233 lines
8.1 KiB
Python
233 lines
8.1 KiB
Python
#!/usr/bin/env python3
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"""
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LoRA inference test script.
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Usage:
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python scripts/test_voxcpm_lora_infer.py \
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--config_path conf/voxcpm/voxcpm_finetune_test.yaml \
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--lora_ckpt checkpoints/step_0002000 \
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--text "Hello, this is LoRA finetuned result." \
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--output lora_test.wav
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With voice cloning:
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python scripts/test_voxcpm_lora_infer.py \
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--config_path conf/voxcpm/voxcpm_finetune_test.yaml \
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--lora_ckpt checkpoints/step_0002000 \
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--text "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 lora_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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import torch
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from voxcpm.model import VoxCPMModel
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from voxcpm.model.voxcpm import LoRAConfig
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from voxcpm.training.config import load_yaml_config
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def parse_args():
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parser = argparse.ArgumentParser("VoxCPM LoRA inference test")
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parser.add_argument(
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"--config_path",
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type=str,
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required=True,
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help="Training YAML config path (contains pretrained_path and lora config)",
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)
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parser.add_argument(
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"--lora_ckpt",
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type=str,
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required=True,
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help="LoRA checkpoint directory (contains lora_weights.ckpt with lora_A/lora_B only)",
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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="lora_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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return parser.parse_args()
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def main():
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args = parse_args()
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# 1. Load YAML config
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cfg = load_yaml_config(args.config_path)
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pretrained_path = cfg["pretrained_path"]
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lora_cfg_dict = cfg.get("lora", {}) or {}
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lora_cfg = LoRAConfig(**lora_cfg_dict) if lora_cfg_dict else None
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# 2. Load base model (with LoRA structure and torch.compile)
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print(f"[1/3] Loading base model: {pretrained_path}")
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model = VoxCPMModel.from_local(
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pretrained_path,
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optimize=True, # compile first, load_lora_weights uses named_parameters for compatibility
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training=False,
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lora_config=lora_cfg,
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)
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# Debug: check DiT param paths after compile
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dit_params = [n for n, _ in model.named_parameters() if 'feat_decoder' in n and 'lora' in n]
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print(f"[DEBUG] DiT LoRA param paths after compile (first 3): {dit_params[:3]}")
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# 3. Load LoRA weights (works after compile)
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ckpt_dir = Path(args.lora_ckpt)
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if not ckpt_dir.exists():
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raise FileNotFoundError(f"LoRA checkpoint not found: {ckpt_dir}")
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print(f"[2/3] Loading LoRA weights: {ckpt_dir}")
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loaded, skipped = model.load_lora_weights(str(ckpt_dir))
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print(f" Loaded {len(loaded)} parameters")
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if skipped:
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print(f"[WARNING] Skipped {len(skipped)} parameters")
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print(f" Skipped keys (first 5): {skipped[:5]}")
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# 4. Synthesize audio
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prompt_wav_path = args.prompt_audio or ""
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prompt_text = args.prompt_text or ""
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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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print(f"\n[3/3] Starting synthesis tests...")
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# === Test 1: With LoRA ===
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print(f"\n [Test 1] Synthesize with LoRA...")
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with torch.inference_mode():
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audio = model.generate(
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target_text=args.text,
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prompt_text=prompt_text,
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prompt_wav_path=prompt_wav_path,
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max_len=args.max_len,
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inference_timesteps=args.inference_timesteps,
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cfg_value=args.cfg_value,
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)
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audio_np = audio.squeeze(0).cpu().numpy() if audio.dim() > 1 else audio.cpu().numpy()
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lora_output = out_path.with_stem(out_path.stem + "_with_lora")
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sf.write(str(lora_output), audio_np, model.sample_rate)
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print(f" Saved: {lora_output}, duration: {len(audio_np) / model.sample_rate:.2f}s")
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# === Test 2: Disable LoRA (via set_lora_enabled) ===
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print(f"\n [Test 2] Disable LoRA (set_lora_enabled=False)...")
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model.set_lora_enabled(False)
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with torch.inference_mode():
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audio = model.generate(
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target_text=args.text,
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prompt_text=prompt_text,
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prompt_wav_path=prompt_wav_path,
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max_len=args.max_len,
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inference_timesteps=args.inference_timesteps,
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cfg_value=args.cfg_value,
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)
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audio_np = audio.squeeze(0).cpu().numpy() if audio.dim() > 1 else audio.cpu().numpy()
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disabled_output = out_path.with_stem(out_path.stem + "_lora_disabled")
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sf.write(str(disabled_output), audio_np, model.sample_rate)
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print(f" Saved: {disabled_output}, duration: {len(audio_np) / model.sample_rate:.2f}s")
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# === Test 3: Re-enable LoRA ===
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print(f"\n [Test 3] Re-enable LoRA (set_lora_enabled=True)...")
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model.set_lora_enabled(True)
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with torch.inference_mode():
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audio = model.generate(
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target_text=args.text,
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prompt_text=prompt_text,
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prompt_wav_path=prompt_wav_path,
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max_len=args.max_len,
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inference_timesteps=args.inference_timesteps,
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cfg_value=args.cfg_value,
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)
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audio_np = audio.squeeze(0).cpu().numpy() if audio.dim() > 1 else audio.cpu().numpy()
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reenabled_output = out_path.with_stem(out_path.stem + "_lora_reenabled")
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sf.write(str(reenabled_output), audio_np, model.sample_rate)
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print(f" Saved: {reenabled_output}, duration: {len(audio_np) / model.sample_rate:.2f}s")
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# === Test 4: Unload LoRA (reset_lora_weights) ===
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print(f"\n [Test 4] Unload LoRA (reset_lora_weights)...")
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model.reset_lora_weights()
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with torch.inference_mode():
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audio = model.generate(
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target_text=args.text,
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prompt_text=prompt_text,
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prompt_wav_path=prompt_wav_path,
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max_len=args.max_len,
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inference_timesteps=args.inference_timesteps,
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cfg_value=args.cfg_value,
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)
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audio_np = audio.squeeze(0).cpu().numpy() if audio.dim() > 1 else audio.cpu().numpy()
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reset_output = out_path.with_stem(out_path.stem + "_lora_reset")
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sf.write(str(reset_output), audio_np, model.sample_rate)
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print(f" Saved: {reset_output}, duration: {len(audio_np) / model.sample_rate:.2f}s")
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# === Test 5: Hot-reload LoRA (load_lora_weights) ===
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print(f"\n [Test 5] Hot-reload LoRA (load_lora_weights)...")
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loaded, _ = model.load_lora_weights(str(ckpt_dir))
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print(f" Reloaded {len(loaded)} parameters")
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with torch.inference_mode():
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audio = model.generate(
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target_text=args.text,
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prompt_text=prompt_text,
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prompt_wav_path=prompt_wav_path,
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max_len=args.max_len,
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inference_timesteps=args.inference_timesteps,
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cfg_value=args.cfg_value,
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)
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audio_np = audio.squeeze(0).cpu().numpy() if audio.dim() > 1 else audio.cpu().numpy()
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reload_output = out_path.with_stem(out_path.stem + "_lora_reloaded")
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sf.write(str(reload_output), audio_np, model.sample_rate)
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print(f" Saved: {reload_output}, duration: {len(audio_np) / model.sample_rate:.2f}s")
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print(f"\n[Done] All tests completed!")
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print(f" - with_lora: {lora_output}")
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print(f" - lora_disabled: {disabled_output}")
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print(f" - lora_reenabled: {reenabled_output}")
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print(f" - lora_reset: {reset_output}")
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print(f" - lora_reloaded: {reload_output}")
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if __name__ == "__main__":
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main()
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