metadata

tags:
  - tensorflowtts
  - audio
  - text-to-speech
  - text-to-mel
language: vi
license: mit
datasets:
  - infore

Install TensorFlowTTS

pip install TensorFlowTTS

Converting your Text to Mel Spectrogram

import numpy as np
import soundfile as sf
import yaml
import IPython.display as ipd

import tensorflow as tf

from tensorflow_tts.inference import AutoProcessor
from tensorflow_tts.inference import TFAutoModel

processor = AutoProcessor.from_pretrained("MarcNg/fastspeech2-vi-infore")
fastspeech2 = TFAutoModel.from_pretrained("MarcNg/fastspeech2-vi-infore")

text = "xin chào đây là một ví dụ về chuyển đổi văn bản thành giọng nói"

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),
)

Bonus: Convert Mel Spectrogram to Speech

mb_melgan = TFAutoModel.from_pretrained("tensorspeech/tts-mb_melgan-ljspeech-en")

audio_before = mb_melgan.inference(mel_before)[0, :, 0]
audio_after = mb_melgan.inference(mel_after)[0, :, 0]

sf.write("audio_before.wav", audio_before, 22050, "PCM_16")
sf.write("audio_after.wav", audio_after, 22050, "PCM_16")

ipd.Audio('audio_after.wav')

Referencing FastSpeech2

@misc{ren2021fastspeech,
      title={FastSpeech 2: Fast and High-Quality End-to-End Text to Speech}, 
      author={Yi Ren and Chenxu Hu and Xu Tan and Tao Qin and Sheng Zhao and Zhou Zhao and Tie-Yan Liu},
      year={2021},
      eprint={2006.04558},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}