app.py

# app.py

import gradio as gr
import torch
import whisper

# Load a fast Whisper model
model = whisper.load_model("small")  # You can use "tiny" if you want even faster

def transcribe_audio(audio):
    # Audio is received as a tuple (sample_rate, numpy_array)
    audio = audio[1]  # Get the raw audio waveform

    # Whisper expects 16000 Hz sample rate
    result = model.transcribe(audio, fp16=torch.cuda.is_available())
    text = result["text"]
    return text

# Gradio Interface
iface = gr.Interface(
    fn=transcribe_audio,
    inputs=gr.Audio(sources=["microphone"], type="numpy", streaming=True),
    outputs=gr.Textbox(label="Recognized Text"),
    live=True,  # Important for real-time streaming
    title="Real-time Voice to Text",
    description="Speak into your microphone and get real-time transcription!",
)

if __name__ == "__main__":
    iface.launch()