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import gradio as gr
from transformers import pipeline
import torch
import os
import tempfile
import soundfile as sf
# Load the Whisper model once during startup
device = 0 if torch.cuda.is_available() else -1
asr_pipeline = pipeline(model="openai/whisper-small", device=device)
# Function to handle the transcription process
def transcribe_audio(audio_file):
# Create a temporary file to save the uploaded audio
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio_file:
temp_audio_file.write(audio_file.read())
temp_file_path = temp_audio_file.name
# Perform the transcription
transcription = asr_pipeline(temp_file_path)
# Remove the temporary file
os.remove(temp_file_path)
# Return the transcription result
return transcription['text']
# Create Gradio interface
interface = gr.Interface(
fn=transcribe_audio, # The function to call when audio is uploaded
inputs=gr.Audio(source="upload", type="file"), # Input type: audio file
outputs="text", # Output type: text (transcription)
title="Whisper Audio Transcription", # Title of the Gradio interface
description="Upload an audio file to get a transcription using OpenAI's Whisper model"
)
# Launch the Gradio interface
interface.launch() |