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from fastapi import FastAPI, UploadFile, File
import soundfile as sf
import ctranslate2
from transformers import WhisperProcessor
import os
os.environ["HF_HOME"] = "/tmp/hf_cache"
# 初始化 FastAPI 应用
app = FastAPI()
# 加载 Whisper 处理器和 CTranslate2 模型
print("✅ THIS IS NEW APP.PY VERSION")
processor = WhisperProcessor.from_pretrained("./whisper_processor")
model = ctranslate2.Whisper("ct2_model", compute_type="int8", device="cpu")
@app.get("/")
def root():
return {
"message": "CTranslate2 Whisper API is running.",
"usage": "POST /transcribe with an audio file (.wav, .mp3, etc.)"
}
@app.post("/transcribe")
async def transcribe(file: UploadFile = File(...)):
# 保存上传音频
temp_path = f"/tmp/{file.filename}"
with open(temp_path, "wb") as f:
f.write(await file.read())
# 加载音频并提取特征
audio_input, sample_rate = sf.read(temp_path)
inputs = processor(audio_input, sampling_rate=sample_rate, return_tensors="np")
features = inputs.input_features[0]
# 运行 CTranslate2 推理
results = model.generate(features)
tokens = results[0]["tokens"]
text = processor.decode(tokens, skip_special_tokens=True)
return {"text": text}
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