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from transformers import pipeline
from datasets import load_dataset
import torch
import streamlit as st
@st.cache_resource
def load_speech_model():
synthesiser = pipeline("text-to-speech", "microsoft/speecht5_tts")
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
return synthesiser, speaker_embedding
def speech_elements():
synthesiser, speaker_embedding = load_speech_model()
text = st.text_area('Enter English text here')
st.write(f'You wrote {len(text)} characters.')
if st.button('Speech'):
speech = synthesiser(text, forward_params={"speaker_embeddings": speaker_embedding})
st.audio(speech['audio'], sample_rate=speech['sampling_rate'])
speech_elements()
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