app.py

import streamlit as st
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from transformers import MarianMTModel, MarianTokenizer
import soundfile as sf

# Device setup
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Load Whisper model
whisper_model_id = "openai/whisper-large-v3"
whisper_model = AutoModelForSpeechSeq2Seq.from_pretrained(
    whisper_model_id, torch_dtype=torch_dtype, use_safetensors=True
).to(device)
whisper_processor = AutoProcessor.from_pretrained(whisper_model_id)
whisper_pipe = pipeline(
    "automatic-speech-recognition",
    model=whisper_model,
    tokenizer=whisper_processor.tokenizer,
    feature_extractor=whisper_processor.feature_extractor,
    torch_dtype=torch_dtype,
    device=device,
)

# Load TTS model
tts_pipe = pipeline("text-to-speech", "microsoft/speecht5_tts")

# Load translation model
def load_translation_model(lang_code):
    model_name = f"Helsinki-NLP/opus-mt-en-{lang_code}"
    model = MarianMTModel.from_pretrained(model_name)
    tokenizer = MarianTokenizer.from_pretrained(model_name)
    return model, tokenizer

st.title("TextLangAudioGenerator")

# Text input
text_input = st.text_area("Enter text in English")

if text_input:
    # Select target language
    target_lang = st.selectbox(
        "Select target language",
        ["fr", "zh", "it", "ur", "hi"],  # Add more language codes as needed
        format_func=lambda x: {"fr": "French", "zh": "Chinese", "it": "Italian", "ur": "Urdu", "hi": "Hindi"}.get(x, x)
    )

    if target_lang:
        # Load translation model
        model, tokenizer = load_translation_model(target_lang)
        inputs = tokenizer(text_input, return_tensors="pt")
        translated = model.generate(**inputs)
        translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
        st.write(f"Translated text: {translated_text}")

        # Generate TTS
        speech = tts_pipe(translated_text)
        audio_path = "translated_speech.wav"
        sf.write(audio_path, speech["audio"], samplerate=speech["sampling_rate"])

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