Multi-band MelGAN trained on KSS (Korean)
This repository provides a pretrained Multi-band MelGAN trained on KSS dataset (ko). For a detail of the model, we encourage you to read more about TensorFlowTTS.
Install TensorFlowTTS
First of all, please install TensorFlowTTS with the following command:
pip install TensorFlowTTS
Converting your Text to Wav
import soundfile as sf
import numpy as np
import tensorflow as tf
from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel
processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-kss-ko")
tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-kss-ko")
mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-kss-ko")
text = "신은 우리의 수학 문제에는 관심이 없다. 신은 다만 경험적으로 통합할 뿐이다."
input_ids = processor.text_to_sequence(text)
# tacotron2 inference (text-to-mel)
decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
)
# melgan inference (mel-to-wav)
audio = mb_melgan.inference(mel_outputs)[0, :, 0]
# save to file
sf.write('./audio.wav', audio, 22050, "PCM_16")
Referencing Multi-band MelGAN
@misc{yang2020multiband,
title={Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech},
author={Geng Yang and Shan Yang and Kai Liu and Peng Fang and Wei Chen and Lei Xie},
year={2020},
eprint={2005.05106},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
Referencing TensorFlowTTS
@misc{TFTTS,
author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata,
Trinh Le and Yunchao He},
title = {TensorflowTTS},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
}