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# Install necessary libraries
import os
import numpy as np
from speechbrain.pretrained import Tacotron2, HIFIGAN
from scipy.io.wavfile import write
import streamlit as st
# Load TTS and vocoder models
@st.cache_resource # Cache the models to avoid reloading
def load_models():
tacotron2 = Tacotron2.from_hparams(source="speechbrain/tts-tacotron2-ljspeech", savedir="tmp_tts")
hifi_gan = HIFIGAN.from_hparams(source="speechbrain/tts-hifigan-ljspeech", savedir="tmp_vocoder")
return tacotron2, hifi_gan
tacotron2, hifi_gan = load_models()
# Text-to-Speech function
def text_to_speech(text):
try:
# Generate mel spectrogram
mel_output, _, _ = tacotron2.encode_text(text)
# Generate waveform from mel spectrogram
waveforms = hifi_gan.decode_batch(mel_output)
# Convert waveform to numpy format
waveform = waveforms.squeeze().cpu().numpy()
# Normalize waveform to range [-1, 1]
waveform = waveform / np.max(np.abs(waveform))
# Save waveform to a .wav file
output_path = "output.wav"
write(output_path, 22050, (waveform * 32767).astype(np.int16))
return output_path
except Exception as e:
st.error(f"Error during text-to-speech generation: {e}")
return None
# Streamlit UI
st.title("Text-to-Speech Application")
st.write("Enter text below and convert it to speech!")
# Input field
text_input = st.text_area("Enter Text:", "Hello, welcome to the Text-to-Speech app!")
if st.button("Generate Speech"):
if text_input.strip():
output_audio = text_to_speech(text_input)
if output_audio:
st.audio(output_audio, format="audio/wav")
else:
st.error("Failed to generate audio. Please check the input text.")
else:
st.warning("Please enter some text to generate speech.")
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