the file in hf is ms type, so when automodel, use ms

app.py

@@ -1,70 +1,53 @@
 import os
-import spaces
 # from huggingface_hub import snapshot_download
-from modelscope.hub.snapshot_download import snapshot_download
+
+REPO_TYPE = "hf" 
+if REPO_TYPE not in ["hf", "ms"]:
+    raise ValueError("REPO_TYPE must be either 'hf' for Hugging Face or 'ms' for ModelScope.")
+
+if REPO_TYPE == "hf":
+    from huggingface_hub import snapshot_download
+else:
+    from modelscope.hub.snapshot_download import snapshot_download
 
 
 # 1. 定义本地路径和远程仓库ID
 FUN_ASR_NANO_LOCAL_PATH = "./Fun-ASR/model"
+FUN_ASR_NANO_REPO_ID = "FunAudioLLM/Fun-ASR-Nano-2512"
 SENSE_VOICE_SMALL_LOCAL_PATH = "./Fun-ASR/model/SenseVoiceSmall"
+# SENSE_VOICE_SMALL_REPO_ID = "FunAudioLLM/SenseVoiceSmall"
+# REPO_TYPE = "hf"  # "hf" for Hugging Face, "ms" for ModelScope
+SENSE_VOICE_SMALL_REPO_ID = "iic/SenseVoiceSmall"
 
-# 配置仓库类型 (通过修改这个变量来切换平台)
-# "ms" = ModelScope, "hf" = Hugging Face
-REPO_TYPE = "hf"  # 修改这里来切换平台
 
-# 根据仓库类型配置仓库ID
-if REPO_TYPE == "hf":
-    # Hugging Face 配置
-    FUN_ASR_NANO_REPO_ID = "FunAudioLLM/Fun-ASR-Nano-2512"
-    SENSE_VOICE_SMALL_REPO_ID = "FunAudioLLM/SenseVoiceSmall"
-else:  # "ms"
-    # ModelScope 配置
+if REPO_TYPE == "ms":
     FUN_ASR_NANO_REPO_ID = "FunAudioLLM/Fun-ASR-Nano-2512"
-    SENSE_VOICE_SMALL_REPO_ID = "iic/SenseVoiceSmall" 
-
-# 2. 导入下载库
-if REPO_TYPE == "hf":
-    from huggingface_hub import snapshot_download as hf_snapshot_download
+    SENSE_VOICE_SMALL_REPO_ID = "iic/SenseVoiceSmall"
 else:
-    from modelscope.hub.snapshot_download import snapshot_download as ms_snapshot_download
-
-def download_model(repo_id, local_path):
-    """
-    根据平台类型下载模型
-
-    Args:
-        repo_id (str): 仓库ID
-        local_path (str): 本地存储路径
-    """
-    if REPO_TYPE == "hf":
-        # Hugging Face 下载逻辑
-        print(f"正在从 Hugging Face 下载模型 {repo_id} 到 {local_path} ...")
-        hf_snapshot_download(
-            repo_id=repo_id,
-            local_dir=local_path,
-            ignore_patterns=["*.onnx"], # 如果你不需要onnx文件，可以过滤掉以节省时间和空间
-        )
-    else:  # "ms"
-        # ModelScope 下载逻辑
-        print(f"正在从 ModelScope 下载模型 {repo_id} 到 {local_path} ...")
-        from modelscope import HubApi
-        api = HubApi()
-        api.login(os.getenv("MODELSCOPE_TOKEN"))
-        ms_snapshot_download(
-            repo_id=repo_id,
-            local_dir=local_path,
-            ignore_patterns=["*.onnx"], # 如果你不需要onnx文件，可以过滤掉以节省时间和空间
-        )
-    print("模型下载完毕！")
+    FUN_ASR_NANO_REPO_ID = "FunAudioLLM/Fun-ASR-Nano-2512"
+    SENSE_VOICE_SMALL_REPO_ID = "FunAudioLLM/SenseVoiceSmall"
 
-# 3. 检查本地是否存在，不存在则下载
+# 2. 检查本地是否存在，不存在则下载
 if not os.path.exists(FUN_ASR_NANO_LOCAL_PATH):
-    download_model(FUN_ASR_NANO_REPO_ID, FUN_ASR_NANO_LOCAL_PATH)
+    print(f"正在下载模型 Fun-ASR-Nano 到 {FUN_ASR_NANO_LOCAL_PATH} ...")
+    snapshot_download(
+        repo_id=FUN_ASR_NANO_REPO_ID,
+        local_dir=FUN_ASR_NANO_LOCAL_PATH,
+        ignore_patterns=["*.onnx"], # 如果你不需要onnx文件，可以过滤掉以节省时间和空间
+    )
+    print("模型下载完毕！")
 else:
     print("检测到本地模型文件，跳过下载。")
 
+
 if not os.path.exists(SENSE_VOICE_SMALL_LOCAL_PATH):
-    download_model(SENSE_VOICE_SMALL_REPO_ID, SENSE_VOICE_SMALL_LOCAL_PATH)
+    print(f"正在下载模型 {SENSE_VOICE_SMALL_REPO_ID} 到 {SENSE_VOICE_SMALL_LOCAL_PATH} ...")
+    snapshot_download(
+        repo_id=SENSE_VOICE_SMALL_REPO_ID,
+        local_dir=SENSE_VOICE_SMALL_LOCAL_PATH,
+        ignore_patterns=["*.onnx"], # 如果你不需要onnx文件，可以过滤掉以节省时间和空间
+    )
+    print("模型下载完毕！")
 else:
     print("检测到本地模型文件，跳过下载。")
 
@@ -89,14 +72,10 @@ from funasr.utils.postprocess_utils import rich_transcription_postprocess
 # Model configurations for Hugging Face deployment
 FUN_ASR_NANO_MODEL_PATH_LIST = [
     "Fun-ASR/model", # local path, ms
-    "FunAudioLLM/fun-asr-nano", # huggingface model repo, hf
-    "FunAudioLLM/fun-asr-nano" # ModelScope model repo, ms
 ]
 
 SENSEVOICE_MODEL_PATH_LIST = [
     "Fun-ASR/model/SenseVoiceSmall", # local path together with this hf space
-    "FunAudioLLM/SenseVoiceSmall", # huggingface model repo
-    "iic/SenseVoiceSmall" # ModelScope model repo
 ]
 
 class LogCapture(io.StringIO):
@@ -430,8 +409,6 @@ def get_model_options(pipeline_type):
 # Dictionary to store loaded models
 loaded_models = {}
 
-
-@spaces.GPU
 def transcribe_audio(audio_input, audio_url, proxy_url, proxy_username, proxy_password, pipeline_type, model_id, download_method, start_time=None, end_time=None, verbose=False):
     """
     Transcribes audio from a given source using SenseVoice.
@@ -516,7 +493,7 @@ def transcribe_audio(audio_input, audio_url, proxy_url, proxy_username, proxy_pa
                     vad_kwargs={"max_single_segment_time": 30000},
                     device=device,
                     disable_update=True,
-                    hub=REPO_TYPE,
+                    hub='ms',
                 )
             elif pipeline_type == "sensevoice":
                 model = AutoModel(
@@ -681,20 +658,7 @@ with gr.Blocks() as iface:
     2. **Select Pipeline Type**: Choose from available pipelines:
        - **Fun-ASR-Nano** (default) - Large language model based ASR model
        - **SenseVoice** - CTC-based based ASR model with VAD
-
-    3. **Available Model Options**:
-
-       **For Fun-ASR-Nano:**
-       - `Fun-ASR/model` (local path, default)
-       - `FunAudioLLM/fun-asr-nano` (HuggingFace)
-       - `FunAudioLLM/fun-asr-nano` (ModelScope)
-
-       **For SenseVoice:**
-       - `Fun-ASR/model/SenseVoiceSmall` (local path, default for this pipeline)
-       - `FunAudioLLM/SenseVoiceSmall` (HuggingFace)
-       - `iic/SenseVoiceSmall` (ModelScope)
-
-    4. **Local Testing**: For development, you can use local paths as shown above
+    3. **Local Testing**: For development, you can use local paths as shown above
 
     Supported languages: 
         - Fun-ASR-Nano: more than 50 languages and Chinese dialects.