fix VoxCPM2 training sample_rate: 48000 -> 16000 (match AudioVAE encoder)

Made-with: Cursor

conf/voxcpm_v2/voxcpm_finetune_all.yaml

@@ -1,7 +1,8 @@
 pretrained_path: /path/to/VoxCPM2/
 train_manifest: /path/to/train.jsonl
 val_manifest: null
-sample_rate: 48000
+sample_rate: 16000        # AudioVAE encoder input rate; must match audio_vae_config.sample_rate
+out_sample_rate: 48000    # AudioVAE decoder output rate; only used at inference, not during training
 batch_size: 2
 grad_accum_steps: 8  # effective batch size = batch_size × grad_accum_steps = 16
 num_workers: 8

conf/voxcpm_v2/voxcpm_finetune_lora.yaml

@@ -1,7 +1,8 @@
 pretrained_path: /path/to/VoxCPM2/
 train_manifest: /path/to/train.jsonl
 val_manifest: null
-sample_rate: 48000
+sample_rate: 16000        # AudioVAE encoder input rate; must match audio_vae_config.sample_rate
+out_sample_rate: 48000    # AudioVAE decoder output rate; only used at inference, not during training
 batch_size: 2
 grad_accum_steps: 8  # effective batch size = batch_size × grad_accum_steps = 16
 num_workers: 8

lora_ft_webui.py

@@ -99,6 +99,24 @@ def get_timestamp_str():
     return datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
 
 
+def detect_sample_rate(pretrained_path: str) -> Optional[int]:
+    """Read audio_vae_config.sample_rate from the model's config.json.
+
+    This is the AudioVAE *encoder* input rate, which is the correct rate for
+    resampling training data.  Returns None when detection fails.
+    """
+    config_file = os.path.join(pretrained_path, "config.json")
+    if not os.path.isfile(config_file):
+        return None
+    try:
+        with open(config_file, "r", encoding="utf-8") as f:
+            cfg = json.load(f)
+        return int(cfg["audio_vae_config"]["sample_rate"])
+    except (KeyError, ValueError, json.JSONDecodeError) as e:
+        print(f"Warning: failed to detect sample_rate from {config_file}: {e}", file=sys.stderr)
+        return None
+
+
 def get_or_load_asr_model():
     global asr_model
     if asr_model is None:
@@ -377,6 +395,16 @@ def start_training(
     os.makedirs(checkpoints_dir, exist_ok=True)
     os.makedirs(logs_dir, exist_ok=True)
 
+    # Auto-detect sample_rate from model config.json to prevent mismatch
+    detected_sr = detect_sample_rate(pretrained_path)
+    if detected_sr is not None:
+        if int(sample_rate) != detected_sr:
+            training_log += (
+                f"[Auto-fix] sample_rate changed from {int(sample_rate)} to {detected_sr} "
+                f"(read from {pretrained_path}/config.json audio_vae_config.sample_rate)\n"
+            )
+        sample_rate = detected_sr
+
     # Create config dictionary
     # Resolve max_steps default
     resolved_max_steps = int(max_steps) if max_steps not in (None, "", 0) else int(num_iters)
@@ -929,6 +957,19 @@ with gr.Blocks(title="VoxCPM LoRA WebUI", theme=gr.themes.Soft(), css=custom_css
                         show_label=False,
                     )
 
+            def on_pretrained_path_change(path):
+                """Auto-detect sample_rate when pretrained model path changes."""
+                sr = detect_sample_rate(path)
+                if sr is not None:
+                    return gr.update(value=sr)
+                return gr.update()
+
+            train_pretrained_path.change(
+                on_pretrained_path_change,
+                inputs=[train_pretrained_path],
+                outputs=[sample_rate],
+            )
+
             start_btn.click(
                 start_training,
                 inputs=[

scripts/train_voxcpm_finetune.py

@@ -46,6 +46,7 @@ def train(
     train_manifest: str,
     val_manifest: str = "",
     sample_rate: int = 16_000,
+    out_sample_rate: int = 0,  # accepted from YAML for documentation; not used in training
     batch_size: int = 1,
     grad_accum_steps: int = 1,
     num_workers: int = 2,
@@ -68,6 +69,7 @@ def train(
     distribute: bool = False,  # If True, save hf_model_id as base_model; otherwise save pretrained_path
 ):
     _ = config_path
+    _ = out_sample_rate
 
     # Validate distribution options
     if lora is not None and distribute and not hf_model_id:
@@ -98,6 +100,12 @@ def train(
     )
     tokenizer = base_model.text_tokenizer
 
+    expected_sr = base_model.audio_vae.sample_rate
+    assert sample_rate == expected_sr, (
+        f"sample_rate mismatch: config says {sample_rate}, but the AudioVAE encoder expects {expected_sr}. "
+        f"Please set sample_rate: {expected_sr} in your training config. "
+    )
+
     train_ds, val_ds = load_audio_text_datasets(
         train_manifest=train_manifest,
         val_manifest=val_manifest,