app.py

import streamlit as st
from transformers import pipeline
import tempfile
from pydub import AudioSegment
import numpy as np

# Load the ASR pipeline
@st.cache_resource
def load_asr_pipeline():
    asr_pipeline = pipeline("automatic-speech-recognition", model="Yehor/whisper-small-ukrainian")
    return asr_pipeline

st.title("Voice Recognition App using Whisper")

st.write("Upload an audio file and the Whisper model will transcribe it to text.")

# Load the ASR pipeline
asr_pipeline = load_asr_pipeline()
st.write("Model loaded successfully.")

# File uploader for audio file
uploaded_file = st.file_uploader("Choose an audio file", type=["wav", "mp3", "m4a"])

if uploaded_file is not None:
    # Save the uploaded file temporarily
    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
        temp_file.write(uploaded_file.read())
        temp_file_path = temp_file.name

    # Convert audio file to WAV format if necessary
    audio = AudioSegment.from_file(temp_file_path)
    temp_wav_path = tempfile.mktemp(suffix=".wav")
    audio.export(temp_wav_path, format="wav")

    st.audio(uploaded_file, format="audio/wav")

    st.write("Transcribing audio...")

    # Read the audio file
    audio_input = AudioSegment.from_file(temp_wav_path).set_frame_rate(16000).set_channels(1)
    audio_input = np.array(audio_input.get_array_of_samples(), dtype=np.float32)

    # Perform transcription
    result = asr_pipeline(audio_input)

    # Display transcription
    st.write("Transcription:")
    st.write(result['text'])