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Dockerfile

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+FROM python:3.8-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+COPY main.py .
+COPY iic iic/
+
+RUN pip install --upgrade pip
+RUN pip install torch>=1.13 torchaudio --index-url https://download.pytorch.org/whl/cpu
+RUN pip install -r requirements.txt
+
+CMD ["python", "main.py"]

main.py

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+# -*- coding: utf-8 -*-
+"""
+Author: 一铭
+Date  : 2024-08-28
+
+Github: https://github.com/HG-ha
+Home  : https://api2.wer.plus
+
+Description:
+    From ali dharma school project: https://github.com/FunAudioLLM/SenseVoice
+
+    This program is distributed using ONNX-encapsulated fastapi,Provides an interface for reading audio from a network or file and predicting content.
+
+    If you need to use cuda, you need to install the OnnxRun-time gpu, not the onnxruntime.
+"""
+
+import librosa
+import numpy as np
+import aiohttp
+from fastapi import FastAPI, Form, UploadFile, HTTPException
+from pydantic import HttpUrl, ValidationError, BaseModel, Field
+from typing import List, Union
+from funasr_onnx import SenseVoiceSmall
+from funasr_onnx.utils.postprocess_utils import rich_transcription_postprocess
+from io import BytesIO
+
+
+class ApiResponse(BaseModel):
+    message: str = Field(..., description="Status message indicating the success of the operation.")
+    results: str = Field(..., description="Remove label output")
+    label_result: str = Field(..., description="Default output")
+
+
+app = FastAPI()
+
+async def from_url_load_audio(audio: HttpUrl) -> np.array:
+    async with aiohttp.ClientSession() as session:
+        async with session.get(
+            audio,
+            headers={
+                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36 Edg/127.0.0.0"
+            },
+        ) as response:
+            if response.status != 200:
+                raise HTTPException(
+                    status_code=400,
+                    detail=f"Failed to download image: {response.status}",
+                )
+            image_bytes = await response.read()
+            return BytesIO(image_bytes)
+
+@app.post("/extract_text",response_model=ApiResponse)
+async def upload_url(url: Union[HttpUrl, None] = Form(None), file: Union[UploadFile, None] = Form(None)):
+    if file:
+        audio = BytesIO(await file.read())
+    elif url:
+        try:
+            audio = await from_url_load_audio(str(url))
+        except Exception as e:
+            raise HTTPException(status_code=500, detail=str(e))
+
+    else:
+        return HTTPException(400,{"error": "No valid audio source provided."})
+    try:
+        res = model(audio, language=language, use_itn=True)
+        return {
+            "message": "input processed successfully", 
+            "results": rich_transcription_postprocess(res[0]),
+            "label_result": res[0]
+            }
+    except ValidationError as e:
+        raise HTTPException(status_code=400, detail=e.errors())
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+if __name__ == "__main__":
+
+    model_dir = "iic/SenseVoiceSmall"
+    device_id = 0  # Use GPU 0, automatically use CPU when not available
+    batch_size = 16
+    language = "auto"
+    quantize = True # Quantization model, small size, fast speed, accuracy may be insufficient: model_quant.onnx
+    # quantize = False # Standard model: model.onnx
+
+    # Override built-in load_data method to fix np.ndarray type accuracy bug
+    # cannot pass the librosa.load object directly, which would make the accuracy of other languages extremely poor
+    # No specific reason
+    def load_data(self, wav_content: Union[str, np.ndarray, List[str], BytesIO], fs: int = None) -> List:
+        def load_wav(path: str) -> np.ndarray:
+            waveform, _ = librosa.load(path, sr=fs)
+            return waveform
+
+        if isinstance(wav_content, np.ndarray):
+            return [wav_content]
+
+        if isinstance(wav_content, str):
+            return [load_wav(wav_content)]
+
+        if isinstance(wav_content, list):
+            return [load_wav(path) for path in wav_content]
+        
+        if isinstance(wav_content, BytesIO):
+            return [load_wav(wav_content)]
+        
+        raise TypeError(f"The type of {wav_content} is not in [str, np.ndarray, list]")
+    
+    SenseVoiceSmall.load_data = load_data
+
+    model = SenseVoiceSmall(
+        model_dir,
+        quantize=quantize,
+        device_id=device_id,
+        batch_size=batch_size
+        )
+
+    print("\n\nDocs: http://127.0.0.1:8000/docs\n")
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)

requirements.txt

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+funasr_onnx==0.4.1
+fastapi==0.112.2
+numpy==1.26.4
+uvicorn==0.30.6
+librosa==0.10.2
+aiohttp==3.10.5
+python-multipart==0.0.9