app.py

import streamlit as st
import torchaudio
from transformers import WhisperProcessor, WhisperForConditionalGeneration

# Load the Whisper model and processor
processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")

# Title
st.title('Audio to Text Transcription')

# Sidebar for file upload
st.sidebar.title("Upload your audio file")
uploaded_file = st.sidebar.file_uploader("Choose an audio file", type=["mp3", "wav", "mp4", "m4a"])

if uploaded_file:
    st.sidebar.audio(uploaded_file)

    # Process the uploaded file
    audio_tensor, sampling_rate = torchaudio.load(uploaded_file)
    resampler = torchaudio.transforms.Resample(sampling_rate, 16000)
    resampled_waveform = resampler(audio_tensor)

    segment_duration = 120  # Segment duration in seconds (2 minutes)
    num_segments = len(resampled_waveform[0]) // (segment_duration * 16000)
    segment_transcriptions = []

    # Transcribe each segment
    for i in range(num_segments):
        start = i * segment_duration * 16000
        end = min(len(resampled_waveform[0]), (i + 1) * segment_duration * 16000)
        segment = resampled_waveform[0][start:end]

        # Transcribe the segment
        input_features = processor(
            segment, sampling_rate=16000, return_tensors="pt"
        ).input_features

        predicted_ids = model.generate(input_features)
        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)

        segment_transcriptions.append(transcription[0])

    # Combine segment transcriptions into the full transcript
    full_transcript = " ".join(segment_transcriptions)

    # Display the transcript
    st.header("Transcription")
    st.write(full_transcript)