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from fastapi import FastAPI, UploadFile, File
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
app = FastAPI()
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "openai/whisper-large-v3"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
chunk_length_s=30,
batch_size=16,
return_timestamps=True,
torch_dtype=torch_dtype,
device=device,
)
@app.post("/speech_to_text")
async def speech_to_text(file : UploadFile = File(...)):
if file:
contents = await file.read()
with open(file.filename, "wb") as f:
f.write(contents)
converted_result = pipe(file.filename)
return {
"status": 200,
"text": converted_result["text"]
}
else:
return {
"status": -1
}
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