Auto-detect NeMo model type during conversion

README.md

@@ -87,7 +87,7 @@ pip install safetensors torch
 python scripts/convert_nemo.py parakeet-tdt_ctc-110m.nemo -o model.safetensors
 ```
 
-The converter supports all model types: `110m-tdt-ctc` (default), `600m-tdt`, `eou-120m`, `nemotron-600m`, `sortformer`.
+The converter auto-detects the model type by default. It also supports explicit model types: `110m-tdt-ctc`, `600m-tdt`, `eou-120m`, `nemotron-600m`, `sortformer`.
 
 ```bash
 python scripts/convert_nemo.py checkpoint.nemo -o model.safetensors --model 600m-tdt

scripts/convert_nemo.py

@@ -19,6 +19,7 @@
 """
 
 import argparse
+import re
 import tarfile
 import tempfile
 import sys
@@ -93,6 +94,44 @@
 NUM_DURATIONS = 5
 
 
+def infer_model_type(state_dict):
+    """Infer the model preset from checkpoint tensor shapes.
+
+    This keeps the common path safe: converting a 600M checkpoint with the
+    110M default silently produces loadable but invalid joint weights.
+    """
+    if any(k.startswith("sortformer_modules.") for k in state_dict):
+        return "sortformer"
+
+    embed = state_dict.get("decoder.prediction.embed.weight")
+    if embed is None:
+        raise ValueError(
+            "could not infer model type: missing decoder.prediction.embed.weight; "
+            "pass --model explicitly"
+        )
+
+    vocab_size = int(embed.shape[0])
+    layer_indices = set()
+    for key in state_dict:
+        match = re.match(r"encoder\.layers\.(\d+)\.", key)
+        if match:
+            layer_indices.add(int(match.group(1)))
+    num_layers = len(layer_indices)
+
+    if vocab_size == 8193:
+        return "600m-tdt"
+    if vocab_size == 1027:
+        return "eou-120m"
+    if vocab_size == 1025 and num_layers == 17:
+        return "110m-tdt-ctc"
+
+    raise ValueError(
+        "could not infer model type from checkpoint "
+        f"(vocab_size={vocab_size}, encoder_layers={num_layers}); "
+        "pass --model explicitly"
+    )
+
+
 # ─── NeMo → Axiom name mapping ──────────────────────────────────────────────
 
 def build_subsampling_map(axiom_prefix="encoder_"):
@@ -384,8 +423,13 @@ def dump_keys(ckpt_path):
         print(f"  {key:70s} {list(t.shape)}")
 
 
-def convert(ckpt_path, output_path, model_type=DEFAULT_MODEL):
+def convert(ckpt_path, output_path, model_type="auto"):
     """Convert NeMo checkpoint to axiom safetensors."""
+    state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True)
+    if model_type == "auto":
+        model_type = infer_model_type(state_dict)
+        print(f"Auto-detected model type: {model_type}")
+
     preset = MODEL_PRESETS[model_type]
     vocab_size = preset["vocab_size"]
     num_durations = preset["num_durations"]
@@ -397,7 +441,6 @@ def convert(ckpt_path, output_path, model_type=DEFAULT_MODEL):
     print(f"  Encoder layers: {preset['num_layers']}, vocab: {vocab_size}, "
           f"LSTM layers: {num_lstm_layers}, CTC: {preset.get('has_ctc', False)}")
 
-    state_dict = torch.load(ckpt_path, map_location="cpu", weights_only=True)
     mapping = build_full_mapping(preset)
 
     output = {}
@@ -517,9 +560,9 @@ def main():
                         help="Output safetensors file (default: model.safetensors)")
     parser.add_argument("--dump", action="store_true",
                         help="Just dump checkpoint keys and shapes")
-    parser.add_argument("--model", choices=list(MODEL_PRESETS.keys()),
-                        default=DEFAULT_MODEL,
-                        help=f"Model type (default: {DEFAULT_MODEL})")
+    parser.add_argument("--model", choices=["auto"] + list(MODEL_PRESETS.keys()),
+                        default="auto",
+                        help="Model type (default: auto-detect from checkpoint shapes)")
     args = parser.parse_args()
 
     ckpt_path = extract_checkpoint(args.input)