|
--- |
|
tags: |
|
- TensorFlowTTS |
|
- audio |
|
- text-to-speech |
|
- text-to-mel |
|
language: eng |
|
license: apache-2.0 |
|
datasets: |
|
- LJSpeech |
|
widget: |
|
- text: "How are you?" |
|
--- |
|
|
|
|
|
|
|
This repository provides a pretrained [FastSpeech](https://arxiv.org/abs/1905.09263) trained on LJSpeech dataset (ENG). For a detail of the model, we encourage you to read more about |
|
[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS). |
|
|
|
|
|
## Install TensorFlowTTS |
|
First of all, please install TensorFlowTTS with the following command: |
|
``` |
|
pip install TensorFlowTTS |
|
``` |
|
|
|
### Converting your Text to Mel Spectrogram |
|
```python |
|
import numpy as np |
|
import soundfile as sf |
|
import yaml |
|
|
|
import tensorflow as tf |
|
|
|
from tensorflow_tts.inference import AutoProcessor |
|
from tensorflow_tts.inference import TFAutoModel |
|
|
|
processor = AutoProcessor.from_pretrained("ruslanmv/tts-fastspeech-ljspeech-en") |
|
fastspeech = TFAutoModel.from_pretrained("ruslanmv/tts-fastspeech-ljspeech-en") |
|
|
|
text = "How are you?" |
|
|
|
input_ids = processor.text_to_sequence(text) |
|
|
|
mel_before, mel_after, duration_outputs = fastspeech.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), |
|
) |
|
``` |
|
|
|
|
|
|
|
|
|
|
|
|
