app.py

import tempfile
import torchaudio
import whisper
import gradio as gr

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

def transcribe_audio(audio_file):
    # Save uploaded audio
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp_wav:
        signal, sr = torchaudio.load(audio_file)

        # Resample if needed
        if sr != 16000:
            signal = torchaudio.transforms.Resample(sr, 16000)(signal)
        if signal.shape[0] > 1:
            signal = signal.mean(dim=0, keepdim=True)

        torchaudio.save(tmp_wav.name, signal, 16000)

        # Transcribe with Whisper
        result = model.transcribe(tmp_wav.name)
        transcript = result['text']

    # Save transcript to file
    with tempfile.NamedTemporaryFile(suffix=".txt", delete=False, mode="w") as f:
        f.write(transcript)
        transcript_file_path = f.name

    return transcript, transcript_file_path

# Gradio Interface
app = gr.Interface(
    fn=transcribe_audio,
    inputs=gr.Audio(type="filepath", label="🎙️ Upload Meeting Audio"),
    outputs=[
        gr.Textbox(label="📝 Transcript", lines=10),
        gr.File(label="⬇️ Download Transcript (.txt)")
    ],
    title="🎧 Meeting Summarizer (STT Only)",
    description="Upload your meeting audio and get a clean transcript you can read and download.",
    theme="soft"
)

app.launch()