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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"] | |
| def root(): | |
| return {"Hello": "World"} | |
| 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) |