app.py

import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
import streamlit as st
from audio_recorder_streamlit import audio_recorder

audio_bytes = audio_recorder(pause_threshold=3.0, sample_rate=16_000)

if audio_bytes:
    st.audio(audio_bytes, format="audio/wav")
    
    # Load pre-trained model and tokenizer
    tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
    model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

    # Tokenize the audio input
    input_values = tokenizer(audio_bytes, return_tensors='pt').input_values

    # Perform inference
    logits = model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)

    # Decode the audio to generate text
    transcriptions = tokenizer.decode(predicted_ids[0])

    if transcriptions is not None:
        st.write(transcriptions)
    else:
        st.write("Error: Failed to decode audio.")
else:
    st.write("No audio recorded.")