app.py

import streamlit as st
from transformers import pipeline

# Streamlit app title
st.title("Text to Speech Converter")

# User input for text to convert to speech
text_input = st.text_area("Enter text to convert to speech:")

# Load the Hugging Face TTS model
# Using a more reliable and commonly used model for TTS
tts_pipeline = pipeline("text-to-speech", model="microsoft/speecht5_tts")
from datasets import load_dataset

ds = load_dataset("Matthijs/cmu-arctic-xvectors")
# Button to generate speech
if st.button("Convert to Speech"):
    if text_input:
        # Generate the speech
        tts_output = tts_pipeline(text_input)

        # The output from the pipeline should be an array of speech chunks
        # Save the generated speech to a file
        audio_file_path = "output.wav"
        with open(audio_file_path, "wb") as f:
            f.write(tts_output[0]["array"])

        # Display the audio player in Streamlit
        st.audio(audio_file_path)
    else:
        st.warning("Please enter some text to convert.")

# Footer
st.markdown("Powered by [Hugging Face Transformers](https://huggingface.co/transformers/).")