151 lines
4.3 KiB
Python
151 lines
4.3 KiB
Python
import torch
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import math
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import numpy as np
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from librosa.filters import mel as librosa_mel_fn
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import torch.nn as nn
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from typing import Any, Dict, Optional
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def dynamic_range_compression(x, C=1, clip_val=1e-5):
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return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
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def dynamic_range_decompression(x, C=1):
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return np.exp(x) / C
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def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
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return torch.log(torch.clamp(x, min=clip_val) * C)
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def dynamic_range_decompression_torch(x, C=1):
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return torch.exp(x) / C
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def spectral_normalize_torch(magnitudes):
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output = dynamic_range_compression_torch(magnitudes)
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return output
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def spectral_de_normalize_torch(magnitudes):
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output = dynamic_range_decompression_torch(magnitudes)
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return output
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class MelSpectrogram(nn.Module):
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def __init__(
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self,
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n_fft,
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num_mels,
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sampling_rate,
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hop_size,
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win_size,
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fmin,
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fmax,
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center=False,
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):
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super(MelSpectrogram, self).__init__()
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self.n_fft = n_fft
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self.hop_size = hop_size
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self.win_size = win_size
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self.sampling_rate = sampling_rate
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self.num_mels = num_mels
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self.fmin = fmin
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self.fmax = fmax
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self.center = center
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mel_basis = {}
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hann_window = {}
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mel = librosa_mel_fn(
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sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax
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)
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mel_basis = torch.from_numpy(mel).float()
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hann_window = torch.hann_window(win_size)
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self.register_buffer("mel_basis", mel_basis)
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self.register_buffer("hann_window", hann_window)
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def forward(self, y):
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y = torch.nn.functional.pad(
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y.unsqueeze(1),
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(
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int((self.n_fft - self.hop_size) / 2),
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int((self.n_fft - self.hop_size) / 2),
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),
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mode="reflect",
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)
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y = y.squeeze(1)
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spec = torch.stft(
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y,
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self.n_fft,
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hop_length=self.hop_size,
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win_length=self.win_size,
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window=self.hann_window,
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center=self.center,
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pad_mode="reflect",
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normalized=False,
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onesided=True,
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return_complex=True,
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)
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spec = torch.view_as_real(spec)
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spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
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spec = torch.matmul(self.mel_basis, spec)
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spec = spectral_normalize_torch(spec)
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return spec
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def load_mel_spectrogram():
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return load_mel_spectrogram_from_cfg(None)
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def _get_from_mapping(cfg: Any, key: str, default: Any = None) -> Any:
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"""Safely read a field from a dict/OmegaConf-like object."""
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if cfg is None:
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return default
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if isinstance(cfg, dict):
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return cfg.get(key, default)
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return getattr(cfg, key, default)
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def load_mel_spectrogram_from_cfg(audio_cfg: Optional[Any] = None) -> MelSpectrogram:
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"""Build MelSpectrogram from `audio_config`-like config.
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Expected keys (either in dict or Hydra/OmegaConf object):
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- hop_size, sample_rate (or sampling_rate), n_fft, num_mels, win_size, fmin, fmax
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"""
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# Defaults keep current behavior.
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mel_cfg: Dict[str, Any] = {
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"hop_size": _get_from_mapping(audio_cfg, "hop_size", 480),
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"sampling_rate": _get_from_mapping(
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audio_cfg,
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"sampling_rate",
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_get_from_mapping(audio_cfg, "sample_rate", 24000),
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),
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"n_fft": _get_from_mapping(audio_cfg, "n_fft", 1920),
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"num_mels": _get_from_mapping(audio_cfg, "num_mels", 128),
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"win_size": _get_from_mapping(audio_cfg, "win_size", 1920),
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"fmin": _get_from_mapping(audio_cfg, "fmin", 0),
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"fmax": _get_from_mapping(audio_cfg, "fmax", 12000),
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}
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mel_model = MelSpectrogram(**mel_cfg)
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mel_model.eval()
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return mel_model
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class MelSpectrogramEncoder(nn.Module):
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def __init__(self, audio_config: dict | None = None):
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super(MelSpectrogramEncoder, self).__init__()
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self.model = load_mel_spectrogram_from_cfg(audio_config)
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audio_config = audio_config or {}
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self.mel_mean = audio_config.get("mel_mean", -4.92)
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self.mel_var = audio_config.get("mel_var", 8.14)
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def forward(self, x):
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x = self.model(x).transpose(1, 2)
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x = (x - self.mel_mean) / math.sqrt(self.mel_var)
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return x |