app.py

import gradio as gr
import logging
from transformers import pipeline
import torch

description = "Simple Speech Recognition App"
title = "This app allows users to record audio through the microphone or upload audio files to be transcribed into text. It uses the speech_recognition library to process audio and extract spoken words. Ideal for quick transcription of short speeches and audio notes."

asr = pipeline(task="automatic-speech-recognition",
               model="distil-whisper/distil-small.en")

# Adjusted function assuming 'asr' expects a file path as input
def transcribe_speech(audio_file_path):
    if not audio_file_path:
        logging.error("No audio file provided.")
        return "No audio found, please retry."
    try:
        logging.info(f"Processing file: {audio_file_path}")
        output = asr(audio_file_path)  # Assuming `asr` directly takes a file path
        return output["text"]
    except Exception as e:
        logging.error(f"Error during transcription: {str(e)}")
        return f"Error processing the audio file: {str(e)}"

logging.basicConfig(level=logging.INFO)

css = """
button {
    background-color: blue !important;
    color: white !important;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Row():
        gr.Markdown("# Simple Speech Recognition App")
    with gr.Row():
        gr.Markdown("### This app allows you to record or upload audio and see its transcription. Powered by the speech_recognition library.")
    with gr.Row():
        mic = gr.Audio(label="Record from Microphone or Upload File", type="filepath")
        transcribe_button = gr.Button("Transcribe Audio")
    with gr.Row():
        transcription = gr.Textbox(label="Transcription", lines=3, placeholder="Transcription will appear here...")

    transcribe_button.click(transcribe_speech, inputs=mic, outputs=transcription)

demo.launch(share=True)