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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/).") | |