Modify lora inference api

scripts/test_voxcpm_ft_infer.py

@@ -3,7 +3,7 @@
 Full finetune inference script (no LoRA).
 
 Checkpoint directory contains complete model files (pytorch_model.bin, config.json, audiovae.pth, etc.),
-can be loaded directly via VoxCPMModel.from_local().
+can be loaded directly via VoxCPM.
 
 Usage:
 
@@ -26,9 +26,8 @@ import argparse
 from pathlib import Path
 
 import soundfile as sf
-import torch
 
-from voxcpm.model import VoxCPMModel
+from voxcpm.core import VoxCPM
 
 
 def parse_args():
@@ -81,49 +80,52 @@ def parse_args():
         default=600,
         help="Max generation steps",
     )
+    parser.add_argument(
+        "--normalize",
+        action="store_true",
+        help="Enable text normalization",
+    )
     return parser.parse_args()
 
 
 def main():
     args = parse_args()
 
-    # Load model from checkpoint directory
+    # Load model from checkpoint directory (no denoiser)
     print(f"[FT Inference] Loading model: {args.ckpt_dir}")
-    model = VoxCPMModel.from_local(args.ckpt_dir, optimize=True, training=False)
+    model = VoxCPM.from_pretrained(
+        hf_model_id=args.ckpt_dir,
+        load_denoiser=False,
+        optimize=True,
+    )
 
     # Run inference
-    prompt_wav_path = args.prompt_audio or ""
-    prompt_text = args.prompt_text or ""
+    prompt_wav_path = args.prompt_audio if args.prompt_audio else None
+    prompt_text = args.prompt_text if args.prompt_text else None
 
     print(f"[FT Inference] Synthesizing: text='{args.text}'")
     if prompt_wav_path:
         print(f"[FT Inference] Using reference audio: {prompt_wav_path}")
         print(f"[FT Inference] Reference text: {prompt_text}")
 
-    with torch.inference_mode():
-        audio = model.generate(
-            target_text=args.text,
-            prompt_text=prompt_text,
-            prompt_wav_path=prompt_wav_path,
-            max_len=args.max_len,
-            inference_timesteps=args.inference_timesteps,
-            cfg_value=args.cfg_value,
-        )
-
-    # Squeeze and save audio
-    if isinstance(audio, torch.Tensor):
-        audio_np = audio.squeeze(0).cpu().numpy()
-    else:
-        raise TypeError(f"Unexpected return type from model.generate: {type(audio)}")
+    audio_np = model.generate(
+        text=args.text,
+        prompt_wav_path=prompt_wav_path,
+        prompt_text=prompt_text,
+        cfg_value=args.cfg_value,
+        inference_timesteps=args.inference_timesteps,
+        max_length=args.max_len,
+        normalize=args.normalize,
+        denoise=False,
+    )
 
+    # Save audio
     out_path = Path(args.output)
     out_path.parent.mkdir(parents=True, exist_ok=True)
-    sf.write(str(out_path), audio_np, model.sample_rate)
+    sf.write(str(out_path), audio_np, model.tts_model.sample_rate)
 
-    print(f"[FT Inference] Saved to: {out_path}, duration: {len(audio_np) / model.sample_rate:.2f}s")
+    print(f"[FT Inference] Saved to: {out_path}, duration: {len(audio_np) / model.tts_model.sample_rate:.2f}s")
 
 
 if __name__ == "__main__":
     main()
-
-