File size: 2,078 Bytes
a783ac1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | import base64
import io
import torch
from fastapi import FastAPI, HTTPException, Request
from transformers import pipeline
app = FastAPI()
# 1. 初始化你的 Whisper 模型的 Pipeline
print("正在載入 Whisper 模型...")
asr = pipeline(
"automatic-speech-recognition",
model="openai/whisper-large-v3-turbo",
torch_dtype=torch.float16,
device="cuda:0", # 如果沒有 GPU 請改成 "cpu"
)
print("模型載入完成！")
@app.post("/v1/audio/transcriptions")
async def transcribe_audio(request: Request):
try:
content_type = request.headers.get("content-type", "")
if "multipart/form-data" in content_type:
# 支援標準的 OpenAI multipart/form-data 格式 (如 reachy_mini 傳送的音訊檔案)
form = await request.form()
if "file" not in form:
raise HTTPException(status_code=400, detail="表單資料中缺少 'file' 欄位")
file_item = form["file"]
audio_bytes = await file_item.read()
else:
# 支援自訂的 Base64 JSON 格式
data = await request.json()
try:
messages = data["messages"]
audio_content = messages[0]["content"][0]
base64_data = audio_content["audio_url"]["url"].split(",")[1]
audio_bytes = base64.b64decode(base64_data)
except Exception as e:
raise HTTPException(status_code=400, detail=f"解析自訂 JSON 失敗: {str(e)}")
# 執行 Whisper 語音辨識
result = asr(
audio_bytes,
chunk_length_s=30,
batch_size=8,
return_timestamps=True,
generate_kwargs={"language": "english", "task": "transcribe"},
)
return {"text": result["text"]}
except HTTPException as he:
raise he
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
import uvicorn
# 啟動在 4002 埠口
uvicorn.run(app, host="0.0.0.0", port=4002)
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