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import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration
import soundfile as sf
from fastapi import FastAPI, File, UploadFile, Form
import uvicorn
import requests
import os
from datetime import datetime
# Initialize FastAPI app
app = FastAPI()
# Print initialization of the application
print("FastAPI application started.")
# Load the Whisper model and processor
model_name = "openai/whisper-base"
print(f"Loading Whisper model: {model_name}")
try:
processor = WhisperProcessor.from_pretrained(model_name)
model = WhisperForConditionalGeneration.from_pretrained(model_name)
print(f"Model {model_name} successfully loaded.")
except Exception as e:
print(f"Error loading the model: {e}")
raise e
# Move model to the appropriate device (GPU if available)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
print(f"Model is using device: {device}")
@app.post("/transcribe/")
def transcribe_audio_url(audio_url: str = Form(...)):
# Download the audio file from the provided URL
try:
response = requests.get(audio_url)
if response.status_code != 200:
return {"error": f"Failed to download audio from URL. Status code: {response.status_code}"}
print(f"Successfully downloaded audio from URL: {audio_url}")
audio_data = io.BytesIO(response.content) # Store audio data in memory
except Exception as e:
print(f"Error downloading the audio file: {e}")
return {"error": f"Error downloading the audio file: {e}"}
# Process the audio
try:
audio_input, _ = sf.read(audio_data) # Read the audio from the in-memory BytesIO
print(f"Audio file from URL successfully read.")
except Exception as e:
print(f"Error reading the audio file: {e}")
return {"error": f"Error reading the audio file: {e}"}
# Preprocess the audio for Whisper
try:
inputs = processor(audio_input, return_tensors="pt", sampling_rate=16000)
print(f"Audio file preprocessed for transcription.")
except Exception as e:
print(f"Error processing the audio file: {e}")
return {"error": f"Error processing the audio file: {e}"}
# Move inputs to the appropriate device
inputs = {key: value.to(device) for key, value in inputs.items()}
print("Inputs moved to the appropriate device.")
# Generate the transcription
try:
with torch.no_grad():
predicted_ids = model.generate(inputs["input_features"])
print("Transcription successfully generated.")
except Exception as e:
print(f"Error during transcription generation: {e}")
return {"error": f"Error during transcription generation: {e}"}
# Decode the transcription
try:
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
print("Transcription successfully decoded.")
except Exception as e:
print(f"Error decoding the transcription: {e}")
return {"error": f"Error decoding the transcription: {e}"}
return {"transcription": transcription}
@app.get("/")
def read_root():
return {"message": "Welcome to the Whisper transcription API"}
if __name__ == "__main__":
# Print when starting the FastAPI server
print("Starting FastAPI server with Uvicorn...")
# Run the FastAPI app on the default port (7860)
uvicorn.run(app, host="0.0.0.0", port=7860)
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