chore: Add Docker configuration for SenseVoice OpenAI server

docker/Dockerfile

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+# Use the official Python 3.12 base image
+FROM python:3.12-slim
+
+# Set the working directory
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application code into the container
+COPY . .
+
+# Expose the port FastAPI will run on
+EXPOSE 8000
+
+# Define environment variables
+ENV TMP_DIR=/app/tmp
+
+# Create the temporary directory
+RUN mkdir -p $TMP_DIR
+
+# Set the command to run the FastAPI server
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

docker/README.md

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+Provide an OpenAI API transcribe server.
+
+
+## Build Docker Image
+
+```bash
+docker-compose build
+```
+
+or
+
+```bash
+docker build -t sensevoice-openai-server .
+```
+
+## Start Server
+
+### Docker Compose
+Change volumes in docker-compose.yml to the path of the model you want to use.
+
+```bash
+docker-compose up -d
+```
+
+### Docker
+```bash
+docker run -d -p 8000:8000 -v "/your/cache/dir:/root/.cache" sensevoice-openai-server
+```
+
+## Usage
+
+```bash
+curl http://127.0.0.1:8000/v1/audio/transcriptions \
+  -H "Content-Type: multipart/form-data" \
+  -F model="iic/SenseVoiceSmall" \
+  -F file="@/path/to/file/openai.mp3"
+```
+
+```python
+from openai import OpenAI
+client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="anything")
+
+audio_file= open("/path/to/file/audio.mp3", "rb")
+transcription = client.audio.transcriptions.create(
+  model="iic/SenseVoiceSmall", 
+  file=audio_file
+)
+print(transcription.text)
+```

docker/docker-compose.yml

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+# @Author: Bi Ying
+# @Date:   2024-07-10 21:10:22
+# @Last Modified by:   Bi Ying
+# @Last Modified time: 2024-07-10 21:20:22
+version: '3.8'
+
+services:
+  sensevoice-openai-server:
+    image: sensevoice-openai-server
+    build: .
+    ports:
+      - "8000:8000"
+    volumes:
+      - ~/.cache:/root/.cache
+    environment:
+      TMP_DIR: /app/tmp

docker/main.py

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+# @Author: Bi Ying
+# @Date:   2024-07-10 17:22:55
+import shutil
+from pathlib import Path
+from typing import Union
+
+import torch
+import torchaudio
+import numpy as np
+from funasr import AutoModel
+from fastapi import FastAPI, Form, UploadFile, File, HTTPException, status
+
+
+app = FastAPI()
+
+TMP_DIR = "./tmp"
+
+# Initialize the model
+model = "iic/SenseVoiceSmall"
+model = AutoModel(
+    model=model,
+    vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch",
+    vad_kwargs={"max_single_segment_time": 30000},
+    trust_remote_code=True,
+)
+
+emo_dict = {
+    "<|HAPPY|>": "😊",
+    "<|SAD|>": "😔",
+    "<|ANGRY|>": "😡",
+    "<|NEUTRAL|>": "",
+    "<|FEARFUL|>": "😰",
+    "<|DISGUSTED|>": "🤢",
+    "<|SURPRISED|>": "😮",
+}
+
+event_dict = {
+    "<|BGM|>": "🎼",
+    "<|Speech|>": "",
+    "<|Applause|>": "👏",
+    "<|Laughter|>": "😀",
+    "<|Cry|>": "😭",
+    "<|Sneeze|>": "🤧",
+    "<|Breath|>": "",
+    "<|Cough|>": "🤧",
+}
+
+emoji_dict = {
+    "<|nospeech|><|Event_UNK|>": "❓",
+    "<|zh|>": "",
+    "<|en|>": "",
+    "<|yue|>": "",
+    "<|ja|>": "",
+    "<|ko|>": "",
+    "<|nospeech|>": "",
+    "<|HAPPY|>": "😊",
+    "<|SAD|>": "😔",
+    "<|ANGRY|>": "😡",
+    "<|NEUTRAL|>": "",
+    "<|BGM|>": "🎼",
+    "<|Speech|>": "",
+    "<|Applause|>": "👏",
+    "<|Laughter|>": "😀",
+    "<|FEARFUL|>": "😰",
+    "<|DISGUSTED|>": "🤢",
+    "<|SURPRISED|>": "😮",
+    "<|Cry|>": "😭",
+    "<|EMO_UNKNOWN|>": "",
+    "<|Sneeze|>": "🤧",
+    "<|Breath|>": "",
+    "<|Cough|>": "😷",
+    "<|Sing|>": "",
+    "<|Speech_Noise|>": "",
+    "<|withitn|>": "",
+    "<|woitn|>": "",
+    "<|GBG|>": "",
+    "<|Event_UNK|>": "",
+}
+
+lang_dict = {
+    "<|zh|>": "<|lang|>",
+    "<|en|>": "<|lang|>",
+    "<|yue|>": "<|lang|>",
+    "<|ja|>": "<|lang|>",
+    "<|ko|>": "<|lang|>",
+    "<|nospeech|>": "<|lang|>",
+}
+
+emo_set = {"😊", "😔", "😡", "😰", "🤢", "😮"}
+event_set = {
+    "🎼",
+    "👏",
+    "😀",
+    "😭",
+    "🤧",
+    "😷",
+}
+
+
+def format_str_v2(text: str, show_emo=True, show_event=True):
+    sptk_dict = {}
+    for sptk in emoji_dict:
+        sptk_dict[sptk] = text.count(sptk)
+        text = text.replace(sptk, "")
+
+    emo = "<|NEUTRAL|>"
+    for e in emo_dict:
+        if sptk_dict[e] > sptk_dict[emo]:
+            emo = e
+    if show_emo:
+        text = text + emo_dict[emo]
+
+    for e in event_dict:
+        if sptk_dict[e] > 0 and show_event:
+            text = event_dict[e] + text
+
+    for emoji in emo_set.union(event_set):
+        text = text.replace(" " + emoji, emoji)
+        text = text.replace(emoji + " ", emoji)
+
+    return text.strip()
+
+
+def format_str_v3(text: str, show_emo=True, show_event=True):
+    def get_emo(s):
+        return s[-1] if s[-1] in emo_set else None
+
+    def get_event(s):
+        return s[0] if s[0] in event_set else None
+
+    text = text.replace("<|nospeech|><|Event_UNK|>", "❓")
+    for lang in lang_dict:
+        text = text.replace(lang, "<|lang|>")
+    parts = [format_str_v2(part, show_emo, show_event).strip(" ") for part in text.split("<|lang|>")]
+    new_s = " " + parts[0]
+    cur_ent_event = get_event(new_s)
+    for i in range(1, len(parts)):
+        if len(parts[i]) == 0:
+            continue
+        if get_event(parts[i]) == cur_ent_event and get_event(parts[i]) is not None:
+            parts[i] = parts[i][1:]
+        cur_ent_event = get_event(parts[i])
+        if get_emo(parts[i]) is not None and get_emo(parts[i]) == get_emo(new_s):
+            new_s = new_s[:-1]
+        new_s += parts[i].strip().lstrip()
+    new_s = new_s.replace("The.", " ")
+    return new_s.strip()
+
+
+def model_inference(input_wav, language, fs=16000, show_emo=True, show_event=True):
+    language = "auto" if len(language) < 1 else language
+
+    if isinstance(input_wav, tuple):
+        fs, input_wav = input_wav
+        input_wav = input_wav.astype(np.float32) / np.iinfo(np.int16).max
+        if len(input_wav.shape) > 1:
+            input_wav = input_wav.mean(-1)
+        if fs != 16000:
+            resampler = torchaudio.transforms.Resample(fs, 16000)
+            input_wav_t = torch.from_numpy(input_wav).to(torch.float32)
+            input_wav = resampler(input_wav_t[None, :])[0, :].numpy()
+
+    if len(input_wav) == 0:
+        raise ValueError("The provided audio is empty.")
+
+    merge_vad = True
+    text = model.generate(
+        input=input_wav,
+        cache={},
+        language=language,
+        use_itn=True,
+        batch_size_s=0,
+        merge_vad=merge_vad,
+    )
+
+    text = text[0]["text"]
+    text = format_str_v3(text, show_emo, show_event)
+
+    return text
+
+
+@app.post("/v1/audio/transcriptions")
+async def transcriptions(file: Union[UploadFile, None] = File(default=None), language: str = Form(default="auto")):
+    if file is None:
+        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail="Bad Request, no file provided")
+
+    filename = file.filename
+    fileobj = file.file
+    tmp_file = Path(TMP_DIR) / filename
+
+    with open(tmp_file, "wb+") as upload_file:
+        shutil.copyfileobj(fileobj, upload_file)
+
+    # 确保音频数据保持为int32格式，并转换为一维数组
+    waveform, sample_rate = torchaudio.load(tmp_file)
+    waveform = (waveform * np.iinfo(np.int32).max).to(dtype=torch.int32).squeeze()
+    if len(waveform.shape) > 1:
+        waveform = waveform.float().mean(axis=0)  # 将多通道音频转换为单通道
+    input_wav = (sample_rate, waveform.numpy())
+
+    result = model_inference(input_wav=input_wav, language=language, show_emo=False)
+
+    # 删除临时文件
+    tmp_file.unlink()
+
+    return {"text": result}
+
+
+if __name__ == "__main__":
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000)