app.py

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

# Load ASR pipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
asr_pipeline = pipeline("automatic-speech-recognition", model="monadical-labs/whisper-medium.en", device=device)

def transcribe(audio):
    if audio is None:
        return "Error: No audio file received."
    
    # Load the audio file correctly
    audio_data, sr = librosa.load(audio, sr=16000)  # Resample to 16kHz (Whisper requirement)
    
    # Process the audio
    text = asr_pipeline(audio_data)["text"]
    return text

# Create Gradio interface
demo = gr.Interface(fn=transcribe, inputs=gr.Audio(type="filepath"), outputs="text")
demo.launch()