app.py

import gradio as gr
import os
import whisper
from pydub import AudioSegment

# Load the Whisper model
model = whisper.load_model("base")

# Function to process the uploaded audio file and perform transcription
def process_audio(upload):
    # Save the uploaded audio file
    file_path = "uploaded_audio"
    upload_path = f"{file_path}.mp3"
    upload.save(upload_path)

    # Convert the audio file to WAV format
    wav_path = f"{file_path}.wav"
    audio = AudioSegment.from_file(upload_path)
    audio.export(wav_path, format="wav")

    # Load the audio file and perform preprocessing
    audio = whisper.load_audio(wav_path)
    audio = whisper.pad_or_trim(audio)
    mel = whisper.log_mel_spectrogram(audio).to(model.device)

    # Detect the spoken language
    _, probs = model.detect_language(mel)
    detected_language = max(probs, key=probs.get)

    # Perform transcription using Whisper ASR
    options = whisper.DecodingOptions()
    result = whisper.decode(model, mel, options)
    transcription = result.text

    # Delete the temporary audio files
    os.remove(upload_path)
    os.remove(wav_path)

    return transcription

# Create a file input component for uploading the audio file
audio_input = gr.inputs.File(label="Upload Audio", accept=".wav, .mp3")

# Create a text output component for displaying the transcription
text_output = gr.outputs.Textbox(label="Transcription")

# Create a Gradio interface
gr.Interface(fn=process_audio, inputs=audio_input, outputs=text_output, title="Audio Transcription").launch()