app.py

import streamlit as st
from tensorflow_tts.inference import AutoProcessor, TFAutoModel
import tensorflow as tf

import numpy as np
import soundfile as sf
import yaml

processor = AutoProcessor.from_pretrained("MarcNg/fastspeech2-vi-infore")
fastspeech2 = TFAutoModel.from_pretrained("MarcNg/fastspeech2-vi-infore")
mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-ljspeech-en")

output = "output.wav"

st.header("MarcNg/fastspeech2-vi-infore Demo")

def tts(text):
  input_ids = processor.text_to_sequence(text)

  mel_before, mel_after, duration_outputs, _, _ = fastspeech2.inference(
      input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
      speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
      speed_ratios=tf.convert_to_tensor([1.0], dtype=tf.float32),
      f0_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
      energy_ratios =tf.convert_to_tensor([1.0], dtype=tf.float32),
  )
  return mel_after

text = st.text_input("Text to process")

if st.button("Speak"):
  mel_after = tts(text)
  audio_after = mb_melgan.inference(mel_after)[0, :, 0]
  sf.write(output, audio_after, 22050, 'PCM_16')
  st.audio(output, format='audio/wav')