| from transformers import pipeline | |
| from datasets import load_dataset | |
| import torch | |
| import streamlit as st | |
| 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() | |