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from fastapi import FastAPI, File, UploadFile, HTTPException
import nemo.collections.asr as nemo_asr
import torch
import shutil
import os
import uvicorn
app = FastAPI()
# Set the device (CPU or CUDA if available)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load and configure the ASR model
model = nemo_asr.models.ASRModel.from_pretrained(
"ai4bharat/indicconformer_stt_hi_hybrid_rnnt_large"
)
model.freeze() # Set to inference mode
model = model.to(device)
model.cur_decoder = "rnnt" # Use RNNT decoder
UPLOAD_FOLDER = "./uploads"
os.makedirs(UPLOAD_FOLDER, exist_ok=True) # Create upload folder if it doesn't exist
@app.post("/transcribe/")
async def transcribe_audio(file: UploadFile = File(...), source_lang: str = "hi"):
try:
# Save the uploaded audio file to disk
file_path = os.path.join(UPLOAD_FOLDER, file.filename)
with open(file_path, "wb") as buffer:
shutil.copyfileobj(file.file, buffer)
# Perform transcription using the provided language ID
transcription = model.transcribe(
[file_path], batch_size=1, language_id=source_lang
)[0]
# Cleanup the uploaded file
os.remove(file_path)
return {"transcription": transcription}
except Exception as e:
raise HTTPException(
status_code=500, detail=f"Error during transcription: {str(e)}"
)
# Run the app if inside a container
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=7860)
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