Spaces:
Sleeping
Sleeping
Yash Chauhan
commited on
Commit
·
9792cba
1
Parent(s):
3b3ca65
[updated] code
Browse files- Dockerfile +18 -0
- app.py +50 -0
- requirements.txt +6 -0
Dockerfile
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# Use an official Python image with CUDA support if using GPU
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FROM nvidia/cuda:12.1.1-devel-ubuntu22.04
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RUN useradd -m -u 1000 user
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USER user
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ENV PATH="/home/user/.local/bin:$PATH"
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WORKDIR /app
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RUN apt-get update && apt-get install -y \
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python3-pip \
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&& rm -rf /var/lib/apt/lists/*
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COPY --chown=user ./requirements.txt requirements.txt
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RUN pip install --no-cache-dir --upgrade -r requirements.txt
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COPY --chown=user . /app
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
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app.py
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import tempfile
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import numpy as np
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import torch
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from fastapi import FastAPI, File, UploadFile
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from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
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app = FastAPI()
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# Device configuration
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device = "cuda:0" if torch.cuda.is_available() else "cpu"
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torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
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# Load Whisper model
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model_id = "openai/whisper-large-v3-turbo"
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model = AutoModelForSpeechSeq2Seq.from_pretrained(
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model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
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).to(device)
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processor = AutoProcessor.from_pretrained(model_id)
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pipe = pipeline(
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"automatic-speech-recognition",
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model=model,
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tokenizer=processor.tokenizer,
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feature_extractor=processor.feature_extractor,
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torch_dtype=torch_dtype,
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device=device
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)
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@app.get("/")
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async def root():
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return {"message": "Welcome to Whisper API!"}
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@app.post("/transcribe/")
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async def transcribe_audio(file: UploadFile = File(...)):
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try:
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# Save the uploaded file temporarily
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with tempfile.NamedTemporaryFile(delete=True, suffix=".wav") as temp_audio:
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temp_audio.write(await file.read())
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temp_audio.flush()
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# Transcribe the audio
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result = pipe(temp_audio.name, return_timestamps="word")
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return {"transcription": result["chunks"]}
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except Exception as e:
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return {"error": str(e)}
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requirements.txt
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torch
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transformers
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fastapi
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uvicorn
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pydantic
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numpy
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