use hydra instead of argparse

decode.py

@@ -1,19 +1,25 @@
-import argparse
 import os
 
+import hydra
 import torch
-
+from funasr import AutoModel
-from model import FunASRNano
+from omegaconf import DictConfig, OmegaConf, ListConfig
 
 
-def main():
+@hydra.main(config_name=None, version_base=None)
-    parser = argparse.ArgumentParser(description="FunASR-Nano")
+def main_hydra(cfg: DictConfig):
-    parser.add_argument("--scp-file", type=str, required=True)
+    def to_plain_list(cfg_item):
-    parser.add_argument("--output-file", type=str, required=True)
+        if isinstance(cfg_item, ListConfig):
-    parser.add_argument(
+            return OmegaConf.to_container(cfg_item, resolve=True)
-        "--model-dir", type=str, default="FunAudioLLM/Fun-ASR-Nano-2512"
+        elif isinstance(cfg_item, DictConfig):
-    )
+            return {k: to_plain_list(v) for k, v in cfg_item.items()}
-    args = parser.parse_args()
+        else:
+            return cfg_item
+    kwargs = to_plain_list(cfg)
+
+    model_dir = kwargs.get("model_dir", "FunAudioLLM/Fun-ASR-Nano-2512")
+    scp_file = kwargs["scp_file"]
+    output_file = kwargs["output_file"]
 
     device = (
         "cuda:0"
@@ -22,24 +28,30 @@ def main():
         if torch.backends.mps.is_available()
         else "cpu"
     )
-    m, kwargs = FunASRNano.from_pretrained(model=args.model_dir, device=device)
+    model = AutoModel(
-    m.eval()
+        model=model_dir,
+        trust_remote_code=True,
+        vad_model="fsmn-vad",
+        vad_kwargs={"max_single_segment_time": 30000},
+        remote_code="./model.py",
+        device=device,
+    )
 
-    output_dir = os.path.dirname(args.output_file)
+    output_dir = os.path.dirname(output_file)
     if output_dir and not os.path.exists(output_dir):
         os.makedirs(output_dir, exist_ok=True)
 
-    with open(args.scp_file, "r", encoding="utf-8") as f1:
+    with open(scp_file, "r", encoding="utf-8") as f1:
-        with open(args.output_file, "w", encoding="utf-8") as f2:
+        with open(output_file, "w", encoding="utf-8") as f2:
             for line in f1:
                 line = line.strip()
                 if not line:
                     continue
                 parts = line.split(maxsplit=1)
                 if len(parts) == 2:
-                    text = m.inference(data_in=[parts[1]], **kwargs)[0][0]["text"]
+                    text = model.generate(input=[parts[1]], cache={}, batch_size=1)[0]["text"]
                     f2.write(f"{parts[0]}\t{text}\n")
 
 
 if __name__ == "__main__":
-    main()
+    main_hydra()