mirror of
https://github.com/OpenBMB/VoxCPM
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132 lines
3.5 KiB
Python
132 lines
3.5 KiB
Python
#!/usr/bin/env python3
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"""
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Full finetune inference script (no LoRA).
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Checkpoint directory contains complete model files (pytorch_model.bin, config.json, audiovae.pth, etc.),
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can be loaded directly via VoxCPM.
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Usage:
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python scripts/test_voxcpm_ft_infer.py \
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--ckpt_dir /path/to/checkpoints/step_0001000 \
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--text "Hello, I am the finetuned VoxCPM." \
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--output ft_test.wav
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With voice cloning:
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python scripts/test_voxcpm_ft_infer.py \
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--ckpt_dir /path/to/checkpoints/step_0001000 \
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--text "Hello, this is voice cloning result." \
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--prompt_audio path/to/ref.wav \
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--prompt_text "Reference audio transcript" \
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--output ft_clone.wav
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"""
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import argparse
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from pathlib import Path
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import soundfile as sf
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from voxcpm.core import VoxCPM
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def parse_args():
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parser = argparse.ArgumentParser("VoxCPM full-finetune inference test (no LoRA)")
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parser.add_argument(
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"--ckpt_dir",
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type=str,
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required=True,
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help="Checkpoint directory (contains pytorch_model.bin, config.json, audiovae.pth, etc.)",
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)
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parser.add_argument(
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"--text",
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type=str,
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required=True,
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help="Target text to synthesize",
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)
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parser.add_argument(
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"--prompt_audio",
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type=str,
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default="",
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help="Optional: reference audio path for voice cloning",
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)
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parser.add_argument(
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"--prompt_text",
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type=str,
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default="",
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help="Optional: transcript of reference audio",
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)
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parser.add_argument(
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"--output",
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type=str,
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default="ft_test.wav",
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help="Output wav file path",
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)
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parser.add_argument(
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"--cfg_value",
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type=float,
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default=2.0,
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help="CFG scale (default: 2.0)",
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)
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parser.add_argument(
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"--inference_timesteps",
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type=int,
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default=10,
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help="Diffusion inference steps (default: 10)",
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)
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parser.add_argument(
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"--max_len",
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type=int,
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default=600,
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help="Max generation steps",
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)
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parser.add_argument(
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"--normalize",
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action="store_true",
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help="Enable text normalization",
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)
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return parser.parse_args()
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def main():
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args = parse_args()
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# Load model from checkpoint directory (no denoiser)
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print(f"[FT Inference] Loading model: {args.ckpt_dir}")
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model = VoxCPM.from_pretrained(
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hf_model_id=args.ckpt_dir,
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load_denoiser=False,
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optimize=True,
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)
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# Run inference
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prompt_wav_path = args.prompt_audio if args.prompt_audio else None
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prompt_text = args.prompt_text if args.prompt_text else None
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print(f"[FT Inference] Synthesizing: text='{args.text}'")
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if prompt_wav_path:
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print(f"[FT Inference] Using reference audio: {prompt_wav_path}")
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print(f"[FT Inference] Reference text: {prompt_text}")
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audio_np = model.generate(
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text=args.text,
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prompt_wav_path=prompt_wav_path,
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prompt_text=prompt_text,
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cfg_value=args.cfg_value,
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inference_timesteps=args.inference_timesteps,
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max_length=args.max_len,
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normalize=args.normalize,
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denoise=False,
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)
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# Save audio
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out_path = Path(args.output)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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sf.write(str(out_path), audio_np, model.tts_model.sample_rate)
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print(f"[FT Inference] Saved to: {out_path}, duration: {len(audio_np) / model.tts_model.sample_rate:.2f}s")
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if __name__ == "__main__":
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main()
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