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from TTSInferencing import TTSInferencing | |
from speechbrain.inference.vocoders import HIFIGAN | |
# import torchaudio | |
import streamlit as st | |
import numpy as np | |
tts_model = TTSInferencing.from_hparams(source="./", | |
hparams_file='./hyperparams.yaml', | |
pymodule_file='./module_classes.py', | |
# savedir="./", | |
) | |
hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech") | |
# text = ["Hello I am a girl", "How is your day going", "I hope you are doing well"] | |
# Input text | |
text_input = st.text_input("Enter your text here") | |
# Check if the input is a list | |
if isinstance(text_input, str): | |
# Convert the input to a list | |
text = [text_input] | |
else: | |
text = text_input | |
if st.button("Synthesize Speech"): | |
if text: | |
mel_outputs = tts_model.encode_batch(text) | |
waveforms = hifi_gan.decode_batch(mel_outputs) | |
waveform = waveforms[0].squeeze(1).numpy() | |
# Normalize the waveform to the range [-1, 1] if necessary | |
if np.max(np.abs(waveform)) > 1.0: | |
waveform /= np.max(np.abs(waveform)) | |
# Display the audio widget to play the synthesized speech | |
st.audio(waveform, format="audio/wav", sample_rate = 22050) | |
else: | |
st.error("Please enter text to get the speech.") |