
+        if not hparams['infer']:  # train
+            trainer.fit(cls)
+        else:
+            trainer.test(cls)
+
+    def on_keyboard_interrupt(self):
+        pass

preprocess/tools/note_transcription/utils/commons/ckpt_utils.py

@@ -0,0 +1,68 @@
+import glob
+import os
+import re
+import torch
+
+
+def get_last_checkpoint(work_dir, steps=None):
+    checkpoint = None
+    last_ckpt_path = None
+    ckpt_paths = get_all_ckpts(work_dir, steps)
+    if len(ckpt_paths) > 0:
+        last_ckpt_path = ckpt_paths[0]
+        checkpoint = torch.load(last_ckpt_path, map_location='cpu')
+    return checkpoint, last_ckpt_path
+
+
+def get_all_ckpts(work_dir, steps=None):
+    if steps is None:
+        ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_*.ckpt'
+    else:
+        ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_{steps}.ckpt'
+    return sorted(glob.glob(ckpt_path_pattern),
+                  key=lambda x: -int(re.findall('.*steps\_(\d+)\.ckpt', x)[0]))
+
+
+def load_ckpt(cur_model, ckpt_base_dir, model_name='model', force=True, strict=True, verbose=True):
+    if os.path.isfile(ckpt_base_dir):
+        base_dir = os.path.dirname(ckpt_base_dir)
+        ckpt_path = ckpt_base_dir
+        checkpoint = torch.load(ckpt_base_dir, map_location='cpu')
+    else:
+        base_dir = ckpt_base_dir
+        checkpoint, ckpt_path = get_last_checkpoint(ckpt_base_dir)
+    if checkpoint is not None:
+        state_dict = checkpoint["state_dict"]
+        if len([k for k in state_dict.keys() if '.' in k]) > 0:
+            state_dict = {k[len(model_name) + 1:]: v for k, v in state_dict.items()
+                          if k.startswith(f'{model_name}.')}
+        else:
+            if '.' not in model_name:
+                state_dict = state_dict[model_name]
+            else:
+                base_model_name = model_name.split('.')[0]
+                rest_model_name = model_name[len(base_model_name) + 1:]
+                state_dict = {
+                    k[len(rest_model_name) + 1:]: v for k, v in state_dict[base_model_name].items()
+                    if k.startswith(f'{rest_model_name}.')}
+        if not strict:
+            cur_model_state_dict = cur_model.state_dict()
+            unmatched_keys = []
+            for key, param in state_dict.items():
+                if key in cur_model_state_dict:
+                    new_param = cur_model_state_dict[key]
+                    if new_param.shape != param.shape:
+                        unmatched_keys.append(key)
+                        print("| Unmatched keys: ", key, new_param.shape, param.shape)
+            for key in unmatched_keys:
+                del state_dict[key]
+        # print(state_dict)
+        cur_model.load_state_dict(state_dict, strict=strict)
+        if verbose:
+            print(f"| load '{model_name}' from '{ckpt_path}'.")
+    else:
+        e_msg = f"| ckpt not found in {base_dir}."
+        if force:
+            assert False, e_msg
+        else:
+            print(e_msg)

preprocess/tools/note_transcription/utils/commons/dataset_utils.py

@@ -0,0 +1,372 @@
+import os
+import sys
+import traceback
+import types
+from functools import wraps
+from itertools import chain
+import numpy as np
+import torch.utils.data
+import torch.nn.functional as F
+from torch.utils.data import ConcatDataset
+from .hparams import hparams
+
+
+def collate_1d_or_2d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None, shift_id=1):
+    if len(values[0].shape) == 1:
+        return collate_1d(values, pad_idx, left_pad, shift_right, max_len, shift_id)
+    else:
+        return collate_2d(values, pad_idx, left_pad, shift_right, max_len)
+
+
+def collate_1d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None, shift_id=1):
+    """Convert a list of 1d tensors into a padded 2d tensor."""
+    size = max(v.size(0) for v in values) if max_len is None else max_len
+    res = values[0].new(len(values), size).fill_(pad_idx)
+
+    def copy_tensor(src, dst):
+        assert dst.numel() == src.numel()
+        if shift_right:
+            dst[1:] = src[:-1]
+            dst[0] = shift_id
+        else:
+            dst.copy_(src)
+
+    for i, v in enumerate(values):
+        copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])
+    return res
+
+
+def collate_2d(values, pad_idx=0, left_pad=False, shift_right=False, max_len=None):
+    """Convert a list of 2d tensors into a padded 3d tensor."""
+    size = max(v.size(0) for v in values) if max_len is None else max_len
+    res = values[0].new(len(values), size, values[0].shape[1]).fill_(pad_idx)
+
+    def copy_tensor(src, dst):
+        assert dst.numel() == src.numel()
+        if shift_right:
+            dst[1:] = src[:-1]
+        else:
+            dst.copy_(src)
+
+    for i, v in enumerate(values):
+        copy_tensor(v, res[i][size - len(v):] if left_pad else res[i][:len(v)])
+    return res
+
+def collate_xd(values, pad_value=0, max_len=None):
+    size = ((max(v.size(0) for v in values) if max_len is None else max_len), *values[0].shape[1:])
+    res = torch.full((len(values), *size), fill_value=pad_value, dtype=values[0].dtype, device=values[0].device)
+
+    for i, v in enumerate(values):
+        res[i, :len(v), ...] = v
+    return res
+
+def pad_or_cut_1d(values: torch.tensor, tgt_len, pad_value=0):
+    src_len = values.shape[0]
+    if src_len < tgt_len:
+        res = F.pad(values, [0, tgt_len - src_len], value=pad_value)
+    else:
+        res = values[:tgt_len]
+    return res
+
+def pad_or_cut_2d(values: torch.tensor, tgt_len, dim=-1, pad_value=0):
+    if dim == 0 or dim == -2:
+        src_len = values.shape[0]
+        if src_len < tgt_len:
+            res = F.pad(values, [0, 0, 0, tgt_len - src_len], value=pad_value)
+        else:
+            res = values[:tgt_len]
+    elif dim == 1 or dim == -1:
+        src_len = values.shape[1]
+        if src_len < tgt_len:
+            res = F.pad(values, [0, tgt_len - src_len], value=pad_value)
+        else:
+            res = values[:, :tgt_len]
+    else:
+        raise RuntimeError(f"Wrong dim number {dim} while the tensor only has {len(values.shape)} dimensions.")
+    return res
+
+def pad_or_cut_3d(values: torch.tensor, tgt_len, dim=-1, pad_value=0):
+    if dim == 0 or dim == -3:
+        src_len = values.shape[0]
+        if src_len < tgt_len:
+            res = F.pad(values, [0, 0, 0, 0, 0, tgt_len - src_len], value=pad_value)
+        else:
+            res = values[:tgt_len]
+    elif dim == 1 or dim == -2:
+        src_len = values.shape[1]
+        if src_len < tgt_len:
+            res = F.pad(values, [0, 0, 0, tgt_len - src_len], value=pad_value)
+        else:
+            res = values[:, :tgt_len]
+    elif dim == 2 or dim == -1:
+        src_len = values.shape[2]
+        if src_len < tgt_len:
+            res = F.pad(values, [0, tgt_len - src_len], value=pad_value)
+        else:
+            res = values[:, :, :tgt_len]
+    else:
+        raise RuntimeError(f"Wrong dim number {dim} while the tensor only has {len(values.shape)} dimensions.")
+    return res
+
+def pad_or_cut_xd(values, tgt_len, dim=-1, pad_value=0):
+    if len(values.shape) == 1:
+        return pad_or_cut_1d(values, tgt_len, pad_value)
+    elif len(values.shape) == 2:
+        return pad_or_cut_2d(values, tgt_len, dim, pad_value)
+    elif len(values.shape) == 3:
+        return pad_or_cut_3d(values, tgt_len, dim, pad_value)
+    else:
+        raise NotImplementedError
+
+def _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
+    if len(batch) == 0:
+        return 0
+    if len(batch) == max_sentences:
+        return 1
+    if num_tokens > max_tokens:
+        return 1
+    return 0
+
+
+def batch_by_size(
+        indices, num_tokens_fn, max_tokens=None, max_sentences=None,
+        required_batch_size_multiple=1, distributed=False
+):
+    """
+    Yield mini-batches of indices bucketed by size. Batches may contain
+    sequences of different lengths.
+
+    Args:
+        indices (List[int]): ordered list of dataset indices
+        num_tokens_fn (callable): function that returns the number of tokens at
+            a given index
+        max_tokens (int, optional): max number of tokens in each batch
+            (default: None).
+        max_sentences (int, optional): max number of sentences in each
+            batch (default: None).
+        required_batch_size_multiple (int, optional): require batch size to
+            be a multiple of N (default: 1).
+    """
+    max_tokens = max_tokens if max_tokens is not None else sys.maxsize
+    max_sentences = max_sentences if max_sentences is not None else sys.maxsize
+    bsz_mult = required_batch_size_multiple
+
+    if isinstance(indices, types.GeneratorType):
+        indices = np.fromiter(indices, dtype=np.int64, count=-1)
+
+    sample_len = 0
+    sample_lens = []
+    batch = []
+    batches = []
+    for i in range(len(indices)):
+        idx = indices[i]
+        num_tokens = num_tokens_fn(idx)
+        sample_lens.append(num_tokens)
+        sample_len = max(sample_len, num_tokens)
+
+        assert sample_len <= max_tokens, (
+            "sentence at index {} of size {} exceeds max_tokens "
+            "limit of {}!".format(idx, sample_len, max_tokens)
+        )
+        num_tokens = (len(batch) + 1) * sample_len
+
+        if _is_batch_full(batch, num_tokens, max_tokens, max_sentences):
+            mod_len = max(
+                bsz_mult * (len(batch) // bsz_mult),
+                len(batch) % bsz_mult,
+            )
+            batches.append(batch[:mod_len])
+            batch = batch[mod_len:]
+            sample_lens = sample_lens[mod_len:]
+            sample_len = max(sample_lens) if len(sample_lens) > 0 else 0
+        batch.append(idx)
+    if len(batch) > 0:
+        batches.append(batch)
+    return batches
+
+
+def build_dataloader(dataset, shuffle, max_tokens=None, max_sentences=None,
+                     required_batch_size_multiple=-1, endless=False, apply_batch_by_size=True, pin_memory=False, use_ddp=False):
+    import torch.distributed as dist
+    devices_cnt = torch.cuda.device_count()
+    if devices_cnt == 0:
+        devices_cnt = 1
+    if not use_ddp:
+        devices_cnt = 1
+    if required_batch_size_multiple == -1:
+        required_batch_size_multiple = devices_cnt
+
+    def shuffle_batches(batches):
+        np.random.shuffle(batches)
+        return batches
+
+    if max_tokens is not None:
+        max_tokens *= devices_cnt
+    if max_sentences is not None:
+        max_sentences *= devices_cnt
+    indices = dataset.ordered_indices()
+    if apply_batch_by_size:
+        batch_sampler = batch_by_size(
+            indices, dataset.num_tokens, max_tokens=max_tokens, max_sentences=max_sentences,
+            required_batch_size_multiple=required_batch_size_multiple,
+        )
+    else:
+        batch_sampler = []
+        for i in range(0, len(indices), max_sentences):
+            batch_sampler.append(indices[i:i + max_sentences])
+
+    if shuffle:
+        batches = shuffle_batches(list(batch_sampler))
+        if endless:
+            batches = [b for _ in range(1000) for b in shuffle_batches(list(batch_sampler))]
+    else:
+        batches = batch_sampler
+        if endless:
+            batches = [b for _ in range(1000) for b in batches]
+    num_workers = dataset.num_workers
+    if use_ddp:
+        num_replicas = dist.get_world_size()
+        rank = dist.get_rank()
+        # batches = [x[rank::num_replicas] for x in batches if len(x) % num_replicas == 0]
+        # ensure that every sample in the dataset is covered
+        batches_ = []
+        for x in batches:
+            if len(x) % num_replicas == 0:
+                batches_.append(x[rank::num_replicas])
+            else:
+                x_ = x + [x[-1]] * (len(x) - len(x) // num_replicas * num_replicas)
+                batches_.append(x_[rank::num_replicas])
+        batches = batches_
+    return torch.utils.data.DataLoader(dataset,
+                                       collate_fn=dataset.collater,
+                                       batch_sampler=batches,
+                                       num_workers=num_workers,
+                                       pin_memory=pin_memory)
+
+
+def unpack_dict_to_list(samples):
+    samples_ = []
+    bsz = samples.get('outputs').size(0)
+    for i in range(bsz):
+        res = {}
+        for k, v in samples.items():
+            try:
+                res[k] = v[i]
+            except:
+                pass
+        samples_.append(res)
+    return samples_
+
+
+def remove_padding(x, padding_idx=0):
+    if x is None:
+        return None
+    assert len(x.shape) in [1, 2]
+    if len(x.shape) == 2:  # [T, H]
+        return x[np.abs(x).sum(-1) != padding_idx]
+    elif len(x.shape) == 1:  # [T]
+        return x[x != padding_idx]
+
+
+def data_loader(fn):
+    """
+    Decorator to make any fx with this use the lazy property
+    :param fn:
+    :return:
+    """
+
+    wraps(fn)
+    attr_name = '_lazy_' + fn.__name__
+
+    def _get_data_loader(self):
+        try:
+            value = getattr(self, attr_name)
+        except AttributeError:
+            try:
+                value = fn(self)  # Lazy evaluation, done only once.
+            except AttributeError as e:
+                # Guard against AttributeError suppression. (Issue #142)
+                traceback.print_exc()
+                error = f'{fn.__name__}: An AttributeError was encountered: ' + str(e)
+                raise RuntimeError(error) from e
+            setattr(self, attr_name, value)  # Memoize evaluation.
+        return value
+
+    return _get_data_loader
+
+
+class BaseDataset(torch.utils.data.Dataset):
+    def __init__(self, shuffle):
+        super().__init__()
+        self.hparams = hparams
+        self.shuffle = shuffle
+        self.sort_by_len = hparams['sort_by_len']
+        self.sizes = None
+
+    @property
+    def _sizes(self):
+        return self.sizes
+
+    def __getitem__(self, index):
+        raise NotImplementedError
+
+    def collater(self, samples):
+        raise NotImplementedError
+
+    def __len__(self):
+        return len(self._sizes)
+
+    def num_tokens(self, index):
+        return self.size(index)
+
+    def size(self, index):
+        """Return an example's size as a float or tuple. This value is used when
+        filtering a dataset with ``--max-positions``."""
+        return min(self._sizes[index], hparams['max_frames'])
+
+    def ordered_indices(self):
+        """Return an ordered list of indices. Batches will be constructed based
+        on this order."""
+        if self.shuffle:
+            indices = np.random.permutation(len(self))
+            if self.sort_by_len:
+                indices = indices[np.argsort(np.array(self._sizes)[indices], kind='mergesort')]
+        else:
+            indices = np.arange(len(self))
+        return indices.tolist()
+
+    @property
+    def num_workers(self):
+        return int(os.getenv('NUM_WORKERS', hparams['ds_workers']))
+
+
+class BaseConcatDataset(ConcatDataset):
+    def collater(self, samples):
+        return self.datasets[0].collater(samples)
+
+    @property
+    def _sizes(self):
+        if not hasattr(self, 'sizes'):
+            self.sizes = list(chain.from_iterable([d._sizes for d in self.datasets]))
+        return self.sizes
+
+    def size(self, index):
+        return min(self._sizes[index], hparams['max_frames'])
+
+    def num_tokens(self, index):
+        return self.size(index)
+
+    def ordered_indices(self):
+        """Return an ordered list of indices. Batches will be constructed based
+        on this order."""
+        if self.datasets[0].shuffle:
+            indices = np.random.permutation(len(self))
+            if self.datasets[0].sort_by_len:
+                indices = indices[np.argsort(np.array(self._sizes)[indices], kind='mergesort')]
+        else:
+            indices = np.arange(len(self))
+        return indices
+
+    @property
+    def num_workers(self):
+        return self.datasets[0].num_workers

preprocess/tools/note_transcription/utils/commons/ddp_utils.py

@@ -0,0 +1,164 @@
+from torch.nn.parallel import DistributedDataParallel
+from torch.nn.parallel.distributed import _find_tensors
+import torch.optim
+import torch.utils.data
+import torch
+from packaging import version
+
+class DDP(DistributedDataParallel):
+    """
+    Override the forward call in lightning so it goes to training and validation step respectively
+    """
+
+    def forward(self, *inputs, **kwargs):  # pragma: no cover
+        # if version.parse(torch.__version__[:6]) < version.parse("1.11"):
+        if version.parse(torch.__version__) < version.parse("1.11"):    # fix the hard [:6] problem
+            self._sync_params()
+            inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
+            assert len(self.device_ids) == 1
+            if self.module.training:
+                output = self.module.training_step(*inputs[0], **kwargs[0])
+            elif self.module.testing:
+                output = self.module.test_step(*inputs[0], **kwargs[0])
+            else:
+                output = self.module.validation_step(*inputs[0], **kwargs[0])
+            if torch.is_grad_enabled():
+                # We'll return the output object verbatim since it is a freeform
+                # object. We need to find any tensors in this object, though,
+                # because we need to figure out which parameters were used during
+                # this forward pass, to ensure we short circuit reduction for any
+                # unused parameters. Only if `find_unused_parameters` is set.
+                if self.find_unused_parameters:
+                    self.reducer.prepare_for_backward(list(_find_tensors(output)))
+                else:
+                    self.reducer.prepare_for_backward([])
+        elif version.parse("1.11") <= version.parse(torch.__version__) < version.parse("2.0"):
+            from torch.nn.parallel.distributed import \
+                logging, Join, _DDPSink, _tree_flatten_with_rref, _tree_unflatten_with_rref
+            with torch.autograd.profiler.record_function("DistributedDataParallel.forward"):
+                if torch.is_grad_enabled() and self.require_backward_grad_sync:
+                    self.logger.set_runtime_stats_and_log()
+                    self.num_iterations += 1
+                    self.reducer.prepare_for_forward()
+
+                # Notify the join context that this process has not joined, if
+                # needed
+                work = Join.notify_join_context(self)
+                if work:
+                    self.reducer._set_forward_pass_work_handle(
+                        work, self._divide_by_initial_world_size
+                    )
+
+                # Calling _rebuild_buckets before forward compuation,
+                # It may allocate new buckets before deallocating old buckets
+                # inside _rebuild_buckets. To save peak memory usage,
+                # call _rebuild_buckets before the peak memory usage increases
+                # during forward computation.
+                # This should be called only once during whole training period.
+                if torch.is_grad_enabled() and self.reducer._rebuild_buckets():
+                    logging.info("Reducer buckets have been rebuilt in this iteration.")
+                    self._has_rebuilt_buckets = True
+
+                # sync params according to location (before/after forward) user
+                # specified as part of hook, if hook was specified.
+                buffer_hook_registered = hasattr(self, 'buffer_hook')
+                if self._check_sync_bufs_pre_fwd():
+                    self._sync_buffers()
+
+                if self._join_config.enable:
+                    # Notify joined ranks whether they should sync in backwards pass or not.
+                    self._check_global_requires_backward_grad_sync(is_joined_rank=False)
+
+                # modified part
+                inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)
+                if self.module.training:
+                    output = self.module.training_step(*inputs[0], **kwargs[0])
+                elif self.module.testing:
+                    output = self.module.test_step(*inputs[0], **kwargs[0])
+                else:
+                    output = self.module.validation_step(*inputs[0], **kwargs[0])
+
+                # sync params according to location (before/after forward) user
+                # specified as part of hook, if hook was specified.
+                if self._check_sync_bufs_post_fwd():
+                    self._sync_buffers()
+
+                if torch.is_grad_enabled() and self.require_backward_grad_sync:
+                    self.require_forward_param_sync = True
+                    # We'll return the output object verbatim since it is a freeform
+                    # object. We need to find any tensors in this object, though,
+                    # because we need to figure out which parameters were used during
+                    # this forward pass, to ensure we short circuit reduction for any
+                    # unused parameters. Only if `find_unused_parameters` is set.
+                    if self.find_unused_parameters and not self.static_graph:
+                        # Do not need to populate this for static graph.
+                        self.reducer.prepare_for_backward(list(_find_tensors(output)))
+                    else:
+                        self.reducer.prepare_for_backward([])
+                else:
+                    self.require_forward_param_sync = False
+
+            # TODO: DDPSink is currently enabled for unused parameter detection and
+            # static graph training for first iteration.
+            if (self.find_unused_parameters and not self.static_graph) or (
+                    self.static_graph and self.num_iterations == 1
+            ):
+                state_dict = {
+                    'static_graph': self.static_graph,
+                    'num_iterations': self.num_iterations,
+                }
+
+                output_tensor_list, treespec, output_is_rref = _tree_flatten_with_rref(
+                    output
+                )
+                output_placeholders = [None for _ in range(len(output_tensor_list))]
+                # Do not touch tensors that have no grad_fn, which can cause issues
+                # such as https://github.com/pytorch/pytorch/issues/60733
+                for i, output in enumerate(output_tensor_list):
+                    if torch.is_tensor(output) and output.grad_fn is None:
+                        output_placeholders[i] = output
+
+                # When find_unused_parameters=True, makes tensors which require grad
+                # run through the DDPSink backward pass. When not all outputs are
+                # used in loss, this makes those corresponding tensors receive
+                # undefined gradient which the reducer then handles to ensure
+                # param.grad field is not touched and we don't error out.
+                passthrough_tensor_list = _DDPSink.apply(
+                    self.reducer,
+                    state_dict,
+                    *output_tensor_list,
+                )
+                for i in range(len(output_placeholders)):
+                    if output_placeholders[i] is None:
+                        output_placeholders[i] = passthrough_tensor_list[i]
+
+                # Reconstruct output data structure.
+                output = _tree_unflatten_with_rref(
+                    output_placeholders, treespec, output_is_rref
+                )
+        else:
+            # now pytorch version >= 2.0
+            with torch.autograd.profiler.record_function("DistributedDataParallel.forward"):
+                inputs, kwargs = self._pre_forward(*inputs, **kwargs)
+                output = (
+                    # self.module.forward(*inputs, **kwargs)
+                    # if self._delay_all_reduce_all_params
+                    # else self._run_ddp_forward(*inputs, **kwargs)
+                    # modified: delete 'delay_all_reduce_named_params' function
+                    self._run_ddp_forward(*inputs, **kwargs)
+                )
+                return self._post_forward(output)
+        return output
+
+    def _run_ddp_forward(self, *inputs, **kwargs):
+        if version.parse(torch.__version__) >= version.parse("2.0"):
+            with self._inside_ddp_forward():
+                if self.module.training:
+                    output = self.module.training_step(*inputs, **kwargs)
+                elif self.module.testing:
+                    output = self.module.test_step(*inputs, **kwargs)
+                else:
+                    output = self.module.validation_step(*inputs, **kwargs)
+                return output  # type: ignore[index]
+        else:
+            return super(DDP, self)._run_ddp_forward(*inputs, **kwargs)

preprocess/tools/note_transcription/utils/commons/gpu_mem_track.py

@@ -0,0 +1,113 @@
+import gc
+import datetime
+import inspect
+
+import torch
+import numpy as np
+
+dtype_memory_size_dict = {
+    torch.float64: 64/8,
+    torch.double: 64/8,
+    torch.float32: 32/8,
+    torch.float: 32/8,
+    torch.float16: 16/8,
+    torch.half: 16/8,
+    torch.int64: 64/8,
+    torch.long: 64/8,
+    torch.int32: 32/8,
+    torch.int: 32/8,
+    torch.int16: 16/8,
+    torch.short: 16/6,
+    torch.uint8: 8/8,
+    torch.int8: 8/8,
+}
+# compatibility of torch1.0
+if getattr(torch, "bfloat16", None) is not None:
+    dtype_memory_size_dict[torch.bfloat16] = 16/8
+if getattr(torch, "bool", None) is not None:
+    dtype_memory_size_dict[torch.bool] = 8/8 # pytorch use 1 byte for a bool, see https://github.com/pytorch/pytorch/issues/41571
+
+def get_mem_space(x):
+    try:
+        ret = dtype_memory_size_dict[x]
+    except KeyError:
+        print(f"dtype {x} is not supported!")
+    return ret
+
+class MemTracker(object):
+    """
+    Class used to track pytorch memory usage
+    Arguments:
+        detail(bool, default True): whether the function shows the detail gpu memory usage
+        path(str): where to save log file
+        verbose(bool, default False): whether show the trivial exception
+        device(int): GPU number, default is 0
+    """
+    def __init__(self, detail=True, path='', verbose=False, device=0):
+        self.print_detail = detail
+        self.last_tensor_sizes = set()
+        self.gpu_profile_fn = path + f'{datetime.datetime.now():%d-%b-%y-%H:%M:%S}-gpu_mem_track.txt'
+        self.verbose = verbose
+        self.begin = True
+        self.device = device
+
+    def get_tensors(self):
+        for obj in gc.get_objects():
+            try:
+                if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
+                    tensor = obj
+                else:
+                    continue
+                if tensor.is_cuda:
+                    yield tensor
+            except Exception as e:
+                if self.verbose:
+                    print('A trivial exception occured: {}'.format(e))
+
+    def get_tensor_usage(self):
+        sizes = [np.prod(np.array(tensor.size())) * get_mem_space(tensor.dtype) for tensor in self.get_tensors()]
+        return np.sum(sizes) / 1024**2
+
+    def get_allocate_usage(self):
+        return torch.cuda.memory_allocated() / 1024**2
+
+    def clear_cache(self):
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def print_all_gpu_tensor(self, file=None):
+        for x in self.get_tensors():
+            print(x.size(), x.dtype, np.prod(np.array(x.size()))*get_mem_space(x.dtype)/1024**2, file=file)
+
+    def track(self):
+        """
+        Track the GPU memory usage
+        """
+        frameinfo = inspect.stack()[1]
+        where_str = frameinfo.filename + ' line ' + str(frameinfo.lineno) + ': ' + frameinfo.function
+
+        with open(self.gpu_profile_fn, 'a+') as f:
+
+            if self.begin:
+                f.write(f"GPU Memory Track | {datetime.datetime.now():%d-%b-%y-%H:%M:%S} |"
+                        f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb"
+                        f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n")
+                self.begin = False
+
+            if self.print_detail is True:
+                ts_list = [(tensor.size(), tensor.dtype) for tensor in self.get_tensors()]
+                new_tensor_sizes = {(type(x),
+                                    tuple(x.size()),
+                                    ts_list.count((x.size(), x.dtype)),
+                                    np.prod(np.array(x.size()))*get_mem_space(x.dtype)/1024**2,
+                                    x.dtype) for x in self.get_tensors()}
+                for t, s, n, m, data_type in new_tensor_sizes - self.last_tensor_sizes:
+                    f.write(f'+ | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n')
+                for t, s, n, m, data_type in self.last_tensor_sizes - new_tensor_sizes:
+                    f.write(f'- | {str(n)} * Size:{str(s):<20} | Memory: {str(m*n)[:6]} M | {str(t):<20} | {data_type}\n')
+
+                self.last_tensor_sizes = new_tensor_sizes
+
+            f.write(f"\nAt {where_str:<50}"
+                    f" Total Tensor Used Memory:{self.get_tensor_usage():<7.1f}Mb"
+                    f" Total Allocated Memory:{self.get_allocate_usage():<7.1f}Mb\n\n")

preprocess/tools/note_transcription/utils/commons/hparams.py

@@ -0,0 +1,131 @@
+import argparse
+import os
+import yaml
+
+global_print_hparams = True
+hparams = {}
+
+
+class Args:
+    def __init__(self, **kwargs):
+        for k, v in kwargs.items():
+            self.__setattr__(k, v)
+
+
+def override_config(old_config: dict, new_config: dict):
+    for k, v in new_config.items():
+        if isinstance(v, dict) and k in old_config:
+            override_config(old_config[k], new_config[k])
+        else:
+            old_config[k] = v
+
+
+def set_hparams(config='', exp_name='', hparams_str='', print_hparams=True, global_hparams=True, root_dir=''):
+    if config == '' and exp_name == '':
+        parser = argparse.ArgumentParser(description='')
+        parser.add_argument('--config', type=str, default='',
+                            help='location of the data corpus')
+        parser.add_argument('--exp_name', type=str, default='', help='exp_name')
+        parser.add_argument('-hp', '--hparams', type=str, default='',
+                            help='location of the data corpus')
+        parser.add_argument('--infer', action='store_true', help='infer')
+        parser.add_argument('--validate', action='store_true', help='validate')
+        parser.add_argument('--reset', action='store_true', help='reset hparams')
+        parser.add_argument('--remove', action='store_true', help='remove old ckpt')
+        parser.add_argument('--debug', action='store_true', help='debug')
+        parser.add_argument('--root_dir', type=str, default='', help='root directory of the project.')
+        args, unknown = parser.parse_known_args()
+        print("| Unknow hparams: ", unknown)
+    else:
+        args = Args(config=config, exp_name=exp_name, hparams=hparams_str,
+                    infer=False, validate=False, reset=False, debug=False, remove=False, root_dir=root_dir)
+    global hparams
+    assert args.config != '' or args.exp_name != ''
+    root_dir = args.root_dir
+    if args.config != '':
+        assert os.path.exists(os.path.join(root_dir, args.config)), f'| Wrong config path! root_dir: {root_dir}, config_path: {args.config}'
+
+    config_chains = []
+    loaded_config = set()
+
+    def load_config(config_fn):
+        # deep first inheritance and avoid the second visit of one node
+        if not os.path.exists(os.path.join(root_dir, config_fn)):
+            return {}
+        with open(os.path.join(root_dir, config_fn)) as f:
+            hparams_ = yaml.safe_load(f)
+        loaded_config.add(config_fn)
+        if 'base_config' in hparams_:
+            ret_hparams = {}
+            if not isinstance(hparams_['base_config'], list):
+                hparams_['base_config'] = [hparams_['base_config']]
+            for c in hparams_['base_config']:
+                if c.startswith('.'):
+                    c = f'{os.path.dirname(config_fn)}/{c}'
+                    c = os.path.normpath(c)
+                if c not in loaded_config:
+                    override_config(ret_hparams, load_config(c))
+            override_config(ret_hparams, hparams_)
+        else:
+            ret_hparams = hparams_
+        config_chains.append(config_fn)
+        return ret_hparams
+
+    saved_hparams = {}
+    args_work_dir = ''
+    if args.exp_name != '':
+        args_work_dir = os.path.join(root_dir, f'checkpoints/{args.exp_name}')
+        ckpt_config_path = f'{args_work_dir}/config.yaml'
+        if os.path.exists(ckpt_config_path):
+            with open(ckpt_config_path) as f:
+                saved_hparams_ = yaml.safe_load(f)
+                if saved_hparams_ is not None:
+                    saved_hparams.update(saved_hparams_)
+    hparams_ = {}
+    if args.config != '':
+        hparams_.update(load_config(args.config))
+    if not args.reset:
+        hparams_.update(saved_hparams)
+    hparams_['work_dir'] = args_work_dir
+
+    # Support config overriding in command line. Support list type config overriding.
+    # Examples: --hparams="a=1,b.c=2,d=[1 1 1]"
+    if args.hparams != "":
+        for new_hparam in args.hparams.split(","):
+            k, v = new_hparam.split("=")
+            v = v.strip("\'\" ")
+            config_node = hparams_
+            for k_ in k.split(".")[:-1]:
+                config_node = config_node[k_]
+            k = k.split(".")[-1]
+            if v in ['True', 'False'] or type(config_node[k]) in [bool, list, dict]:
+                if type(config_node[k]) == list:
+                    v = v.replace(" ", ",")
+                config_node[k] = eval(v)
+            else:
+                config_node[k] = type(config_node[k])(v)
+    if args_work_dir != '' and args.remove:
+        answer = input("REMOVE old checkpoint? Y/N [Default: N]: ")
+        if answer.lower() == "y":
+            pass
+    if args_work_dir != '' and (not os.path.exists(ckpt_config_path) or args.reset) and not args.infer:
+        os.makedirs(hparams_['work_dir'], exist_ok=True)
+        with open(ckpt_config_path, 'w') as f:
+            yaml.safe_dump(hparams_, f)
+
+    hparams_['infer'] = args.infer
+    hparams_['debug'] = args.debug
+    hparams_['validate'] = args.validate
+    hparams_['exp_name'] = args.exp_name
+    global global_print_hparams
+    if global_hparams:
+        hparams.clear()
+        hparams.update(hparams_)
+    if print_hparams and global_print_hparams and global_hparams:
+        # print('| Hparams chains: ', config_chains)
+        # print('| Hparams: ')
+        # for i, (k, v) in enumerate(sorted(hparams_.items())):
+        #     print(f"\033[;33;m{k}\033[0m: {v}, ", end="\n" if i % 5 == 4 else "")
+        # print("")
+        global_print_hparams = False
+    return hparams_

preprocess/tools/note_transcription/utils/commons/indexed_datasets.py

@@ -0,0 +1,71 @@
+import pickle
+from copy import deepcopy
+
+import numpy as np
+
+
+class IndexedDataset:
+    def __init__(self, path, num_cache=1):
+        super().__init__()
+        self.path = path
+        self.data_file = None
+        self.data_offsets = np.load(f"{path}.idx", allow_pickle=True).item()['offsets']
+        self.data_file = open(f"{path}.data", 'rb', buffering=-1)
+        self.cache = []
+        self.num_cache = num_cache
+
+    def check_index(self, i):
+        if i < 0 or i >= len(self.data_offsets) - 1:
+            raise IndexError('index out of range')
+
+    def __del__(self):
+        if self.data_file:
+            self.data_file.close()
+
+    def __getitem__(self, i):
+        self.check_index(i)
+        if self.num_cache > 0:
+            for c in self.cache:
+                if c[0] == i:
+                    return c[1]
+        self.data_file.seek(self.data_offsets[i])
+        b = self.data_file.read(self.data_offsets[i + 1] - self.data_offsets[i])
+        item = pickle.loads(b)
+        if self.num_cache > 0:
+            self.cache = [(i, deepcopy(item))] + self.cache[:-1]
+        return item
+
+    def __len__(self):
+        return len(self.data_offsets) - 1
+
+class IndexedDatasetBuilder:
+    def __init__(self, path):
+        self.path = path
+        self.out_file = open(f"{path}.data", 'wb')
+        self.byte_offsets = [0]
+
+    def add_item(self, item):
+        s = pickle.dumps(item)
+        bytes = self.out_file.write(s)
+        self.byte_offsets.append(self.byte_offsets[-1] + bytes)
+
+    def finalize(self):
+        self.out_file.close()
+        np.save(open(f"{self.path}.idx", 'wb'), {'offsets': self.byte_offsets})
+
+
+if __name__ == "__main__":
+    import random
+    from tqdm import tqdm
+    ds_path = '/tmp/indexed_ds_example'
+    size = 100
+    items = [{"a": np.random.normal(size=[10000, 10]),
+              "b": np.random.normal(size=[10000, 10])} for i in range(size)]
+    builder = IndexedDatasetBuilder(ds_path)
+    for i in tqdm(range(size)):
+        builder.add_item(items[i])
+    builder.finalize()
+    ds = IndexedDataset(ds_path)
+    for i in tqdm(range(10000)):
+        idx = random.randint(0, size - 1)
+        assert (ds[idx]['a'] == items[idx]['a']).all()

preprocess/tools/note_transcription/utils/commons/losses.py

@@ -0,0 +1,53 @@
+import torch
+import torch.nn.functional as F
+
+def sigmoid_focal_loss(
+    inputs: torch.Tensor,
+    targets: torch.Tensor,
+    alpha: float = 0.25,
+    gamma: float = 2,
+    reduction: str = "none",
+) -> torch.Tensor:
+    """
+    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
+
+    Args:
+        inputs (Tensor): A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets (Tensor): A float tensor with the same shape as inputs. Stores the binary
+                classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+        alpha (float): Weighting factor in range (0,1) to balance
+                positive vs negative examples or -1 for ignore. Default: ``0.25``.
+        gamma (float): Exponent of the modulating factor (1 - p_t) to
+                balance easy vs hard examples. Default: ``2``.
+        reduction (string): ``'none'`` | ``'mean'`` | ``'sum'``
+                ``'none'``: No reduction will be applied to the output.
+                ``'mean'``: The output will be averaged.
+                ``'sum'``: The output will be summed. Default: ``'none'``.
+    Returns:
+        Loss tensor with the reduction option applied.
+    """
+    # Original implementation from https://github.com/facebookresearch/fvcore/blob/master/fvcore/nn/focal_loss.py
+    p = torch.sigmoid(inputs)
+    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
+    p_t = p * targets + (1 - p) * (1 - targets)
+    loss = ce_loss * ((1 - p_t) ** gamma)
+
+    if alpha >= 0:  # decrease the importance of negative samples
+        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
+        loss = alpha_t * loss
+
+    # Check reduction option and return loss accordingly
+    if reduction == "none":
+        pass
+    elif reduction == "mean":
+        loss = loss.mean()
+    elif reduction == "sum":
+        loss = loss.sum()
+    else:
+        raise ValueError(
+            f"Invalid Value for arg 'reduction': '{reduction} \n Supported reduction modes: 'none', 'mean', 'sum'"
+        )
+    return loss
+

preprocess/tools/note_transcription/utils/commons/meters.py

@@ -0,0 +1,42 @@
+import time
+import torch
+
+
+class AvgrageMeter(object):
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.avg = 0
+        self.sum = 0
+        self.cnt = 0
+
+    def update(self, val, n=1):
+        self.sum += val * n
+        self.cnt += n
+        self.avg = self.sum / self.cnt
+
+
+class Timer:
+    timer_map = {}
+
+    def __init__(self, name, enable=False):
+        if name not in Timer.timer_map:
+            Timer.timer_map[name] = 0
+        self.name = name
+        self.enable = enable
+
+    def __enter__(self):
+        if self.enable:
+            if torch.cuda.is_available():
+                torch.cuda.synchronize()
+            self.t = time.time()
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self.enable:
+            if torch.cuda.is_available():
+                torch.cuda.synchronize()
+            Timer.timer_map[self.name] += time.time() - self.t
+            if self.enable:
+                print(f'[Timer] {self.name}: {Timer.timer_map[self.name]}')

preprocess/tools/note_transcription/utils/commons/multiprocess_utils.py

@@ -0,0 +1,180 @@
+import os
+import traceback
+from functools import partial
+from tqdm import tqdm
+import torch
+
+def chunked_worker(worker_id, args_queue=None, results_queue=None, init_ctx_func=None):
+    ctx = init_ctx_func(worker_id) if init_ctx_func is not None else None
+    while True:
+        args = args_queue.get()
+        if args == '<KILL>':
+            return
+        job_idx, map_func, arg = args
+        try:
+            map_func_ = partial(map_func, ctx=ctx) if ctx is not None else map_func
+            if isinstance(arg, dict):
+                res = map_func_(**arg)
+            elif isinstance(arg, (list, tuple)):
+                res = map_func_(*arg)
+            else:
+                res = map_func_(arg)
+            results_queue.put((job_idx, res))
+        except:
+            traceback.print_exc()
+            results_queue.put((job_idx, None))
+
+
+class MultiprocessManager:
+    def __init__(self, num_workers=None, init_ctx_func=None, multithread=False, queue_max=-1):
+        if multithread:
+            from multiprocessing.dummy import Queue, Process
+        else:
+            from multiprocessing import Queue, Process
+        if num_workers is None:
+            num_workers = int(os.getenv('N_PROC', os.cpu_count()))
+        self.num_workers = num_workers
+        self.results_queue = Queue(maxsize=-1)
+        self.jobs_pending = []
+        self.args_queue = Queue(maxsize=queue_max)
+        self.workers = []
+        self.total_jobs = 0
+        self.multithread = multithread
+        for i in range(num_workers):
+            if multithread:
+                p = Process(target=chunked_worker,
+                            args=(i, self.args_queue, self.results_queue, init_ctx_func))
+            else:
+                p = Process(target=chunked_worker,
+                            args=(i, self.args_queue, self.results_queue, init_ctx_func),
+                            daemon=True)
+            self.workers.append(p)
+            p.start()
+
+    def add_job(self, func, args):
+        if not self.args_queue.full():
+            self.args_queue.put((self.total_jobs, func, args))
+        else:
+            self.jobs_pending.append((self.total_jobs, func, args))
+        self.total_jobs += 1
+
+    def get_results(self):
+        self.n_finished = 0
+        while self.n_finished < self.total_jobs:
+            while len(self.jobs_pending) > 0 and not self.args_queue.full():
+                self.args_queue.put(self.jobs_pending[0])
+                self.jobs_pending = self.jobs_pending[1:]
+            job_id, res = self.results_queue.get()
+            yield job_id, res
+            self.n_finished += 1
+        for w in range(self.num_workers):
+            self.args_queue.put("<KILL>")
+        for w in self.workers:
+            w.join()
+
+    def close(self):
+        if not self.multithread:
+            for w in self.workers:
+                w.terminate()
+
+    def __len__(self):
+        return self.total_jobs
+
+
+def multiprocess_run_tqdm(map_func, args, num_workers=None, ordered=True, init_ctx_func=None,
+                          multithread=False, queue_max=-1, desc=None):
+    for i, res in tqdm(
+            multiprocess_run(map_func, args, num_workers, ordered, init_ctx_func, multithread,
+                             queue_max=queue_max),
+            total=len(args), desc=desc):
+        yield i, res
+
+
+def multiprocess_run(map_func, args, num_workers=None, ordered=True, init_ctx_func=None, multithread=False,
+                     queue_max=-1):
+    """
+    Multiprocessing running chunked jobs.
+
+    Examples:
+    >>> for res in tqdm(multiprocess_run(job_func, args):
+    >>>     print(res)
+
+    :param map_func:
+    :param args:
+    :param num_workers:
+    :param ordered:
+    :param init_ctx_func:
+    :param q_max_size:
+    :param multithread:
+    :return:
+    """
+    if num_workers is None:
+        num_workers = int(os.getenv('N_PROC', os.cpu_count()))
+    manager = MultiprocessManager(num_workers, init_ctx_func, multithread, queue_max=queue_max)
+    for arg in args:
+        manager.add_job(map_func, arg)
+    if ordered:
+        n_jobs = len(args)
+        results = ['<WAIT>' for _ in range(n_jobs)]
+        i_now = 0
+        for job_i, res in manager.get_results():
+            results[job_i] = res
+            while i_now < n_jobs and (not isinstance(results[i_now], str) or results[i_now] != '<WAIT>'):
+                yield i_now, results[i_now]
+                results[i_now] = None
+                i_now += 1
+    else:
+        for job_i, res in manager.get_results():
+            yield job_i, res
+    manager.close()
+
+# #### this is the old version of chunked_multiprocess_run
+def chunked_worker_old(worker_id, map_func, args, results_queue=None, init_ctx_func=None):
+    ctx = init_ctx_func(worker_id) if init_ctx_func is not None else None
+    for job_idx, arg in args:
+        try:
+            if not isinstance(arg, tuple) and not isinstance(arg, list):
+                arg = [arg]
+            if ctx is not None:
+                res = map_func(*arg, ctx=ctx)
+            else:
+                res = map_func(*arg)
+            results_queue.put((job_idx, res))
+        except:
+            traceback.print_exc()
+            results_queue.put((job_idx, None))
+
+def chunked_multiprocess_run(
+        map_func, args, num_workers=None, ordered=True,
+        init_ctx_func=None, q_max_size=1000, multithread=False):
+    if multithread:
+        from multiprocessing.dummy import Queue, Process
+    else:
+        from multiprocessing import Queue, Process
+    args = zip(range(len(args)), args)
+    args = list(args)
+    n_jobs = len(args)
+    if num_workers is None:
+        num_workers = int(os.getenv('N_PROC', os.cpu_count()))
+    results_queues = []
+    if ordered:
+        for i in range(num_workers):
+            results_queues.append(Queue(maxsize=q_max_size // num_workers))
+    else:
+        results_queue = Queue(maxsize=q_max_size)
+        for i in range(num_workers):
+            results_queues.append(results_queue)
+    workers = []
+    for i in range(num_workers):
+        args_worker = args[i::num_workers]
+        p = Process(target=chunked_worker_old, args=(
+            i, map_func, args_worker, results_queues[i], init_ctx_func), daemon=True)
+        workers.append(p)
+        p.start()
+    for n_finished in range(n_jobs):
+        results_queue = results_queues[n_finished % num_workers]
+        job_idx, res = results_queue.get()
+        assert job_idx == n_finished or not ordered, (job_idx, n_finished)
+        yield res
+    for w in workers:
+        w.join()

preprocess/tools/note_transcription/utils/commons/signal.py

@@ -0,0 +1,74 @@
+import numpy as np
+import torch
+import torch.nn as nn
+
+def get_filter_2d(kernel, kernel_size, channels, no_grad=True):
+    # Reshape to 2d depthwise convolutional weight
+    kernel = kernel.view(1, 1, kernel_size, kernel_size)
+    kernel = kernel.repeat(channels, 1, 1, 1)
+
+    filter = nn.Conv2d(in_channels=channels, out_channels=channels, kernel_size=kernel_size, groups=channels,
+                       bias=False, padding=kernel_size // 2)
+
+    filter.weight.data = kernel
+    if no_grad:
+        filter.weight.requires_grad = False
+
+    return filter
+
+def get_filter_1d(kernel, kernel_size, channels, no_grad=True):
+    kernel = kernel.view(1, 1, kernel_size)
+    kernel = kernel.repeat(channels, 1, 1)
+
+    filter = nn.Conv1d(in_channels=channels, out_channels=channels, kernel_size=kernel_size, groups=channels,
+                       bias=False, padding=kernel_size // 2)
+
+    filter.weight.data = kernel
+    if no_grad:
+        filter.weight.requires_grad = False
+
+    return filter
+
+def get_gaussian_kernel_2d(kernel_size, sigma):
+    # Create a x, y coordinate grid of shape (kernel_size, kernel_size, 2)
+    x_coord = torch.arange(kernel_size)
+    x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size)
+    y_grid = x_grid.t()
+    xy_grid = torch.stack([x_grid, y_grid], dim=-1).float()
+
+    mean = (kernel_size - 1) / 2.
+    variance = sigma ** 2.
+
+    # Calculate the 2-dimensional gaussian kernel which is
+    # the product of two gaussian distributions for two different
+    # variables (in this case called x and y)
+    gaussian_kernel = (1. / (2. * np.pi * variance)) * torch.exp(
+        -torch.sum((xy_grid - mean) ** 2., dim=-1) / (2 * variance))
+
+    # Make sure sum of values in gaussian kernel equals 1.
+    gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
+
+    return gaussian_kernel
+
+def get_gaussian_kernel_1d(kernel_size, sigma):
+    x_grid = torch.arange(kernel_size)
+    mean = (kernel_size - 1) / 2.
+    variance = sigma ** 2.
+    gaussian_kernel = (1. / ((2. * np.pi) ** 0.5 * sigma)) * torch.exp(-(x_grid - mean) ** 2. / (2 * variance))
+    gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
+    return gaussian_kernel
+
+def get_hann_kernel_1d(kernel_size, periodic=False):
+    # periodic=False gives symmetric kernel, otherwise equivalent to hann(kernel_size + 1)
+    return torch.hann_window(kernel_size, periodic)
+
+def get_triangle_kernel_1d(kernel_size):
+    kernel = torch.zeros(kernel_size)
+    for idx in range(kernel_size):
+        kernel[idx] = 1 - abs((idx - (kernel_size - 1) / 2) / ((kernel_size - 1) / 2))
+    return kernel
+
+def add_gaussian_noise(tensor, mean=0, std=1):
+    noise = torch.randn(tensor.size()) * std + mean
+    noisy_tensor = tensor + noise
+    return noisy_tensor

preprocess/tools/note_transcription/utils/commons/single_thread_env.py

@@ -0,0 +1,5 @@
+import os
+
+os.environ["OMP_NUM_THREADS"] = "1"
+os.environ['TF_NUM_INTEROP_THREADS'] = '1'
+os.environ['TF_NUM_INTRAOP_THREADS'] = '1'

preprocess/tools/note_transcription/utils/commons/tensor_utils.py

@@ -0,0 +1,92 @@
+import torch
+import torch.distributed as dist
+
+
+def reduce_tensors(metrics):
+    new_metrics = {}
+    for k, v in metrics.items():
+        if isinstance(v, torch.Tensor):
+            dist.all_reduce(v)
+            v = v / dist.get_world_size()
+        if type(v) is dict:
+            v = reduce_tensors(v)
+        new_metrics[k] = v
+    return new_metrics
+
+
+def tensors_to_scalars(tensors):
+    if isinstance(tensors, torch.Tensor):
+        tensors = tensors.item()
+        return tensors
+    elif isinstance(tensors, dict):
+        new_tensors = {}
+        for k, v in tensors.items():
+            v = tensors_to_scalars(v)
+            new_tensors[k] = v
+        return new_tensors
+    elif isinstance(tensors, list):
+        return [tensors_to_scalars(v) for v in tensors]
+    else:
+        return tensors
+
+
+def tensors_to_np(tensors):
+    if isinstance(tensors, dict):
+        new_np = {}
+        for k, v in tensors.items():
+            if isinstance(v, torch.Tensor):
+                v = v.cpu().numpy()
+            if type(v) is dict:
+                v = tensors_to_np(v)
+            new_np[k] = v
+    elif isinstance(tensors, list):
+        new_np = []
+        for v in tensors:
+            if isinstance(v, torch.Tensor):
+                v = v.cpu().numpy()
+            if type(v) is dict:
+                v = tensors_to_np(v)
+            new_np.append(v)
+    elif isinstance(tensors, torch.Tensor):
+        v = tensors
+        if isinstance(v, torch.Tensor):
+            v = v.cpu().numpy()
+        if type(v) is dict:
+            v = tensors_to_np(v)
+        new_np = v
+    else:
+        raise Exception(f'tensors_to_np does not support type {type(tensors)}.')
+    return new_np
+
+
+def move_to_cpu(tensors):
+    ret = {}
+    for k, v in tensors.items():
+        if isinstance(v, torch.Tensor):
+            v = v.cpu()
+        if type(v) is dict:
+            v = move_to_cpu(v)
+        ret[k] = v
+    return ret
+
+
+def move_to_cuda(batch, gpu_id=0):
+    # base case: object can be directly moved using `cuda` or `to`
+    if callable(getattr(batch, 'cuda', None)):
+        return batch.cuda(gpu_id, non_blocking=True)
+    elif callable(getattr(batch, 'to', None)):
+        return batch.to(torch.device('cuda', gpu_id), non_blocking=True)
+    elif isinstance(batch, list):
+        for i, x in enumerate(batch):
+            batch[i] = move_to_cuda(x, gpu_id)
+        return batch
+    elif isinstance(batch, tuple):
+        batch = list(batch)
+        for i, x in enumerate(batch):
+            batch[i] = move_to_cuda(x, gpu_id)
+        return tuple(batch)
+    elif isinstance(batch, dict):
+        for k, v in batch.items():
+            batch[k] = move_to_cuda(v, gpu_id)
+        return batch
+    return batch

preprocess/tools/note_transcription/utils/commons/trainer.py

@@ -0,0 +1,557 @@
+import random
+import subprocess
+import traceback
+from datetime import datetime
+
+from torch.cuda.amp import GradScaler, autocast
+import numpy as np
+import torch.optim
+import torch.utils.data
+import copy
+import logging
+import os
+import re
+import sys
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+import tqdm
+
+from .ckpt_utils import get_last_checkpoint, get_all_ckpts
+from .ddp_utils import DDP
+from .hparams import hparams
+from .tensor_utils import move_to_cuda
+
+
+class Tee(object):
+    def __init__(self, name, mode):
+        self.file = open(name, mode)
+        self.stdout = sys.stdout
+        sys.stdout = self
+
+    def __del__(self):
+        sys.stdout = self.stdout
+        self.file.close()
+
+    def write(self, data):
+        self.file.write(data)
+        self.stdout.write(data)
+
+    def flush(self):
+        self.file.flush()
+
+
+class Trainer:
+    def __init__(
+            self,
+            work_dir,
+            default_save_path=None,
+            accumulate_grad_batches=1,
+            max_updates=160000,
+            print_nan_grads=False,
+            val_check_interval=2000,
+            num_sanity_val_steps=5,
+            amp=False,
+            # tb logger
+            log_save_interval=100,
+            tb_log_interval=10,
+            # checkpoint
+            monitor_key='val_loss',
+            monitor_mode='min',
+            num_ckpt_keep=5,
+            save_best=True,
+            resume_from_checkpoint=0,
+            seed=1234,
+            debug=False,
+    ):
+        os.makedirs(work_dir, exist_ok=True)
+        self.work_dir = work_dir
+        self.accumulate_grad_batches = accumulate_grad_batches
+        self.max_updates = max_updates
+        self.num_sanity_val_steps = num_sanity_val_steps
+        self.print_nan_grads = print_nan_grads
+        self.default_save_path = default_save_path
+        self.resume_from_checkpoint = resume_from_checkpoint if resume_from_checkpoint > 0 else None
+        self.seed = seed
+        self.debug = debug
+        # model and optm
+        self.task = None
+        self.optimizers = []
+
+        # trainer state
+        self.testing = False
+        self.global_step = 0
+        self.current_epoch = 0
+        self.total_batches = 0
+
+        # configure checkpoint
+        self.monitor_key = monitor_key
+        self.num_ckpt_keep = num_ckpt_keep
+        self.save_best = save_best
+        self.monitor_op = np.less if monitor_mode == 'min' else np.greater
+        self.best_val_results = np.Inf if monitor_mode == 'min' else -np.Inf
+        self.mode = 'min'
+
+        # allow int, string and gpu list
+        self.all_gpu_ids = [
+            int(x) for x in os.environ.get("CUDA_VISIBLE_DEVICES", "").split(",") if x != '']
+        self.num_gpus = len(self.all_gpu_ids)
+        self.on_gpu = self.num_gpus > 0
+        self.root_gpu = 0
+        logging.info(f'GPU available: {torch.cuda.is_available()}, GPU used: {self.all_gpu_ids}')
+        self.use_ddp = self.num_gpus > 1
+        self.proc_rank = 0
+        # Tensorboard logging
+        self.log_save_interval = log_save_interval
+        self.val_check_interval = val_check_interval
+        self.tb_log_interval = tb_log_interval
+        self.amp = amp
+        self.amp_scalar = GradScaler()
+
+    def test(self, task_cls):
+        self.testing = True
+        self.fit(task_cls)
+
+    def fit(self, task_cls):
+        if len(self.all_gpu_ids) > 1:
+            mp.spawn(self.ddp_run, nprocs=self.num_gpus, args=(task_cls, copy.deepcopy(hparams)))
+        else:
+            self.task = task_cls()
+            self.task.trainer = self
+            self.run_single_process(self.task)
+        return 1
+
+    def ddp_run(self, gpu_idx, task_cls, hparams_):
+        hparams.update(hparams_)
+        self.proc_rank = gpu_idx
+        self.init_ddp_connection(self.proc_rank, self.num_gpus)
+        if dist.get_rank() != 0 and not self.debug:
+            sys.stdout = open(os.devnull, "w")
+            sys.stderr = open(os.devnull, "w")
+        task = task_cls()
+        task.trainer = self
+        torch.cuda.set_device(gpu_idx)
+        self.root_gpu = gpu_idx
+        self.task = task
+        self.run_single_process(task)
+
+    def run_single_process(self, task):
+        """Sanity check a few things before starting actual training.
+
+        :param task:
+        """
+        # build model, optm and load checkpoint
+        if self.proc_rank == 0:
+            self.save_terminal_logs()
+            if not self.testing:
+                self.save_codes()
+
+        model = task.build_model()
+        if model is not None:
+            task.model = model
+        checkpoint, _ = get_last_checkpoint(self.work_dir, self.resume_from_checkpoint)
+        if checkpoint is not None:
+            self.restore_weights(checkpoint)
+        elif self.on_gpu:
+            task.cuda(self.root_gpu)
+        if not self.testing:
+            self.optimizers = task.configure_optimizers()
+            self.fisrt_epoch = True
+        if checkpoint is not None:
+            self.restore_opt_state(checkpoint)
+        del checkpoint
+        # clear cache after restore
+        if self.on_gpu:
+            torch.cuda.empty_cache()
+
+        if self.use_ddp:
+            self.task = self.configure_ddp(self.task)
+            dist.barrier()
+
+        task_ref = self.get_task_ref()
+        task_ref.trainer = self
+        task_ref.testing = self.testing
+        # link up experiment object
+        if self.proc_rank == 0:
+            task_ref.build_tensorboard(save_dir=self.work_dir, name='tb_logs')
+        else:
+            os.makedirs('tmp', exist_ok=True)
+            task_ref.build_tensorboard(save_dir='tmp', name='tb_tmp')
+        self.logger = task_ref.logger
+        try:
+            if self.testing:
+                self.run_evaluation(test=True)
+            else:
+                self.train()
+        except KeyboardInterrupt as e:
+            traceback.print_exc()
+            task_ref.on_keyboard_interrupt()
+
+    ####################
+    # valid and test
+    ####################
+    def run_evaluation(self, test=False):
+        eval_results = self.evaluate(self.task, test, tqdm_desc='Valid' if not test else 'test',
+                                     max_batches=hparams['eval_max_batches'])
+        if eval_results is not None and 'tb_log' in eval_results:
+            tb_log_output = eval_results['tb_log']
+            self.log_metrics_to_tb(tb_log_output)
+        if self.proc_rank == 0 and not test:
+            self.save_checkpoint(epoch=self.current_epoch, logs=eval_results)
+
+    def evaluate(self, task, test=False, tqdm_desc='Valid', max_batches=None):
+        if max_batches == -1:
+            max_batches = None
+        # enable eval mode
+        task.zero_grad()
+        task.eval()
+        torch.set_grad_enabled(False)
+
+        task_ref = self.get_task_ref()
+        if test:
+            ret = task_ref.test_start()
+            if ret == 'EXIT':
+                return
+        else:
+            task_ref.validation_start()
+        outputs = []
+        dataloader = task_ref.test_dataloader() if test else task_ref.val_dataloader()
+        pbar = tqdm.tqdm(dataloader, desc=tqdm_desc, total=max_batches, dynamic_ncols=True, unit='step',
+                         disable=self.root_gpu > 0)
+        # give model a chance to do something with the outputs (and method defined)
+        for batch_idx, batch in enumerate(pbar):
+            if batch is None:  # pragma: no cover
+                continue
+            # stop short when on fast_dev_run (sets max_batch=1)
+            if max_batches is not None and batch_idx >= max_batches:
+                break
+
+            # make dataloader_idx arg in validation_step optional
+            if self.on_gpu:
+                batch = move_to_cuda(batch, self.root_gpu)
+            args = [batch, batch_idx]
+            if self.use_ddp:
+                output = task(*args)
+            else:
+                if test:
+                    output = task_ref.test_step(*args)
+                else:
+                    output = task_ref.validation_step(*args)
+            # track outputs for collation
+            outputs.append(output)
+        # give model a chance to do something with the outputs (and method defined)
+        if test:
+            eval_results = task_ref.test_end(outputs)
+        else:
+            eval_results = task_ref.validation_end(outputs)
+        # enable train mode again
+        task.train()
+        torch.set_grad_enabled(True)
+        return eval_results
+
+    ####################
+    # train
+    ####################
+    def train(self):
+        task_ref = self.get_task_ref()
+        task_ref.on_train_start()
+        if self.num_sanity_val_steps > 0:
+            # run tiny validation (if validation defined) to make sure program won't crash during val
+            self.evaluate(self.task, False, 'Sanity Val', max_batches=self.num_sanity_val_steps)
+        # clear cache before training
+        if self.on_gpu:
+            torch.cuda.empty_cache()
+        dataloader = task_ref.train_dataloader()
+        epoch = self.current_epoch
+        # run all epochs
+        while True:
+            # set seed for distributed sampler (enables shuffling for each epoch)
+            if self.use_ddp and hasattr(dataloader.sampler, 'set_epoch'):
+                dataloader.sampler.set_epoch(epoch)
+            # update training progress in trainer and model
+            task_ref.current_epoch = epoch
+            self.current_epoch = epoch
+            # total batches includes multiple val checks
+            self.batch_loss_value = 0  # accumulated grads
+            # before epoch hook
+            task_ref.on_epoch_start()
+
+            # run epoch
+            train_pbar = tqdm.tqdm(dataloader, initial=self.global_step, total=float('inf'),
+                                   dynamic_ncols=True, unit='step', disable=self.root_gpu > 0)
+            for batch_idx, batch in enumerate(train_pbar):
+                if self.global_step % self.val_check_interval == 0 and not self.fisrt_epoch:
+                    self.run_evaluation()
+                pbar_metrics, tb_metrics = self.run_training_batch(batch_idx, batch)
+                train_pbar.set_postfix(**pbar_metrics)
+                self.fisrt_epoch = False
+                # when metrics should be logged
+                if (self.global_step + 1) % self.tb_log_interval == 0:
+                    # logs user requested information to logger
+                    self.log_metrics_to_tb(tb_metrics)
+
+                self.global_step += 1
+                task_ref.global_step = self.global_step
+                if self.global_step > self.max_updates:
+                    print("| Training end..")
+                    break
+            # epoch end hook
+            task_ref.on_epoch_end()
+            epoch += 1
+            if self.global_step > self.max_updates:
+                break
+        task_ref.on_train_end()
+
+    def run_training_batch(self, batch_idx, batch):
+        if batch is None:
+            return {}
+        all_progress_bar_metrics = []
+        all_log_metrics = []
+        task_ref = self.get_task_ref()
+        for opt_idx, optimizer in enumerate(self.optimizers):
+            if optimizer is None:
+                continue
+            # make sure only the gradients of the current optimizer's paramaters are calculated
+            # in the training step to prevent dangling gradients in multiple-optimizer setup.
+            if len(self.optimizers) > 1:
+                for param in task_ref.parameters():
+                    param.requires_grad = False
+                for group in optimizer.param_groups:
+                    for param in group['params']:
+                        param.requires_grad = True
+
+            # forward pass
+            with autocast(enabled=self.amp):
+                if self.on_gpu:
+                    batch = move_to_cuda(copy.copy(batch), self.root_gpu)
+                args = [batch, batch_idx, opt_idx]
+                if self.use_ddp:
+                    output = self.task(*args)
+                else:
+                    output = task_ref.training_step(*args)
+                loss = output['loss']
+                if loss is None:
+                    continue
+                progress_bar_metrics = output['progress_bar']
+                log_metrics = output['tb_log']
+                # accumulate loss
+                loss = loss / self.accumulate_grad_batches
+
+            # backward pass
+            if loss.requires_grad:
+                if self.amp:
+                    self.amp_scalar.scale(loss).backward()
+                else:
+                    loss.backward()
+
+            # track progress bar metrics
+            all_log_metrics.append(log_metrics)
+            all_progress_bar_metrics.append(progress_bar_metrics)
+
+            if loss is None:
+                continue
+
+            # nan grads
+            if self.print_nan_grads:
+                has_nan_grad = False
+                for name, param in task_ref.named_parameters():
+                    if (param.grad is not None) and torch.isnan(param.grad.float()).any():
+                        print("| NaN params: ", name, param, param.grad)
+                        has_nan_grad = True
+                if has_nan_grad:
+                    exit(0)
+
+            # gradient update with accumulated gradients
+            if (self.global_step + 1) % self.accumulate_grad_batches == 0:
+                task_ref.on_before_optimization(opt_idx)
+                if self.amp:
+                    self.amp_scalar.step(optimizer)
+                    self.amp_scalar.update()
+                else:
+                    optimizer.step()
+                optimizer.zero_grad()
+                task_ref.on_after_optimization(self.current_epoch, batch_idx, optimizer, opt_idx)
+
+        # collapse all metrics into one dict
+        all_progress_bar_metrics = {k: v for d in all_progress_bar_metrics for k, v in d.items()}
+        all_log_metrics = {k: v for d in all_log_metrics for k, v in d.items()}
+        return all_progress_bar_metrics, all_log_metrics
+
+    ####################
+    # load and save checkpoint
+    ####################
+    def restore_weights(self, checkpoint):
+        # load model state
+        task_ref = self.get_task_ref()
+
+        for k, v in checkpoint['state_dict'].items():
+            getattr(task_ref, k).load_state_dict(v)
+
+        if self.on_gpu:
+            task_ref.cuda(self.root_gpu)
+        # load training state (affects trainer only)
+        self.best_val_results = checkpoint['checkpoint_callback_best']
+        self.global_step = checkpoint['global_step']
+        self.current_epoch = checkpoint['epoch']
+        task_ref.global_step = self.global_step
+
+        # wait for all models to restore weights
+        if self.use_ddp:
+            # wait for all processes to catch up
+            dist.barrier()
+
+    def restore_opt_state(self, checkpoint):
+        if self.testing:
+            return
+        # restore the optimizers
+        optimizer_states = checkpoint['optimizer_states']
+        for optimizer, opt_state in zip(self.optimizers, optimizer_states):
+            if optimizer is None:
+                return
+            try:
+                optimizer.load_state_dict(opt_state)
+                # move optimizer to GPU 1 weight at a time
+                if self.on_gpu:
+                    for state in optimizer.state.values():
+                        for k, v in state.items():
+                            if isinstance(v, torch.Tensor):
+                                state[k] = v.cuda(self.root_gpu)
+            except ValueError:
+                print("| WARMING: optimizer parameters not match !!!")
+        try:
+            if dist.is_initialized() and dist.get_rank() > 0:
+                return
+        except Exception as e:
+            print(e)
+            return
+        did_restore = True
+        return did_restore
+
+    def save_checkpoint(self, epoch, logs=None):
+        monitor_op = np.less
+        ckpt_path = f'{self.work_dir}/model_ckpt_steps_{self.global_step}.ckpt'
+        logging.info(f'Epoch {epoch:05d}@{self.global_step}: saving model to {ckpt_path}')
+        self._atomic_save(ckpt_path)
+        for old_ckpt in get_all_ckpts(self.work_dir)[self.num_ckpt_keep:]:
+            pass
+        current = None
+        if logs is not None and self.monitor_key in logs:
+            current = logs[self.monitor_key]
+        if current is not None and self.save_best:
+            if monitor_op(current, self.best_val_results):
+                best_filepath = f'{self.work_dir}/model_ckpt_best.pt'
+                self.best_val_results = current
+                logging.info(
+                    f'Epoch {epoch:05d}@{self.global_step}: {self.monitor_key} reached {current:0.5f}. '
+                    f'Saving model to {best_filepath}')
+                self._atomic_save(best_filepath)
+
+    def _atomic_save(self, filepath):
+        checkpoint = self.dump_checkpoint()
+        tmp_path = str(filepath) + ".part"
+        torch.save(checkpoint, tmp_path, _use_new_zipfile_serialization=False)
+        os.replace(tmp_path, filepath)
+
+    def dump_checkpoint(self):
+        checkpoint = {'epoch': self.current_epoch, 'global_step': self.global_step,
+                      'checkpoint_callback_best': self.best_val_results}
+        # save optimizers
+        optimizer_states = []
+        for i, optimizer in enumerate(self.optimizers):
+            if optimizer is not None:
+                optimizer_states.append(optimizer.state_dict())
+
+        checkpoint['optimizer_states'] = optimizer_states
+        task_ref = self.get_task_ref()
+        checkpoint['state_dict'] = {
+            k: v.state_dict() for k, v in task_ref.named_children() if len(list(v.parameters())) > 0}
+        return checkpoint
+
+    ####################
+    # DDP
+    ####################
+    def configure_ddp(self, task):
+        task = DDP(task, device_ids=[self.root_gpu], find_unused_parameters=hparams.get('find_unused_parameters', True))
+        random.seed(self.seed)
+        np.random.seed(self.seed)
+        return task
+
+    def init_ddp_connection(self, proc_rank, world_size):
+        root_node = '127.0.0.1'
+        root_node = self.resolve_root_node_address(root_node)
+        os.environ['MASTER_ADDR'] = root_node
+        dist.init_process_group('nccl', rank=proc_rank, world_size=world_size)
+
+    def resolve_root_node_address(self, root_node):
+        if '[' in root_node:
+            name = root_node.split('[')[0]
+            number = root_node.split(',')[0]
+            if '-' in number:
+                number = number.split('-')[0]
+            number = re.sub('[^0-9]', '', number)
+            root_node = name + number
+        return root_node
+
+    ####################
+    # utils
+    ####################
+    def get_task_ref(self):
+        from .base_task import BaseTask
+        task: BaseTask = self.task.module if isinstance(self.task, DDP) else self.task
+        return task
+
+    def log_metrics_to_tb(self, metrics, step=None):
+        """Logs the metric dict passed in.
+
+        :param metrics:
+        """
+        # turn all tensors to scalars
+        scalar_metrics = self.metrics_to_scalars(metrics)
+
+        step = step if step is not None else self.global_step
+        # log actual metrics
+        if self.proc_rank == 0:
+            self.log_metrics(self.logger, scalar_metrics, step=step)
+
+    @staticmethod
+    def log_metrics(logger, metrics, step=None):
+        for k, v in metrics.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            logger.add_scalar(k, v, step)
+
+    def metrics_to_scalars(self, metrics):
+        new_metrics = {}
+        for k, v in metrics.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+
+            if type(v) is dict:
+                v = self.metrics_to_scalars(v)
+
+            new_metrics[k] = v
+
+        return new_metrics
+
+    def save_terminal_logs(self):
+        t = datetime.now().strftime('%Y%m%d%H%M%S')
+        os.makedirs(f'{self.work_dir}/terminal_logs', exist_ok=True)
+        Tee(f'{self.work_dir}/terminal_logs/log_{t}.txt', 'w')
+
+    def save_codes(self):
+        if len(hparams['save_codes']) > 0:
+            t = datetime.now().strftime('%Y%m%d%H%M%S')
+            code_dir = f'{self.work_dir}/codes/{t}'
+            subprocess.check_call(f'mkdir -p "{code_dir}"', shell=True)
+            for c in hparams['save_codes']:
+                if os.path.exists(c):
+                    subprocess.check_call(
+                        f'rsync -aR '
+                        f'--include="*.py" '
+                        f'--include="*.yaml" '
+                        f'--exclude="__pycache__" '
+                        f'--include="*/" '
+                        f'--exclude="*" '
+                        f'"./{c}" "{code_dir}/"',
+                        shell=True)
+            print(f"| Copied codes to {code_dir}.")

preprocess/tools/note_transcription/utils/metrics/diagonal_metrics.py

@@ -0,0 +1,74 @@
+import torch
+
+
+def get_focus_rate(attn, src_padding_mask=None, tgt_padding_mask=None):
+    '''
+    attn: bs x L_t x L_s
+    '''
+    if src_padding_mask is not None:
+        attn = attn * (1 - src_padding_mask.float())[:, None, :]
+
+    if tgt_padding_mask is not None:
+        attn = attn * (1 - tgt_padding_mask.float())[:, :, None]
+
+    focus_rate = attn.max(-1).values.sum(-1)
+    focus_rate = focus_rate / attn.sum(-1).sum(-1)
+    return focus_rate
+
+
+def get_phone_coverage_rate(attn, src_padding_mask=None, src_seg_mask=None, tgt_padding_mask=None):
+    '''
+    attn: bs x L_t x L_s
+    '''
+    src_mask = attn.new(attn.size(0), attn.size(-1)).bool().fill_(False)
+    if src_padding_mask is not None:
+        src_mask |= src_padding_mask
+    if src_seg_mask is not None:
+        src_mask |= src_seg_mask
+
+    attn = attn * (1 - src_mask.float())[:, None, :]
+    if tgt_padding_mask is not None:
+        attn = attn * (1 - tgt_padding_mask.float())[:, :, None]
+
+    phone_coverage_rate = attn.max(1).values.sum(-1)
+    # phone_coverage_rate = phone_coverage_rate / attn.sum(-1).sum(-1)
+    phone_coverage_rate = phone_coverage_rate / (1 - src_mask.float()).sum(-1)
+    return phone_coverage_rate
+
+
+def get_diagonal_focus_rate(attn, attn_ks, target_len, src_padding_mask=None, tgt_padding_mask=None,
+                            band_mask_factor=5, band_width=50):
+    '''
+    attn: bx x L_t x L_s
+    attn_ks: shape: tensor with shape [batch_size], input_lens/output_lens
+
+    diagonal: y=k*x (k=attn_ks, x:output, y:input)
+    1 0 0
+    0 1 0
+    0 0 1
+    y>=k*(x-width) and y<=k*(x+width):1
+    else:0
+    '''
+    # width = min(target_len/band_mask_factor, 50)
+    width1 = target_len / band_mask_factor
+    width2 = target_len.new(target_len.size()).fill_(band_width)
+    width = torch.where(width1 < width2, width1, width2).float()
+    base = torch.ones(attn.size()).to(attn.device)
+    zero = torch.zeros(attn.size()).to(attn.device)
+    x = torch.arange(0, attn.size(1)).to(attn.device)[None, :, None].float() * base
+    y = torch.arange(0, attn.size(2)).to(attn.device)[None, None, :].float() * base
+    cond = (y - attn_ks[:, None, None] * x)
+    cond1 = cond + attn_ks[:, None, None] * width[:, None, None]
+    cond2 = cond - attn_ks[:, None, None] * width[:, None, None]
+    mask1 = torch.where(cond1 < 0, zero, base)
+    mask2 = torch.where(cond2 > 0, zero, base)
+    mask = mask1 * mask2
+
+    if src_padding_mask is not None:
+        attn = attn * (1 - src_padding_mask.float())[:, None, :]
+    if tgt_padding_mask is not None:
+        attn = attn * (1 - tgt_padding_mask.float())[:, :, None]
+
+    diagonal_attn = attn * mask
+    diagonal_focus_rate = diagonal_attn.sum(-1).sum(-1) / attn.sum(-1).sum(-1)
+    return diagonal_focus_rate, mask

preprocess/tools/note_transcription/utils/metrics/dtw.py

@@ -0,0 +1,160 @@
+from numpy import array, zeros, full, argmin, inf, ndim
+from scipy.spatial.distance import cdist
+from math import isinf
+
+
+def dtw(x, y, dist, warp=1, w=inf, s=1.0):
+    """
+    Computes Dynamic Time Warping (DTW) of two sequences.
+
+    :param array x: N1*M array
+    :param array y: N2*M array
+    :param func dist: distance used as cost measure
+    :param int warp: how many shifts are computed.
+    :param int w: window size limiting the maximal distance between indices of matched entries |i,j|.
+    :param float s: weight applied on off-diagonal moves of the path. As s gets larger, the warping path is increasingly biased towards the diagonal
+    Returns the minimum distance, the cost matrix, the accumulated cost matrix, and the wrap path.
+    """
+    assert len(x)
+    assert len(y)
+    assert isinf(w) or (w >= abs(len(x) - len(y)))
+    assert s > 0
+    r, c = len(x), len(y)
+    if not isinf(w):
+        D0 = full((r + 1, c + 1), inf)
+        for i in range(1, r + 1):
+            D0[i, max(1, i - w):min(c + 1, i + w + 1)] = 0
+        D0[0, 0] = 0
+    else:
+        D0 = zeros((r + 1, c + 1))
+        D0[0, 1:] = inf
+        D0[1:, 0] = inf
+    D1 = D0[1:, 1:]  # view
+    for i in range(r):
+        for j in range(c):
+            if (isinf(w) or (max(0, i - w) <= j <= min(c, i + w))):
+                D1[i, j] = dist(x[i], y[j])
+    C = D1.copy()
+    jrange = range(c)
+    for i in range(r):
+        if not isinf(w):
+            jrange = range(max(0, i - w), min(c, i + w + 1))
+        for j in jrange:
+            min_list = [D0[i, j]]
+            for k in range(1, warp + 1):
+                i_k = min(i + k, r)
+                j_k = min(j + k, c)
+                min_list += [D0[i_k, j] * s, D0[i, j_k] * s]
+            D1[i, j] += min(min_list)
+    if len(x) == 1:
+        path = zeros(len(y)), range(len(y))
+    elif len(y) == 1:
+        path = range(len(x)), zeros(len(x))
+    else:
+        path = _traceback(D0)
+    return D1[-1, -1], C, D1, path
+
+
+def accelerated_dtw(x, y, dist, warp=1):
+    """
+    Computes Dynamic Time Warping (DTW) of two sequences in a faster way.
+    Instead of iterating through each element and calculating each distance,
+    this uses the cdist function from scipy (https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.cdist.html)
+
+    :param array x: N1*M array
+    :param array y: N2*M array
+    :param string or func dist: distance parameter for cdist. When string is given, cdist uses optimized functions for the distance metrics.
+    If a string is passed, the distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski', 'yule'.
+    :param int warp: how many shifts are computed.
+    Returns the minimum distance, the cost matrix, the accumulated cost matrix, and the wrap path.
+    """
+    assert len(x)
+    assert len(y)
+    if ndim(x) == 1:
+        x = x.reshape(-1, 1)
+    if ndim(y) == 1:
+        y = y.reshape(-1, 1)
+    r, c = len(x), len(y)
+    D0 = zeros((r + 1, c + 1))
+    D0[0, 1:] = inf
+    D0[1:, 0] = inf
+    D1 = D0[1:, 1:]
+    D0[1:, 1:] = cdist(x, y, dist)
+    C = D1.copy()
+    for i in range(r):
+        for j in range(c):
+            min_list = [D0[i, j]]
+            for k in range(1, warp + 1):
+                min_list += [D0[min(i + k, r), j],
+                             D0[i, min(j + k, c)]]
+            D1[i, j] += min(min_list)
+    if len(x) == 1:
+        path = zeros(len(y)), range(len(y))
+    elif len(y) == 1:
+        path = range(len(x)), zeros(len(x))
+    else:
+        path = _traceback(D0)
+    return D1[-1, -1], C, D1, path
+
+
+def _traceback(D):
+    i, j = array(D.shape) - 2
+    p, q = [i], [j]
+    while (i > 0) or (j > 0):
+        tb = argmin((D[i, j], D[i, j + 1], D[i + 1, j]))
+        if tb == 0:
+            i -= 1
+            j -= 1
+        elif tb == 1:
+            i -= 1
+        else:  # (tb == 2):
+            j -= 1
+        p.insert(0, i)
+        q.insert(0, j)
+    return array(p), array(q)
+
+
+if __name__ == '__main__':
+    w = inf
+    s = 1.0
+    if 1:  # 1-D numeric
+        from sklearn.metrics.pairwise import manhattan_distances
+
+        x = [0, 0, 1, 1, 2, 4, 2, 1, 2, 0]
+        y = [1, 1, 1, 2, 2, 2, 2, 3, 2, 0]
+        dist_fun = manhattan_distances
+        w = 1
+        # s = 1.2
+    elif 0:  # 2-D numeric
+        from sklearn.metrics.pairwise import euclidean_distances
+
+        x = [[0, 0], [0, 1], [1, 1], [1, 2], [2, 2], [4, 3], [2, 3], [1, 1], [2, 2], [0, 1]]
+        y = [[1, 0], [1, 1], [1, 1], [2, 1], [4, 3], [4, 3], [2, 3], [3, 1], [1, 2], [1, 0]]
+        dist_fun = euclidean_distances
+    else:  # 1-D list of strings
+        from nltk.metrics.distance import edit_distance
+
+        # x = ['we', 'shelled', 'clams', 'for', 'the', 'chowder']
+        # y = ['class', 'too']
+        x = ['i', 'soon', 'found', 'myself', 'muttering', 'to', 'the', 'walls']
+        y = ['see', 'drown', 'himself']
+        # x = 'we talked about the situation'.split()
+        # y = 'we talked about the situation'.split()
+        dist_fun = edit_distance
+    dist, cost, acc, path = dtw(x, y, dist_fun, w=w, s=s)
+
+    # Vizualize
+    from matplotlib import pyplot as plt
+
+    plt.imshow(cost.T, origin='lower', cmap=plt.cm.Reds, interpolation='nearest')
+    plt.plot(path[0], path[1], '-o')  # relation
+    plt.xticks(range(len(x)), x)
+    plt.yticks(range(len(y)), y)
+    plt.xlabel('x')
+    plt.ylabel('y')
+    plt.axis('tight')
+    if isinf(w):
+        plt.title('Minimum distance: {}, slope weight: {}'.format(dist, s))
+    else:
+        plt.title('Minimum distance: {}, window widht: {}, slope weight: {}'.format(dist, w, s))
+    plt.show()

preprocess/tools/note_transcription/utils/metrics/laplace_var.py

@@ -0,0 +1,4 @@
+import scipy.ndimage
+
+def laplace_var(x):
+    return scipy.ndimage.laplace(x).var()

preprocess/tools/note_transcription/utils/metrics/pitch_distance.py

@@ -0,0 +1,102 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from numba import jit
+
+import torch
+
+
+@jit
+def time_warp(costs):
+    dtw = np.zeros_like(costs)
+    dtw[0, 1:] = np.inf
+    dtw[1:, 0] = np.inf
+    eps = 1e-4
+    for i in range(1, costs.shape[0]):
+        for j in range(1, costs.shape[1]):
+            dtw[i, j] = costs[i, j] + min(dtw[i - 1, j], dtw[i, j - 1], dtw[i - 1, j - 1])
+    return dtw
+
+
+def align_from_distances(distance_matrix, debug=False, return_mindist=False):
+    # for each position in spectrum 1, returns best match position in spectrum2
+    # using monotonic alignment
+    dtw = time_warp(distance_matrix)
+
+    i = distance_matrix.shape[0] - 1
+    j = distance_matrix.shape[1] - 1
+    results = [0] * distance_matrix.shape[0]
+    while i > 0 and j > 0:
+        results[i] = j
+        i, j = min([(i - 1, j), (i, j - 1), (i - 1, j - 1)], key=lambda x: dtw[x[0], x[1]])
+
+    if debug:
+        visual = np.zeros_like(dtw)
+        visual[range(len(results)), results] = 1
+        plt.matshow(visual)
+        plt.show()
+    if return_mindist:
+        return results, dtw[-1, -1]
+    return results
+
+
+def get_local_context(input_f, max_window=32, scale_factor=1.):
+    # input_f: [S, 1], support numpy array or torch tensor
+    # return hist: [S, max_window * 2], list of list
+    T = input_f.shape[0]
+    # max_window = int(max_window * scale_factor)
+    derivative = [[0 for _ in range(max_window * 2)] for _ in range(T)]
+
+    for t in range(T):  # travel the time series
+        for feat_idx in range(-max_window, max_window):
+            if t + feat_idx < 0 or t + feat_idx >= T:
+                value = 0
+            else:
+                value = input_f[t + feat_idx]
+            derivative[t][feat_idx + max_window] = value
+    return derivative
+
+
+def cal_localnorm_dist(src, tgt, src_len, tgt_len):
+    local_src = torch.tensor(get_local_context(src))
+    local_tgt = torch.tensor(get_local_context(tgt, scale_factor=tgt_len / src_len))
+
+    local_norm_src = (local_src - local_src.mean(-1).unsqueeze(-1))  # / local_src.std(-1).unsqueeze(-1)  # [T1, 32]
+    local_norm_tgt = (local_tgt - local_tgt.mean(-1).unsqueeze(-1))  # / local_tgt.std(-1).unsqueeze(-1)  # [T2, 32]
+
+    dists = torch.cdist(local_norm_src[None, :, :], local_norm_tgt[None, :, :])  # [1, T1, T2]
+    return dists
+
+
+## here is API for one sample
+def LoNDTWDistance(src, tgt):
+    # src: [S]
+    # tgt: [T]
+    dists = cal_localnorm_dist(src, tgt, src.shape[0], tgt.shape[0])  # [1, S, T]
+    costs = dists.squeeze(0)  # [S, T]
+    alignment, min_distance = align_from_distances(costs.T.cpu().detach().numpy(), return_mindist=True)  # [T]
+    return alignment, min_distance
+
+# if __name__ == '__main__':
+#     # utils from ns
+#     from utils.pitch_utils import denorm_f0
+#     from tasks.singing.fsinging import FastSingingDataset
+#     from utils.hparams import hparams, set_hparams
+#
+#     set_hparams()
+#
+#     train_ds = FastSingingDataset('test')
+#
+#     # Test One sample case
+#     sample = train_ds[0]
+#     amateur_f0 = sample['f0']
+#     prof_f0 = sample['prof_f0']
+#
+#     amateur_uv = sample['uv']
+#     amateur_padding = sample['mel2ph'] == 0
+#     prof_uv = sample['prof_uv']
+#     prof_padding = sample['prof_mel2ph'] == 0
+#     amateur_f0_denorm = denorm_f0(amateur_f0, amateur_uv, hparams, pitch_padding=amateur_padding)
+#     prof_f0_denorm = denorm_f0(prof_f0, prof_uv, hparams, pitch_padding=prof_padding)
+#     alignment, min_distance = LoNDTWDistance(amateur_f0_denorm, prof_f0_denorm)
+#     print(min_distance)
+# python utils/pitch_distance.py --config egs/datasets/audio/molar/svc_ppg.yaml

preprocess/tools/note_transcription/utils/metrics/ssim.py

@@ -0,0 +1,84 @@
+"""
+Adapted from https://github.com/Po-Hsun-Su/pytorch-ssim
+"""
+
+import torch
+import torch.nn.functional as F
+from torch.autograd import Variable
+import numpy as np
+from math import exp
+
+
+def gaussian(window_size, sigma):
+    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
+    return gauss / gauss.sum()
+
+
+def create_window(window_size, channel):
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
+    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
+    return window
+
+
+def _ssim(img1, img2, window, window_size, channel, size_average=True):
+    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
+    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
+    sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
+    sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
+
+    C1 = 0.01 ** 2
+    C2 = 0.03 ** 2
+
+    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
+
+    if size_average:
+        return ssim_map.mean()
+    else:
+        return ssim_map.mean(1)
+
+
+class SSIM(torch.nn.Module):
+    def __init__(self, window_size=11, size_average=True):
+        super(SSIM, self).__init__()
+        self.window_size = window_size
+        self.size_average = size_average
+        self.channel = 1
+        self.window = create_window(window_size, self.channel)
+
+    def forward(self, img1, img2):
+        (_, channel, _, _) = img1.size()
+
+        if channel == self.channel and self.window.data.type() == img1.data.type():
+            window = self.window
+        else:
+            window = create_window(self.window_size, channel)
+
+            if img1.is_cuda:
+                window = window.cuda(img1.get_device())
+            window = window.type_as(img1)
+
+            self.window = window
+            self.channel = channel
+
+        return _ssim(img1, img2, window, self.window_size, channel, self.size_average)
+
+
+window = None
+
+
+def ssim(img1, img2, window_size=11, size_average=True):
+    (_, channel, _, _) = img1.size()
+    global window
+    if window is None:
+        window = create_window(window_size, channel)
+        if img1.is_cuda:
+            window = window.cuda(img1.get_device())
+        window = window.type_as(img1)
+    return _ssim(img1, img2, window, window_size, channel, size_average)

preprocess/tools/note_transcription/utils/nn/model_utils.py

@@ -0,0 +1,14 @@
+import numpy as np
+
+
+def print_arch(model, model_name='model'):
+    print(f"| {model_name} Arch: ", model)
+    num_params(model, model_name=model_name)
+
+
+def num_params(model, print_out=True, model_name="model"):
+    parameters = filter(lambda p: p.requires_grad, model.parameters())
+    parameters = sum([np.prod(p.size()) for p in parameters]) / 1_000_000
+    if print_out:
+        print(f'| {model_name} Trainable Parameters: %.3fM' % parameters)
+    return parameters

preprocess/tools/note_transcription/utils/nn/schedulers.py

@@ -0,0 +1,65 @@
+class NoneSchedule(object):
+    def __init__(self, optimizer, lr):
+        self.optimizer = optimizer
+        self.constant_lr = lr
+        self.step(0)
+
+    def step(self, num_updates):
+        self.lr = self.constant_lr
+        for param_group in self.optimizer.param_groups:
+            param_group['lr'] = self.lr
+        return self.lr
+
+    def get_lr(self):
+        return self.optimizer.param_groups[0]['lr']
+
+    def get_last_lr(self):
+        return self.get_lr()
+
+
+class RSQRTSchedule(NoneSchedule):
+    def __init__(self, optimizer, lr, warmup_updates, hidden_size, last_step=-1):
+        self.optimizer = optimizer
+        self.constant_lr = lr
+        self.warmup_updates = warmup_updates
+        self.hidden_size = hidden_size
+        self.lr = lr
+        self.last_step = last_step
+        for param_group in optimizer.param_groups:
+            param_group['lr'] = self.lr
+        self.step()
+
+    def step(self, num_updates=None):
+        if num_updates is None:
+            self.last_step += 1
+            num_updates = self.last_step
+        constant_lr = self.constant_lr
+        warmup = min(num_updates / self.warmup_updates, 1.0)
+        rsqrt_decay = max(self.warmup_updates, num_updates) ** -0.5
+        rsqrt_hidden = self.hidden_size ** -0.5
+        self.lr = max(constant_lr * warmup * rsqrt_decay * rsqrt_hidden, 1e-7)
+        for param_group in self.optimizer.param_groups:
+            param_group['lr'] = self.lr
+        return self.lr
+
+
+class WarmupSchedule(NoneSchedule):
+    def __init__(self, optimizer, lr, warmup_updates, last_step=-1):
+        self.optimizer = optimizer
+        self.constant_lr = self.lr = lr
+        self.warmup_updates = warmup_updates
+        self.last_step = last_step
+        for param_group in optimizer.param_groups:
+            param_group['lr'] = self.lr
+        self.step()
+
+    def step(self, num_updates=None):
+        if num_updates is None:
+            self.last_step += 1
+            num_updates = self.last_step
+        constant_lr = self.constant_lr
+        warmup = min(num_updates / self.warmup_updates, 1.0)
+        self.lr = max(constant_lr * warmup, 1e-7)
+        for param_group in self.optimizer.param_groups:
+            param_group['lr'] = self.lr
+        return self.lr

preprocess/tools/note_transcription/utils/nn/seq_utils.py

@@ -0,0 +1,305 @@
+from collections import defaultdict
+import torch
+import torch.nn.functional as F
+
+
+def make_positions(tensor, padding_idx):
+    """Replace non-padding symbols with their position numbers.
+
+    Position numbers begin at padding_idx+1. Padding symbols are ignored.
+    """
+    # The series of casts and type-conversions here are carefully
+    # balanced to both work with ONNX export and XLA. In particular XLA
+    # prefers ints, cumsum defaults to output longs, and ONNX doesn't know
+    # how to handle the dtype kwarg in cumsum.
+    mask = tensor.ne(padding_idx).int()
+    return (
+                   torch.cumsum(mask, dim=1).type_as(mask) * mask
+           ).long() + padding_idx
+
+
+def softmax(x, dim):
+    return F.softmax(x, dim=dim, dtype=torch.float32)
+
+
+def sequence_mask(lengths, maxlen, dtype=torch.bool):
+    if maxlen is None:
+        maxlen = lengths.max()
+    mask = ~(torch.ones((len(lengths), maxlen)).to(lengths.device).cumsum(dim=1).t() > lengths).t()
+    mask.type(dtype)
+    return mask
+
+
+def weights_nonzero_speech(target):
+    # target : B x T x mel
+    # Assign weight 1.0 to all labels except for padding (id=0).
+    dim = target.size(-1)
+    return target.abs().sum(-1, keepdim=True).ne(0).float().repeat(1, 1, dim)
+
+
+INCREMENTAL_STATE_INSTANCE_ID = defaultdict(lambda: 0)
+
+
+def _get_full_incremental_state_key(module_instance, key):
+    module_name = module_instance.__class__.__name__
+
+    # assign a unique ID to each module instance, so that incremental state is
+    # not shared across module instances
+    if not hasattr(module_instance, '_instance_id'):
+        INCREMENTAL_STATE_INSTANCE_ID[module_name] += 1
+        module_instance._instance_id = INCREMENTAL_STATE_INSTANCE_ID[module_name]
+
+    return '{}.{}.{}'.format(module_name, module_instance._instance_id, key)
+
+
+def get_incremental_state(module, incremental_state, key):
+    """Helper for getting incremental state for an nn.Module."""
+    full_key = _get_full_incremental_state_key(module, key)
+    if incremental_state is None or full_key not in incremental_state:
+        return None
+    return incremental_state[full_key]
+
+
+def set_incremental_state(module, incremental_state, key, value):
+    """Helper for setting incremental state for an nn.Module."""
+    if incremental_state is not None:
+        full_key = _get_full_incremental_state_key(module, key)
+        incremental_state[full_key] = value
+
+
+def fill_with_neg_inf(t):
+    """FP16-compatible function that fills a tensor with -inf."""
+    return t.float().fill_(float('-inf')).type_as(t)
+
+
+def fill_with_neg_inf2(t):
+    """FP16-compatible function that fills a tensor with -inf."""
+    return t.float().fill_(-1e8).type_as(t)
+
+
+def select_attn(attn_logits, type='best'):
+    """
+
+    :param attn_logits: [n_layers, B, n_head, T_sp, T_txt]
+    :return:
+    """
+    encdec_attn = torch.stack(attn_logits, 0).transpose(1, 2)
+    # [n_layers * n_head, B, T_sp, T_txt]
+    encdec_attn = (encdec_attn.reshape([-1, *encdec_attn.shape[2:]])).softmax(-1)
+    if type == 'best':
+        indices = encdec_attn.max(-1).values.sum(-1).argmax(0)
+        encdec_attn = encdec_attn.gather(
+            0, indices[None, :, None, None].repeat(1, 1, encdec_attn.size(-2), encdec_attn.size(-1)))[0]
+        return encdec_attn
+    elif type == 'mean':
+        return encdec_attn.mean(0)
+
+
+def make_pad_mask(lengths, xs=None, length_dim=-1):
+    """Make mask tensor containing indices of padded part.
+    Args:
+        lengths (LongTensor or List): Batch of lengths (B,).
+        xs (Tensor, optional): The reference tensor.
+            If set, masks will be the same shape as this tensor.
+        length_dim (int, optional): Dimension indicator of the above tensor.
+            See the example.
+    Returns:
+        Tensor: Mask tensor containing indices of padded part.
+                dtype=torch.uint8 in PyTorch 1.2-
+                dtype=torch.bool in PyTorch 1.2+ (including 1.2)
+    Examples:
+        With only lengths.
+        >>> lengths = [5, 3, 2]
+        >>> make_non_pad_mask(lengths)
+        masks = [[0, 0, 0, 0 ,0],
+                 [0, 0, 0, 1, 1],
+                 [0, 0, 1, 1, 1]]
+        With the reference tensor.
+        >>> xs = torch.zeros((3, 2, 4))
+        >>> make_pad_mask(lengths, xs)
+        tensor([[[0, 0, 0, 0],
+                 [0, 0, 0, 0]],
+                [[0, 0, 0, 1],
+                 [0, 0, 0, 1]],
+                [[0, 0, 1, 1],
+                 [0, 0, 1, 1]]], dtype=torch.uint8)
+        >>> xs = torch.zeros((3, 2, 6))
+        >>> make_pad_mask(lengths, xs)
+        tensor([[[0, 0, 0, 0, 0, 1],
+                 [0, 0, 0, 0, 0, 1]],
+                [[0, 0, 0, 1, 1, 1],
+                 [0, 0, 0, 1, 1, 1]],
+                [[0, 0, 1, 1, 1, 1],
+                 [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)
+        With the reference tensor and dimension indicator.
+        >>> xs = torch.zeros((3, 6, 6))
+        >>> make_pad_mask(lengths, xs, 1)
+        tensor([[[0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0],
+                 [1, 1, 1, 1, 1, 1]],
+                [[0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0],
+                 [1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1]],
+                [[0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0],
+                 [1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1]]], dtype=torch.uint8)
+        >>> make_pad_mask(lengths, xs, 2)
+        tensor([[[0, 0, 0, 0, 0, 1],
+                 [0, 0, 0, 0, 0, 1],
+                 [0, 0, 0, 0, 0, 1],
+                 [0, 0, 0, 0, 0, 1],
+                 [0, 0, 0, 0, 0, 1],
+                 [0, 0, 0, 0, 0, 1]],
+                [[0, 0, 0, 1, 1, 1],
+                 [0, 0, 0, 1, 1, 1],
+                 [0, 0, 0, 1, 1, 1],
+                 [0, 0, 0, 1, 1, 1],
+                 [0, 0, 0, 1, 1, 1],
+                 [0, 0, 0, 1, 1, 1]],
+                [[0, 0, 1, 1, 1, 1],
+                 [0, 0, 1, 1, 1, 1],
+                 [0, 0, 1, 1, 1, 1],
+                 [0, 0, 1, 1, 1, 1],
+                 [0, 0, 1, 1, 1, 1],
+                 [0, 0, 1, 1, 1, 1]]], dtype=torch.uint8)
+    """
+    if length_dim == 0:
+        raise ValueError("length_dim cannot be 0: {}".format(length_dim))
+
+    if not isinstance(lengths, list):
+        lengths = lengths.tolist()
+    bs = int(len(lengths))
+    if xs is None:
+        maxlen = int(max(lengths))
+    else:
+        maxlen = xs.size(length_dim)
+
+    seq_range = torch.arange(0, maxlen, dtype=torch.int64)
+    seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
+    seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
+    mask = seq_range_expand >= seq_length_expand
+
+    if xs is not None:
+        assert xs.size(0) == bs, (xs.size(0), bs)
+
+        if length_dim < 0:
+            length_dim = xs.dim() + length_dim
+        # ind = (:, None, ..., None, :, , None, ..., None)
+        ind = tuple(
+            slice(None) if i in (0, length_dim) else None for i in range(xs.dim())
+        )
+        mask = mask[ind].expand_as(xs).to(xs.device)
+    return mask
+
+
+def make_non_pad_mask(lengths, xs=None, length_dim=-1):
+    """Make mask tensor containing indices of non-padded part.
+    Args:
+        lengths (LongTensor or List): Batch of lengths (B,).
+        xs (Tensor, optional): The reference tensor.
+            If set, masks will be the same shape as this tensor.
+        length_dim (int, optional): Dimension indicator of the above tensor.
+            See the example.
+    Returns:
+        ByteTensor: mask tensor containing indices of padded part.
+                    dtype=torch.uint8 in PyTorch 1.2-
+                    dtype=torch.bool in PyTorch 1.2+ (including 1.2)
+    Examples:
+        With only lengths.
+        >>> lengths = [5, 3, 2]
+        >>> make_non_pad_mask(lengths)
+        masks = [[1, 1, 1, 1 ,1],
+                 [1, 1, 1, 0, 0],
+                 [1, 1, 0, 0, 0]]
+        With the reference tensor.
+        >>> xs = torch.zeros((3, 2, 4))
+        >>> make_non_pad_mask(lengths, xs)
+        tensor([[[1, 1, 1, 1],
+                 [1, 1, 1, 1]],
+                [[1, 1, 1, 0],
+                 [1, 1, 1, 0]],
+                [[1, 1, 0, 0],
+                 [1, 1, 0, 0]]], dtype=torch.uint8)
+        >>> xs = torch.zeros((3, 2, 6))
+        >>> make_non_pad_mask(lengths, xs)
+        tensor([[[1, 1, 1, 1, 1, 0],
+                 [1, 1, 1, 1, 1, 0]],
+                [[1, 1, 1, 0, 0, 0],
+                 [1, 1, 1, 0, 0, 0]],
+                [[1, 1, 0, 0, 0, 0],
+                 [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8)
+        With the reference tensor and dimension indicator.
+        >>> xs = torch.zeros((3, 6, 6))
+        >>> make_non_pad_mask(lengths, xs, 1)
+        tensor([[[1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1],
+                 [0, 0, 0, 0, 0, 0]],
+                [[1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1],
+                 [0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0]],
+                [[1, 1, 1, 1, 1, 1],
+                 [1, 1, 1, 1, 1, 1],
+                 [0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0],
+                 [0, 0, 0, 0, 0, 0]]], dtype=torch.uint8)
+        >>> make_non_pad_mask(lengths, xs, 2)
+        tensor([[[1, 1, 1, 1, 1, 0],
+                 [1, 1, 1, 1, 1, 0],
+                 [1, 1, 1, 1, 1, 0],
+                 [1, 1, 1, 1, 1, 0],
+                 [1, 1, 1, 1, 1, 0],
+                 [1, 1, 1, 1, 1, 0]],
+                [[1, 1, 1, 0, 0, 0],
+                 [1, 1, 1, 0, 0, 0],
+                 [1, 1, 1, 0, 0, 0],
+                 [1, 1, 1, 0, 0, 0],
+                 [1, 1, 1, 0, 0, 0],
+                 [1, 1, 1, 0, 0, 0]],
+                [[1, 1, 0, 0, 0, 0],
+                 [1, 1, 0, 0, 0, 0],
+                 [1, 1, 0, 0, 0, 0],
+                 [1, 1, 0, 0, 0, 0],
+                 [1, 1, 0, 0, 0, 0],
+                 [1, 1, 0, 0, 0, 0]]], dtype=torch.uint8)
+    """
+    return ~make_pad_mask(lengths, xs, length_dim)
+
+
+def get_mask_from_lengths(lengths):
+    max_len = torch.max(lengths).item()
+    ids = torch.arange(0, max_len).to(lengths.device)
+    mask = (ids < lengths.unsqueeze(1)).bool()
+    return mask
+
+
+def group_hidden_by_segs(h, seg_ids, max_len):
+    """
+
+    :param h: [B, T, H]
+    :param seg_ids: [B, T]
+    :return: h_ph: [B, T_ph, H]
+    """
+    B, T, H = h.shape
+    h_gby_segs = h.new_zeros([B, max_len + 1, H]).scatter_add_(1, seg_ids[:, :, None].repeat([1, 1, H]), h)
+    all_ones = h.new_ones(h.shape[:2])
+    cnt_gby_segs = h.new_zeros([B, max_len + 1]).scatter_add_(1, seg_ids, all_ones).contiguous()
+    h_gby_segs = h_gby_segs[:, 1:]
+    cnt_gby_segs = cnt_gby_segs[:, 1:]
+    h_gby_segs = h_gby_segs / torch.clamp(cnt_gby_segs[:, :, None], min=1)
+    return h_gby_segs, cnt_gby_segs

preprocess/tools/note_transcription/utils/os_utils.py

@@ -0,0 +1,21 @@
+import os
+import subprocess
+from pathlib import Path
+
+def link_file(from_file, to_file):
+    subprocess.check_call(
+        f'ln -s "`realpath --relative-to="{os.path.dirname(to_file)}" "{from_file}"`" "{to_file}"', shell=True)
+
+
+def move_file(from_file, to_file):
+    subprocess.check_call(f'mv "{from_file}" "{to_file}"', shell=True)
+
+
+def copy_file(from_file, to_file):
+    subprocess.check_call(f'cp -r "{from_file}" "{to_file}"', shell=True)
+
+
+def safe_path(path):
+    os.makedirs(Path(path).parent, exist_ok=True)
+    return path
+

preprocess/tools/note_transcription/utils/plot/plot.py

@@ -0,0 +1,51 @@
+import matplotlib
+
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+LINE_COLORS = ['w', 'r', 'orange', 'k', 'cyan', 'm', 'b', 'lime', 'g', 'brown', 'navy']
+
+
+def spec_to_figure(spec, vmin=None, vmax=None, title='', f0s=None, dur_info=None):
+    if isinstance(spec, torch.Tensor):
+        spec = spec.cpu().numpy()
+    H = spec.shape[1] // 2
+    fig = plt.figure(figsize=(12, 6))
+    plt.title(title)
+    plt.pcolor(spec.T, vmin=vmin, vmax=vmax)
+    if dur_info is not None:
+        assert isinstance(dur_info, dict)
+        txt = dur_info['txt']
+        dur_gt = dur_info['dur_gt']
+        if isinstance(dur_gt, torch.Tensor):
+            dur_gt = dur_gt.cpu().numpy()
+        dur_gt = np.cumsum(dur_gt).astype(int)
+        for i in range(len(dur_gt)):
+            shift = (i % 8) + 1
+            plt.text(dur_gt[i], shift * 4, txt[i])
+            plt.vlines(dur_gt[i], 0, H // 2, colors='b')  # blue is gt
+        plt.xlim(0, dur_gt[-1])
+        if 'dur_pred' in dur_info:
+            dur_pred = dur_info['dur_pred']
+            if isinstance(dur_pred, torch.Tensor):
+                dur_pred = dur_pred.cpu().numpy()
+            dur_pred = np.cumsum(dur_pred).astype(int)
+            for i in range(len(dur_pred)):
+                shift = (i % 8) + 1
+                plt.text(dur_pred[i], H + shift * 4, txt[i])
+                plt.vlines(dur_pred[i], H, H * 1.5, colors='r')  # red is pred
+            plt.xlim(0, max(dur_gt[-1], dur_pred[-1]))
+    if f0s is not None:
+        ax = plt.gca()
+        ax2 = ax.twinx()
+        if not isinstance(f0s, dict):
+            f0s = {'f0': f0s}
+        for i, (k, f0) in enumerate(f0s.items()):
+            if isinstance(f0, torch.Tensor):
+                f0 = f0.cpu().numpy()
+            ax2.plot(f0, label=k, c=LINE_COLORS[i], linewidth=1, alpha=0.5)
+        ax2.set_ylim(0, 1000)
+        ax2.legend()
+    return fig

preprocess/tools/note_transcription/utils/rosvot_utils.py

@@ -0,0 +1,111 @@
+import numpy as np
+import torch
+
+def regulate_real_note_itv(note_itv, note_bd, word_bd, word_durs, hop_size, audio_sample_rate):
+    # regulate note_itv in seconds according to the correspondence between note_bd and word_bd
+    assert note_itv.shape[0] == np.sum(note_bd) + 1
+    assert np.sum(word_bd) <= np.sum(note_bd)
+    assert word_durs.shape[0] == np.sum(word_bd) + 1, f"{word_durs.shape[0]} {np.sum(word_bd) + 1}"
+    word_bd = np.cumsum(word_bd) * word_bd  # [0,1,0,0,1,0,0,0] -> [0,1,0,0,2,0,0,0]
+    word_itv = np.zeros((word_durs.shape[0], 2))
+    word_offsets = np.cumsum(word_durs)
+    note2words = np.zeros(note_itv.shape[0], dtype=int)
+    for idx in range(len(word_offsets) - 1):
+        word_itv[idx, 1] = word_itv[idx + 1, 0] = word_offsets[idx]
+    word_itv[-1, 1] = word_offsets[-1]
+    note_itv_secs = note_itv * hop_size / audio_sample_rate
+    for idx, itv in enumerate(note_itv):
+        start_idx, end_idx = itv
+        if word_bd[start_idx] > 0:
+            word_dur_idx = word_bd[start_idx]
+            note_itv_secs[idx, 0] = word_itv[word_dur_idx, 0]
+            note2words[idx] = word_dur_idx
+        if word_bd[end_idx] > 0:
+            word_dur_idx = word_bd[end_idx] - 1
+            note_itv_secs[idx, 1] = word_itv[word_dur_idx, 1]
+            note2words[idx] = word_dur_idx
+    note2words += 1  # mel2ph fashion: start from 1
+    return note_itv_secs, note2words
+
+def regulate_ill_slur(notes, note_itv, note2words):
+    res_note2words = []
+    res_note_itv = []
+    res_notes = []
+    note_idx = 0
+    note_idx_end = 0
+    while True:
+        if note_idx > len(notes) - 1:
+            break
+        while note_idx <= note_idx_end < len(notes) and note2words[note_idx] == note2words[note_idx_end]:
+            note_idx_end += 1
+        res_note2words.append(note2words[note_idx])
+        res_note_itv.append(note_itv[note_idx].tolist())
+        res_notes.append(notes[note_idx])
+        for idx in range(note_idx+1, note_idx_end):
+            if notes[idx] == notes[idx-1]:
+                res_note_itv[-1][1] = note_itv[idx][1]
+            else:
+                res_note_itv.append(note_itv[idx].tolist())
+                res_note2words.append(note2words[idx])
+                res_notes.append(notes[idx])
+        note_idx = note_idx_end
+    res_notes = np.array(res_notes, dtype=notes.dtype)
+    res_note_itv = np.array(res_note_itv, dtype=note_itv.dtype)
+    res_note2words = np.array(res_note2words, dtype=note2words.dtype)
+    return res_notes, res_note_itv, res_note2words
+
+def bd_to_idxs(bd):
+    # bd [T]
+    idxs = []
+    for idx in range(len(bd)):
+        if bd[idx] == 1:
+            idxs.append(idx)
+    return idxs
+
+def bd_to_durs(bd):
+    # bd [T]
+    last_idx = 0
+    durs = []
+    for idx in range(len(bd)):
+        if bd[idx] == 1:
+            durs.append(idx - last_idx)
+            last_idx = idx
+    durs.append(len(bd) - last_idx)
+    return durs
+
+def get_mel_len(wav_len, hop_size):
+    return (wav_len + hop_size - 1) // hop_size
+
+def mel2token_to_dur(mel2token, T_txt=None, max_dur=None):
+    is_torch = isinstance(mel2token, torch.Tensor)
+    has_batch_dim = True
+    if not is_torch:
+        mel2token = torch.LongTensor(mel2token)
+    if T_txt is None:
+        T_txt = mel2token.max()
+    if len(mel2token.shape) == 1:
+        mel2token = mel2token[None, ...]
+        has_batch_dim = False
+    B, _ = mel2token.shape
+    dur = mel2token.new_zeros(B, T_txt + 1).scatter_add(1, mel2token, torch.ones_like(mel2token))
+    dur = dur[:, 1:]
+    if max_dur is not None:
+        dur = dur.clamp(max=max_dur)
+    if not is_torch:
+        dur = dur.numpy()
+    if not has_batch_dim:
+        dur = dur[0]
+    return dur
+
+def align_word(word_durs, mel_len, hop_size, audio_sample_rate):
+    mel2word = np.zeros([mel_len], int)
+    start_time = 0
+    for i_word in range(len(word_durs)):
+        start_frame = int(start_time * audio_sample_rate / hop_size + 0.5)
+        end_frame = int((start_time + word_durs[i_word]) * audio_sample_rate / hop_size + 0.5)
+        mel2word[start_frame:end_frame] = i_word + 1
+        start_time = start_time + word_durs[i_word]
+
+    dur_word = mel2token_to_dur(mel2word)
+
+    return mel2word, dur_word.tolist()

preprocess/tools/note_transcription/utils/text/encoding.py

@@ -0,0 +1,9 @@
+import chardet
+
+
+def get_encoding(file):
+    with open(file, 'rb') as f:
+        encoding = chardet.detect(f.read())['encoding']
+    if encoding == 'GB2312':
+        encoding = 'GB18030'
+    return encoding

preprocess/tools/note_transcription/utils/text/text_encoder.py

@@ -0,0 +1,263 @@
+import json
+import re
+import six
+from six.moves import range  # pylint: disable=redefined-builtin
+
+PAD = "<pad>"
+EOS = "<EOS>"
+UNK = "<UNK>"
+SEG = "|"
+PUNCS = '!,.?;:'
+RESERVED_TOKENS = [PAD, EOS, UNK]
+NUM_RESERVED_TOKENS = len(RESERVED_TOKENS)
+PAD_ID = RESERVED_TOKENS.index(PAD)  # Normally 0
+EOS_ID = RESERVED_TOKENS.index(EOS)  # Normally 1
+UNK_ID = RESERVED_TOKENS.index(UNK)  # Normally 2
+
+if six.PY2:
+    RESERVED_TOKENS_BYTES = RESERVED_TOKENS
+else:
+    RESERVED_TOKENS_BYTES = [bytes(PAD, "ascii"), bytes(EOS, "ascii")]
+
+# Regular expression for unescaping token strings.
+# '\u' is converted to '_'
+# '\\' is converted to '\'
+# '\213;' is converted to unichr(213)
+_UNESCAPE_REGEX = re.compile(r"\\u|\\\\|\\([0-9]+);")
+_ESCAPE_CHARS = set(u"\\_u;0123456789")
+
+
+def strip_ids(ids, ids_to_strip):
+    """Strip ids_to_strip from the end ids."""
+    ids = list(ids)
+    while ids and ids[-1] in ids_to_strip:
+        ids.pop()
+    return ids
+
+
+class TextEncoder(object):
+    """Base class for converting from ints to/from human readable strings."""
+
+    def __init__(self, num_reserved_ids=NUM_RESERVED_TOKENS):
+        self._num_reserved_ids = num_reserved_ids
+
+    @property
+    def num_reserved_ids(self):
+        return self._num_reserved_ids
+
+    def encode(self, s):
+        """Transform a human-readable string into a sequence of int ids.
+
+        The ids should be in the range [num_reserved_ids, vocab_size). Ids [0,
+        num_reserved_ids) are reserved.
+
+        EOS is not appended.
+
+        Args:
+        s: human-readable string to be converted.
+
+        Returns:
+        ids: list of integers
+        """
+        return [int(w) + self._num_reserved_ids for w in s.split()]
+
+    def decode(self, ids, strip_extraneous=False):
+        """Transform a sequence of int ids into a human-readable string.
+
+        EOS is not expected in ids.
+
+        Args:
+        ids: list of integers to be converted.
+        strip_extraneous: bool, whether to strip off extraneous tokens
+            (EOS and PAD).
+
+        Returns:
+        s: human-readable string.
+        """
+        if strip_extraneous:
+            ids = strip_ids(ids, list(range(self._num_reserved_ids or 0)))
+        return " ".join(self.decode_list(ids))
+
+    def decode_list(self, ids):
+        """Transform a sequence of int ids into a their string versions.
+
+        This method supports transforming individual input/output ids to their
+        string versions so that sequence to/from text conversions can be visualized
+        in a human readable format.
+
+        Args:
+        ids: list of integers to be converted.
+
+        Returns:
+        strs: list of human-readable string.
+        """
+        decoded_ids = []
+        for id_ in ids:
+            if 0 <= id_ < self._num_reserved_ids:
+                decoded_ids.append(RESERVED_TOKENS[int(id_)])
+            else:
+                decoded_ids.append(id_ - self._num_reserved_ids)
+        return [str(d) for d in decoded_ids]
+
+    @property
+    def vocab_size(self):
+        raise NotImplementedError()
+
+
+class TokenTextEncoder(TextEncoder):
+    """Encoder based on a user-supplied vocabulary (file or list)."""
+
+    def __init__(self,
+                 vocab_filename,
+                 reverse=False,
+                 vocab_list=None,
+                 replace_oov=None,
+                 num_reserved_ids=NUM_RESERVED_TOKENS):
+        """Initialize from a file or list, one token per line.
+
+        Handling of reserved tokens works as follows:
+        - When initializing from a list, we add reserved tokens to the vocab.
+        - When initializing from a file, we do not add reserved tokens to the vocab.
+        - When saving vocab files, we save reserved tokens to the file.
+
+        Args:
+            vocab_filename: If not None, the full filename to read vocab from. If this
+                is not None, then vocab_list should be None.
+            reverse: Boolean indicating if tokens should be reversed during encoding
+                and decoding.
+            vocab_list: If not None, a list of elements of the vocabulary. If this is
+                not None, then vocab_filename should be None.
+            replace_oov: If not None, every out-of-vocabulary token seen when
+                encoding will be replaced by this string (which must be in vocab).
+            num_reserved_ids: Number of IDs to save for reserved tokens like <EOS>.
+        """
+        super(TokenTextEncoder, self).__init__(num_reserved_ids=num_reserved_ids)
+        self._reverse = reverse
+        self._replace_oov = replace_oov
+        if vocab_filename:
+            self._init_vocab_from_file(vocab_filename)
+        else:
+            assert vocab_list is not None
+            self._init_vocab_from_list(vocab_list)
+        self.pad_index = self.token_to_id[PAD]
+        self.eos_index = self.token_to_id[EOS]
+        self.unk_index = self.token_to_id[UNK]
+        self.seg_index = self.token_to_id[SEG] if SEG in self.token_to_id else self.eos_index
+
+    def encode(self, s):
+        """Converts a space-separated string of tokens to a list of ids."""
+        sentence = s
+        tokens = sentence.strip().split()
+        if self._replace_oov is not None:
+            tokens = [t if t in self.token_to_id else self._replace_oov
+                      for t in tokens]
+        ret = [self.token_to_id[tok] for tok in tokens]
+        return ret[::-1] if self._reverse else ret
+
+    def decode(self, ids, strip_eos=False, strip_padding=False):
+        if strip_padding and self.pad() in list(ids):
+            pad_pos = list(ids).index(self.pad())
+            ids = ids[:pad_pos]
+        if strip_eos and self.eos() in list(ids):
+            eos_pos = list(ids).index(self.eos())
+            ids = ids[:eos_pos]
+        return " ".join(self.decode_list(ids))
+
+    def decode_list(self, ids):
+        seq = reversed(ids) if self._reverse else ids
+        return [self._safe_id_to_token(i) for i in seq]
+
+    @property
+    def vocab_size(self):
+        return len(self.id_to_token)
+
+    def __len__(self):
+        return self.vocab_size
+
+    def _safe_id_to_token(self, idx):
+        return self.id_to_token.get(idx, "ID_%d" % idx)
+
+    def _init_vocab_from_file(self, filename):
+        """Load vocab from a file.
+
+        Args:
+        filename: The file to load vocabulary from.
+        """
+        with open(filename) as f:
+            tokens = [token.strip() for token in f.readlines()]
+
+        def token_gen():
+            for token in tokens:
+                yield token
+
+        self._init_vocab(token_gen(), add_reserved_tokens=False)
+
+    def _init_vocab_from_list(self, vocab_list):
+        """Initialize tokens from a list of tokens.
+
+        It is ok if reserved tokens appear in the vocab list. They will be
+        removed. The set of tokens in vocab_list should be unique.
+
+        Args:
+        vocab_list: A list of tokens.
+        """
+
+        def token_gen():
+            for token in vocab_list:
+                if token not in RESERVED_TOKENS:
+                    yield token
+
+        self._init_vocab(token_gen())
+
+    def _init_vocab(self, token_generator, add_reserved_tokens=True):
+        """Initialize vocabulary with tokens from token_generator."""
+
+        self.id_to_token = {}
+        non_reserved_start_index = 0
+
+        if add_reserved_tokens:
+            self.id_to_token.update(enumerate(RESERVED_TOKENS))
+            non_reserved_start_index = len(RESERVED_TOKENS)
+
+        self.id_to_token.update(
+            enumerate(token_generator, start=non_reserved_start_index))
+
+        # _token_to_id is the reverse of _id_to_token
+        self.token_to_id = dict((v, k) for k, v in six.iteritems(self.id_to_token))
+
+    def pad(self):
+        return self.pad_index
+
+    def eos(self):
+        return self.eos_index
+
+    def unk(self):
+        return self.unk_index
+
+    def seg(self):
+        return self.seg_index
+
+    def store_to_file(self, filename):
+        """Write vocab file to disk.
+
+        Vocab files have one token per line. The file ends in a newline. Reserved
+        tokens are written to the vocab file as well.
+
+        Args:
+        filename: Full path of the file to store the vocab to.
+        """
+        with open(filename, "w") as f:
+            for i in range(len(self.id_to_token)):
+                f.write(self.id_to_token[i] + "\n")
+
+    def sil_phonemes(self):
+        return [p for p in self.id_to_token.values() if is_sil_phoneme(p)]
+
+
+def build_token_encoder(token_list_file):
+    token_list = json.load(open(token_list_file))
+    return TokenTextEncoder(None, vocab_list=token_list, replace_oov='<UNK>')
+
+
+def is_sil_phoneme(p):
+    return p == '' or not p[0].isalpha()

preprocess/tools/note_transcription/utils/text/textgrid.py

@@ -0,0 +1,90 @@
+from collections import OrderedDict
+import re
+import json
+
+
+def remove_empty_lines(text):
+    """remove empty lines"""
+    assert (len(text) > 0)
+    assert (isinstance(text, list))
+    text = [t.strip() for t in text]
+    if "" in text:
+        text.remove("")
+    return text
+
+
+class TextGrid(object):
+    def __init__(self, text):
+        text = remove_empty_lines(text)
+        self.text = text
+        self.line_count = 0
+        self._get_type()
+        self._get_time_intval()
+        self._get_size()
+        self.tier_list = []
+        self._get_item_list()
+
+    def _extract_pattern(self, pattern, inc):
+        """
+        Parameters
+        ----------
+        pattern : regex to extract pattern
+        inc : increment of line count after extraction
+        Returns
+        -------
+        group : extracted info
+        """
+        try:
+            group = re.match(pattern, self.text[self.line_count]).group(1)
+            self.line_count += inc
+        except AttributeError:
+            raise ValueError("File format error at line %d:%s" % (self.line_count, self.text[self.line_count]))
+        return group
+
+    def _get_type(self):
+        self.file_type = self._extract_pattern(r"File type = \"(.*)\"", 2)
+
+    def _get_time_intval(self):
+        self.xmin = self._extract_pattern(r"xmin = (.*)", 1)
+        self.xmax = self._extract_pattern(r"xmax = (.*)", 2)
+
+    def _get_size(self):
+        self.size = int(self._extract_pattern(r"size = (.*)", 2))
+
+    def _get_item_list(self):
+        """Only supports IntervalTier currently"""
+        for itemIdx in range(1, self.size + 1):
+            tier = OrderedDict()
+            item_list = []
+            tier_idx = self._extract_pattern(r"item \[(.*)\]:", 1)
+            tier_class = self._extract_pattern(r"class = \"(.*)\"", 1)
+            if tier_class != "IntervalTier":
+                raise NotImplementedError("Only IntervalTier class is supported currently")
+            tier_name = self._extract_pattern(r"name = \"(.*)\"", 1)
+            tier_xmin = self._extract_pattern(r"xmin = (.*)", 1)
+            tier_xmax = self._extract_pattern(r"xmax = (.*)", 1)
+            tier_size = self._extract_pattern(r"intervals: size = (.*)", 1)
+            for i in range(int(tier_size)):
+                item = OrderedDict()
+                item["idx"] = self._extract_pattern(r"intervals \[(.*)\]", 1)
+                item["xmin"] = self._extract_pattern(r"xmin = (.*)", 1)
+                item["xmax"] = self._extract_pattern(r"xmax = (.*)", 1)
+                item["text"] = self._extract_pattern(r"text = \"(.*)\"", 1)
+                item_list.append(item)
+            tier["idx"] = tier_idx
+            tier["class"] = tier_class
+            tier["name"] = tier_name
+            tier["xmin"] = tier_xmin
+            tier["xmax"] = tier_xmax
+            tier["size"] = tier_size
+            tier["items"] = item_list
+            self.tier_list.append(tier)
+
+    def toJson(self):
+        _json = OrderedDict()
+        _json["file_type"] = self.file_type
+        _json["xmin"] = self.xmin
+        _json["xmax"] = self.xmax
+        _json["size"] = self.size
+        _json["tiers"] = self.tier_list
+        return json.dumps(_json, ensure_ascii=False, indent=2)