Update app.py

app.py

@@ -9,19 +9,14 @@ import json
 from typing import Optional
 
 import torch
-import gradio as gr  # 添加Gradio库
+import gradio as gr
 
 from config import model_config
-
-from fastapi import FastAPI, File, Form, UploadFile, HTTPException
-from fastapi.responses import StreamingResponse, Response
-
-import uvicorn
+from gradio_client import Client, handle_file
 
 device = "cuda:0" if torch.cuda.is_available() else "cpu"
 model_dir = snapshot_download(model_config['model_dir'])
 
-# 初始化模型
 model = AutoModel(
     model=model_dir,
     trust_remote_code=False,
@@ -39,15 +34,12 @@ def transcribe_audio(file_path, vad_model="fsmn-vad", vad_kwargs='{"max_single_s
                      merge_vad=True, merge_length_s=15, batch_size_threshold_s=50, 
                      hotword=" ", spk_model="cam++", ban_emo_unk=False):
     try:
-        # 将字符串转换为字典
         vad_kwargs = json.loads(vad_kwargs)
         
-        # 使用文件路径作为输入
         temp_file_path = file_path
 
-        # 生成结果
         res = model.generate(
-            input=temp_file_path,  # 使用文件路径作为输入
+            input=temp_file_path,
             cache={},
             language=language,
             use_itn=use_itn,
@@ -60,18 +52,15 @@ def transcribe_audio(file_path, vad_model="fsmn-vad", vad_kwargs='{"max_single_s
             ban_emo_unk=ban_emo_unk
         )
 
-        # 处理结果
         text = rich_transcription_postprocess(res[0]["text"])
         
         return text
 
     except Exception as e:
-        # 捕获异常并返回错误信息
         return str(e)
 
-# 创建Gradio界面
 inputs = [
-    gr.Audio(type="filepath"),  # 设置为'filepath'来支持文件路径
+    gr.Audio(type="filepath"),
     gr.Textbox(value="fsmn-vad", label="VAD Model"),
     gr.Textbox(value='{"max_single_segment_time": 30000}', label="VAD Kwargs"),
     gr.Slider(1, 8, value=4, step=1, label="NCPU"),
@@ -97,84 +86,21 @@ gr.Interface(
 ).launch()
 
 
-class SynthesizeResponse(Response):
-    media_type = 'text/plain'
-
-app = FastAPI()
-
-@app.post('/asr', response_class=SynthesizeResponse)
-async def generate(
-    file: UploadFile = File(...),
-    vad_model: str = Form("fsmn-vad"),
-    vad_kwargs: str = Form('{"max_single_segment_time": 30000}'),
-    ncpu: int = Form(4),
-    batch_size: int = Form(1),
-    language: str = Form("auto"),
-    use_itn: bool = Form(True),
-    batch_size_s: int = Form(60),
-    merge_vad: bool = Form(True),
-    merge_length_s: int = Form(15),
-    batch_size_threshold_s: int = Form(50),
-    hotword: Optional[str] = Form(" "),
-    spk_model: str = Form("cam++"),
-    ban_emo_unk: bool = Form(False),
-) -> StreamingResponse:
-    try:
-        # 将字符串转换为字典
-        vad_kwargs = json.loads(vad_kwargs)
-
-        # 创建临时文件并保存上传的音频文件
-        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
-            temp_file_path = temp_file.name
-            input_wav_bytes = await file.read()
-            temp_file.write(input_wav_bytes)
-
-        try:
-            # 初始化模型
-            model = AutoModel(
-                model=model_dir,
-                trust_remote_code=False,
-                remote_code="./model.py",
-                vad_model=vad_model,
-                vad_kwargs=vad_kwargs,
-                ncpu=ncpu,
-                batch_size=batch_size,
-                hub="ms",
-                device=device,
-            )
-
-            # 生成结果
-            res = model.generate(
-                input=temp_file_path,  # 使用临时文件路径作为输入
-                cache={},
-                language=language,
-                use_itn=use_itn,
-                batch_size_s=batch_size_s,
-                merge_vad=merge_vad,
-                merge_length_s=merge_length_s,
-                batch_size_threshold_s=batch_size_threshold_s,
-                hotword=hotword,
-                spk_model=spk_model,
-                ban_emo_unk=ban_emo_unk
-            )
-
-            # 处理结果
-            text = rich_transcription_postprocess(res[0]["text"])
-            
-            # 返回结果
-            return StreamingResponse(io.BytesIO(text.encode('utf-8')), media_type="text/plain")
-        
-        finally:
-            # 确保在处理完毕后删除临时文件
-            if os.path.exists(temp_file_path):
-                os.remove(temp_file_path)
-    
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-@app.get("/root")
-async def read_root():
-    return {"message": "Hello World"}
-    
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+client = Client("TaiYouWeb/funasr-svsmall-cpu")
+result = client.predict(
+		file_path=handle_file('https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav'),
+		vad_model="fsmn-vad",
+		vad_kwargs="{"max_single_segment_time": 30000}",
+		ncpu=4,
+		batch_size=1,
+		language="auto",
+		use_itn=True,
+		batch_size_s=60,
+		merge_vad=True,
+		merge_length_s=15,
+		batch_size_threshold_s=50,
+		hotword=" ",
+		spk_model="cam++",
+		ban_emo_unk=False,
+		api_name="/asr"
+)