app.py

import requests
import gradio as gr
import numpy as np
import io
import soundfile as sf
import os

HF_ACCESS_TOKEN = os.environ['HF_ACCESS_TOKEN']
API_URL = "https://api-inference.huggingface.co/models/openai/whisper-large-v3"
headers = {"Authorization": f"Bearer {HF_ACCESS_TOKEN}"}

def query(audio_data):
    with io.BytesIO() as f:
        sf.write(f, audio_data[1], audio_data[0], format='wav')
        data = f.getvalue()
    
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()['text']

def transcribe(audio_data):
    global output_text
    print("Received audio data:", audio_data)
    
    if audio_data is None:
        print("Audio data is None. Check the microphone and input configuration.")
        return None
    
    sr, y = audio_data
    y = y.astype(np.float32)
    y /= np.max(np.abs(y))
    
    # Add your transcription logic here if needed
    transcription = query(audio_data)    
    return transcription

dark_minimalist = gr.Theme.from_hub("Taithrah/Minimal")

iface = gr.Interface(theme=dark_minimalist,
    fn=transcribe,
    inputs=gr.Microphone(label="Speak into the microphone",),
    outputs="text",
    allow_flagging="never",
    css="""
    footer {
        visibility: hidden;
    }
    .content-container::-webkit-scrollbar {
        display: none;
    }
    body {
    overflow: hidden !important;
    }
    """
)

iface.launch()