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from fastapi import FastAPI
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
app = FastAPI()
device = "cuda" 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, low_cpu_mem_usage=True, use_safetensors=True, cache_dir="./app.cache"
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id, cache_dir="./app.cache")
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,
)
def speech_to_text(path_to_file: str) -> str:
"""
Given the path to the .wav file, it returns the transcription.
"""
result = pipe(path_to_file)
return result["text"]
@app.get("/")
def root():
return {"Hello": "World"}
@app.post("/upload/")
async def create_upload_file(file: UploadFile = File(...)):
# Process the file here
content = await file.read()
with open("tempfile.wav", "wb") as f:
f.write(contents)
response_text = speech_to_text("tempfile.wav")
return PlainTextResponse(content=response_text, status_code=200) |