app.py

import gradio as gr
import torch
import torchaudio
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline

# Load MMS ASR model
MODEL_NAME = "facebook/mms-1b-all"
device = "cuda" if torch.cuda.is_available() else "cpu"

processor = AutoProcessor.from_pretrained(MODEL_NAME)
model = AutoModelForSpeechSeq2Seq.from_pretrained(MODEL_NAME).to(device)
asr_pipeline = pipeline("automatic-speech-recognition", model=model, processor=processor, torch_dtype=torch.float16, device=0 if device == "cuda" else -1)

# Speech-to-text function
def transcribe(audio):
    waveform, sr = torchaudio.load(audio)
    waveform = torchaudio.transforms.Resample(sr, 16000)(waveform)  # Ensure 16kHz sample rate
    text = asr_pipeline({"array": waveform.squeeze().numpy(), "sampling_rate": 16000})["text"]
    return text

# Gradio UI
gr.Interface(
    fn=transcribe,
    inputs=gr.Audio(source="microphone", type="filepath"),
    outputs=gr.Text(label="Transcription"),
    title="Real-time Speech-to-Text",
    description="Speak into your microphone and see the transcribed text.",
).launch()