app.py

import torch
import pickle
import whisper
import streamlit as st
import torchaudio as ta

from io import BytesIO
from transformers import WhisperProcessor, WhisperForConditionalGeneration

# Set up device and dtype
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if device == "cuda:0" else torch.float32

SAMPLING_RATE = 16000

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

# Title of the app
st.title("Audio Player with Live Transcription")

# Sidebar for file uploader and submit button
st.sidebar.header("Upload Audio Files")
uploaded_files = st.sidebar.file_uploader("Choose audio files", type=["mp3", "wav"], accept_multiple_files=True)
submit_button = st.sidebar.button("Submit")

# Session state to hold data
if 'audio_files' not in st.session_state:
    st.session_state.audio_files = []
    st.session_state.transcriptions = {}
    st.session_state.translations = {}
    st.session_state.detected_languages = []
    st.session_state.waveforms = []


def detect_language(audio_file):
    whisper_model = whisper.load_model("small")
    trimmed_audio = whisper.pad_or_trim(audio_file)
    mel = whisper.log_mel_spectrogram(trimmed_audio).to(whisper_model.device)
    _, probs = whisper_model.detect_language(mel[0])
    detected_lang = max(probs, key=probs.get)
    print(f"Detected language: {detected_lang}")
    return detected_lang


# Process uploaded files
if submit_button and uploaded_files is not None:
    st.session_state.audio_files = uploaded_files
    st.session_state.detected_languages = []

    for uploaded_file in uploaded_files:
        waveform, sampling_rate = ta.load(BytesIO(uploaded_file.read()))
        if sampling_rate != SAMPLING_RATE:
            waveform = ta.functional.resample(waveform, orig_freq=sampling_rate, new_freq=SAMPLING_RATE)

        st.session_state.waveforms.append(waveform)
        detected_language = detect_language(waveform)
        st.session_state.detected_languages.append(detected_language)

# Display uploaded files and options
if 'audio_files' in st.session_state and st.session_state.audio_files:
    for i, uploaded_file in enumerate(st.session_state.audio_files):
        col1, col2 = st.columns([1, 3])

        with col1:
            st.write(f"**File name**: {uploaded_file.name}")
            st.audio(BytesIO(uploaded_file.read()), format=uploaded_file.type)
            st.write(f"**Detected Language**: {st.session_state.detected_languages[i]}")

        with col2:
            # import pdb;pdb.set_trace()
            input_features = processor(st.session_state.waveforms[i][0], sampling_rate=SAMPLING_RATE, return_tensors='pt').input_features

            if st.button(f"Transcribe {uploaded_file.name}"):
                predicted_ids = model.generate(input_features)
                transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
                st.session_state.transcriptions[i] = transcription

            if st.session_state.transcriptions.get(i):
                st.write("**Transcription**:")
                for line in st.session_state.transcriptions[i]:
                    st.write(line)

            if st.button(f"Translate {uploaded_file.name}"):
                with open('languages.pkl', 'rb') as f:
                    lang_dict = pickle.load(f)
                detected_language_name = lang_dict[st.session_state.detected_languages[i]]

                forced_decoder_ids = processor.get_decoder_prompt_ids(language=detected_language_name, task="translate")
                predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
                translation = processor.batch_decode(predicted_ids, skip_special_tokens=True)
                st.session_state.translations[i] = translation

            if st.session_state.translations.get(i):
                st.write("**Translation**:")
                for line in st.session_state.translations[i]:
                    st.write(line)