add svc inference code

2026-03-03 09:35:06 +08:00
parent f8fce96f9c
commit edf4e11bf9
7 changed files with 986 additions and 22 deletions
@@ -0,0 +1,105 @@
+import os
+import torch
+import json
+import argparse
+from tqdm import tqdm
+import numpy as np
+import soundfile as sf
+from collections import OrderedDict
+from omegaconf import DictConfig
+
+from soulxsinger.utils.file_utils import load_config
+from soulxsinger.models.soulxsinger_svc import SoulXSingerSVC
+from soulxsinger.utils.audio_utils import load_wav
+
+
+def build_model(
+    model_path: str,
+    config: DictConfig,
+    device: str = "cuda",
+):
+    """
+    Build the model from the pre-trained model path and model configuration.
+
+    Args:
+        model_path (str): Path to the checkpoint file.
+        config (DictConfig): Model configuration.
+        device (str, optional): Device to use. Defaults to "cuda".
+
+    Returns:
+        Tuple[torch.nn.Module, torch.nn.Module]: The initialized model and vocoder.
+    """
+
+    if not os.path.isfile(model_path):
+        raise FileNotFoundError(
+            f"Model checkpoint not found: {model_path}. "
+            "Please download the pretrained model and place it at the path, or set --model_path."
+        )
+    model = SoulXSingerSVC(config).to(device)
+    print("Model initialized.")
+    print("Model parameters:", sum(p.numel() for p in model.parameters()) / 1e6, "M")
+    
+    checkpoint = torch.load(model_path, weights_only=False, map_location=device)
+    if "state_dict" not in checkpoint:
+        raise KeyError(
+            f"Checkpoint at {model_path} has no 'state_dict' key. "
+            "Expected a checkpoint saved with model.state_dict()."
+        )
+    model.load_state_dict(checkpoint["state_dict"], strict=True)
+    
+    model.eval()
+    model.to(device)
+    print("Model checkpoint loaded.")
+
+    return model
+
+
+def process(args, config, model: torch.nn.Module):
+    """Run the full inference pipeline given a data_processor and model.
+    """
+
+    os.makedirs(args.save_dir, exist_ok=True)
+    pt_wav = load_wav(args.prompt_wav_path, config.audio.sample_rate).to(args.device)
+    gt_wav = load_wav(args.target_wav_path, config.audio.sample_rate).to(args.device)
+    pt_f0 = torch.from_numpy(np.load(args.prompt_f0_path)).unsqueeze(0).to(args.device)
+    gt_f0 = torch.from_numpy(np.load(args.target_f0_path)).unsqueeze(0).to(args.device)
+
+    n_step = args.n_steps if hasattr(args, "n_steps") else config.infer.n_steps
+    cfg = args.cfg if hasattr(args, "cfg") else config.infer.cfg
+
+    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)
+    generated_audio = generated_audio.squeeze().cpu().numpy()
+    if args.pitch_shift != generated_shift:
+        args.pitch_shift = generated_shift
+        # print(f"Applied pitch shift of {generated_shift} semitones to match GT F0 contour.")
+
+    sf.write(os.path.join(args.save_dir, "generated.wav"), generated_audio, config.audio.sample_rate)
+    print(f"Generated audio saved to {os.path.join(args.save_dir, 'generated.wav')}")
+
+
+def main(args, config):
+    model = build_model(
+        model_path=args.model_path,
+        config=config,
+        device=args.device,
+    )
+    process(args, config, model)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--device", type=str, default="cuda")
+    parser.add_argument("--model_path", type=str, default='pretrained_models/soulx-singer/model.pt')
+    parser.add_argument("--config", type=str, default='soulxsinger/config/soulxsinger.yaml')
+    parser.add_argument("--prompt_wav_path", type=str, default='example/audio/zh_prompt.wav')
+    parser.add_argument("--target_wav_path", type=str, default='example/audio/zh_target.wav')
+    parser.add_argument("--prompt_f0_path", type=str, default='example/audio/zh_prompt_f0.npy')
+    parser.add_argument("--target_f0_path", type=str, default='example/audio/zh_target_f0.npy')
+    parser.add_argument("--save_dir", type=str, default='outputs')
+    parser.add_argument("--auto_shift", action="store_true")
+    parser.add_argument("--pitch_shift", type=int, default=0)
+    parser.add_argument("--n_steps", type=int, default=32)
+    parser.add_argument("--cfg", type=float, default=3.0)
+    args = parser.parse_args()
+    
+    config = load_config(args.config)
+    main(args, config)