106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
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import os
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import torch
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import json
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import argparse
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from tqdm import tqdm
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import numpy as np
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import soundfile as sf
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from collections import OrderedDict
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from omegaconf import DictConfig
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from soulxsinger.utils.file_utils import load_config
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from soulxsinger.models.soulxsinger_svc import SoulXSingerSVC
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from soulxsinger.utils.audio_utils import load_wav
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def build_model(
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model_path: str,
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config: DictConfig,
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device: str = "cuda",
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):
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"""
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Build the model from the pre-trained model path and model configuration.
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Args:
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model_path (str): Path to the checkpoint file.
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config (DictConfig): Model configuration.
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device (str, optional): Device to use. Defaults to "cuda".
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Returns:
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Tuple[torch.nn.Module, torch.nn.Module]: The initialized model and vocoder.
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"""
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if not os.path.isfile(model_path):
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raise FileNotFoundError(
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f"Model checkpoint not found: {model_path}. "
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"Please download the pretrained model and place it at the path, or set --model_path."
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)
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model = SoulXSingerSVC(config).to(device)
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print("Model initialized.")
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print("Model parameters:", sum(p.numel() for p in model.parameters()) / 1e6, "M")
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checkpoint = torch.load(model_path, weights_only=False, map_location=device)
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if "state_dict" not in checkpoint:
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raise KeyError(
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f"Checkpoint at {model_path} has no 'state_dict' key. "
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"Expected a checkpoint saved with model.state_dict()."
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)
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model.load_state_dict(checkpoint["state_dict"], strict=True)
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model.eval()
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model.to(device)
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print("Model checkpoint loaded.")
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return model
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def process(args, config, model: torch.nn.Module):
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"""Run the full inference pipeline given a data_processor and model.
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"""
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os.makedirs(args.save_dir, exist_ok=True)
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pt_wav = load_wav(args.prompt_wav_path, config.audio.sample_rate).to(args.device)
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gt_wav = load_wav(args.target_wav_path, config.audio.sample_rate).to(args.device)
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pt_f0 = torch.from_numpy(np.load(args.prompt_f0_path)).unsqueeze(0).to(args.device)
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gt_f0 = torch.from_numpy(np.load(args.target_f0_path)).unsqueeze(0).to(args.device)
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n_step = args.n_steps if hasattr(args, "n_steps") else config.infer.n_steps
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cfg = args.cfg if hasattr(args, "cfg") else config.infer.cfg
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generated_audio, generated_shift = model.infer(pt_wav, gt_wav, pt_f0, gt_f0, auto_shift=args.auto_shift, pitch_shift=args.pitch_shift, n_steps=n_step, cfg=cfg)
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generated_audio = generated_audio.squeeze().cpu().numpy()
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if args.pitch_shift != generated_shift:
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args.pitch_shift = generated_shift
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# print(f"Applied pitch shift of {generated_shift} semitones to match GT F0 contour.")
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sf.write(os.path.join(args.save_dir, "generated.wav"), generated_audio, config.audio.sample_rate)
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print(f"Generated audio saved to {os.path.join(args.save_dir, 'generated.wav')}")
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def main(args, config):
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model = build_model(
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model_path=args.model_path,
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config=config,
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device=args.device,
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)
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process(args, config, model)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--device", type=str, default="cuda")
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parser.add_argument("--model_path", type=str, default='pretrained_models/soulx-singer/model.pt')
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parser.add_argument("--config", type=str, default='soulxsinger/config/soulxsinger.yaml')
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parser.add_argument("--prompt_wav_path", type=str, default='example/audio/zh_prompt.wav')
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parser.add_argument("--target_wav_path", type=str, default='example/audio/zh_target.wav')
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parser.add_argument("--prompt_f0_path", type=str, default='example/audio/zh_prompt_f0.npy')
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parser.add_argument("--target_f0_path", type=str, default='example/audio/zh_target_f0.npy')
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parser.add_argument("--save_dir", type=str, default='outputs')
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parser.add_argument("--auto_shift", action="store_true")
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parser.add_argument("--pitch_shift", type=int, default=0)
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parser.add_argument("--n_steps", type=int, default=32)
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parser.add_argument("--cfg", type=float, default=3.0)
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args = parser.parse_args()
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config = load_config(args.config)
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main(args, config)
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