app.py

import torch
import torchaudio
from torchaudio.transforms import Resample
from transformers import Speech2TextProcessor, Speech2TextForConditionalGeneration
import streamlit as st
from streamlit_audio_recorder import st_audio_recorder

def preprocess_audio(audio_bytes, sample_rate=16000):
    # Load audio and convert to mono if necessary
    waveform, _ = torchaudio.load(audio_bytes, normalize=True)
    if waveform.size(0) > 1:
        waveform = torch.mean(waveform, dim=0, keepdim=True)
    
    # Resample if needed
    if waveform.shape[1] != sample_rate:
        resampler = Resample(orig_freq=waveform.shape[1], new_freq=sample_rate)
        waveform = resampler(waveform)
    
    return waveform

def transcribe_audio(audio_bytes):
    model = Speech2TextForConditionalGeneration.from_pretrained("facebook/s2t-small-mustc-en-fr-st")
    processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-mustc-en-fr-st")

    # Preprocess audio
    input_features = preprocess_audio(audio_bytes)

    # Tokenize audio
    inputs = processor(input_features.squeeze(0), return_tensors="pt", padding=True)

    # Generate transcription
    generated_ids = model.generate(inputs.input_features)
    translation = processor.batch_decode(generated_ids, skip_special_tokens=True)

    return translation

st.title("Audio to Text Transcription with Recording")

# Use the st_audio_recorder widget to record audio
audio_bytes = st_audio_recorder(sample_rate=16000, codec="wav", show_playback_controls=True)

# Display the recorded audio
if audio_bytes:
    st.audio(audio_bytes, format="audio/wav")

    transcription = transcribe_audio(audio_bytes)

    if transcription:
        st.write("Transcription:")
        st.write(transcription[0])
    else:
        st.write("Error: Failed to transcribe audio.")
else:
    st.write("Please record an audio.")