# coding=utf-8 from io import BytesIO from typing import Optional from fastapi import FastAPI, File, UploadFile, HTTPException, Depends from fastapi.middleware.cors import CORSMiddleware from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials from fastapi.responses import HTMLResponse import numpy as np import torch import torchaudio from funasr import AutoModel from dotenv import load_dotenv import os # 加载环境变量 load_dotenv() # 获取API Token API_TOKEN = os.getenv("API_TOKEN") if not API_TOKEN: raise RuntimeError("API_TOKEN environment variable is not set") # 设置认证 security = HTTPBearer() app = FastAPI( title="SenseVoice API", description="语音识别 API 服务", version="1.0.0" ) # 允许跨域请求 app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # 初始化模型 model = AutoModel( model="FunAudioLLM/SenseVoiceSmall", vad_model="iic/speech_fsmn_vad_zh-cn-16k-common-pytorch", vad_kwargs={"max_single_segment_time": 30000}, hub="hf", device="cuda" ) # 复用原有的格式化函数 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(s): for sptk in emoji_dict: s = s.replace(sptk, emoji_dict[sptk]) return s def format_str_v2(s): sptk_dict = {} for sptk in emoji_dict: sptk_dict[sptk] = s.count(sptk) s = s.replace(sptk, "") emo = "<|NEUTRAL|>" for e in emo_dict: if sptk_dict[e] > sptk_dict[emo]: emo = e for e in event_dict: if sptk_dict[e] > 0: s = event_dict[e] + s s = s + emo_dict[emo] for emoji in emo_set.union(event_set): s = s.replace(" " + emoji, emoji) s = s.replace(emoji + " ", emoji) return s.strip() def format_str_v3(s): 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 s = s.replace("<|nospeech|><|Event_UNK|>", "❓") for lang in lang_dict: s = s.replace(lang, "<|lang|>") s_list = [format_str_v2(s_i).strip(" ") for s_i in s.split("<|lang|>")] new_s = " " + s_list[0] cur_ent_event = get_event(new_s) for i in range(1, len(s_list)): if len(s_list[i]) == 0: continue if get_event(s_list[i]) == cur_ent_event and get_event(s_list[i]) != None: s_list[i] = s_list[i][1:] cur_ent_event = get_event(s_list[i]) if get_emo(s_list[i]) != None and get_emo(s_list[i]) == get_emo(new_s): new_s = new_s[:-1] new_s += s_list[i].strip().lstrip() new_s = new_s.replace("The.", " ") return new_s.strip() async def process_audio(audio_data: bytes, language: str = "auto") -> str: """处理音频数据并返回识别结果""" try: # 将字节数据转换为 numpy 数组 audio_buffer = BytesIO(audio_data) waveform, sample_rate = torchaudio.load(audio_buffer) # 转换为单声道 if waveform.shape[0] > 1: waveform = waveform.mean(dim=0) # 转换为 numpy array 并归一化 input_wav = waveform.numpy().astype(np.float32) # 重采样到 16kHz if sample_rate != 16000: resampler = torchaudio.transforms.Resample(sample_rate, 16000) input_wav = resampler(torch.from_numpy(input_wav)[None, :])[0, :].numpy() # 模型推理 text = model.generate( input=input_wav, cache={}, language=language, use_itn=True, batch_size_s=500, merge_vad=True ) # 格式化结果 result = text[0]["text"] result = format_str_v3(result) return result except Exception as e: raise HTTPException(status_code=500, detail=f"音频处理失败：{str(e)}") async def verify_token(credentials: HTTPAuthorizationCredentials = Depends(security)): """验证Bearer Token""" if credentials.credentials != API_TOKEN: raise HTTPException( status_code=401, detail="Invalid authentication token", headers={"WWW-Authenticate": "Bearer"} ) return credentials @app.post("/v1/audio/transcriptions") async def transcribe_audio( file: UploadFile = File(...), model: Optional[str] = "FunAudioLLM/SenseVoiceSmall", language: Optional[str] = "auto", token: HTTPAuthorizationCredentials = Depends(verify_token) ): """音频转写接口 Args: file: 音频文件（支持常见音频格式） model: 模型名称，目前仅支持 FunAudioLLM/SenseVoiceSmall language: 语言代码，支持 auto/zh/en/yue/ja/ko/nospeech Returns: {"text": "识别结果"} """ if not file.filename.lower().endswith((".mp3", ".wav", ".flac", ".ogg", ".m4a")): raise HTTPException(status_code=400, detail="不支持的音频格式") if model != "FunAudioLLM/SenseVoiceSmall": raise HTTPException(status_code=400, detail="不支持的模型") if language not in ["auto", "zh", "en", "yue", "ja", "ko", "nospeech"]: raise HTTPException(status_code=400, detail="不支持的语言") try: content = await file.read() text = await process_audio(content, language) return {"text": text} except Exception as e: raise HTTPException(status_code=500, detail=str(e)) @app.get("/", response_class=HTMLResponse) async def root(): html_content = """ SenseVoice API

欢迎使用 SenseVoice API

服务信息

版本：1.0.0

描述：多语言语音识别服务，支持中文、英语、粤语、日语、韩语等多种语言的语音转写。

主要功能

查看API文档 """ return html_content if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000)