https://github.com/candlewill/aivoice
Deep CNN networks for Speech Synthesis
https://github.com/candlewill/aivoice
cnn deep-learning tts
Last synced: 8 months ago
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Deep CNN networks for Speech Synthesis
- Host: GitHub
- URL: https://github.com/candlewill/aivoice
- Owner: candlewill
- Created: 2017-11-10T02:06:13.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-11-15T03:23:42.000Z (over 8 years ago)
- Last Synced: 2025-03-30T05:41:16.481Z (about 1 year ago)
- Topics: cnn, deep-learning, tts
- Language: Python
- Size: 29.3 KB
- Stars: 49
- Watchers: 6
- Forks: 15
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# Deep Voice 3
This is a tensorflow implementation of [DEEP VOICE 3: 2000-SPEAKER NEURAL TEXT-TO-SPEECH](https://arxiv.org/pdf/1710.07654.pdf). For now, we are just focusing on single speaker synthesis.
## Requirement
* Tensorflow >= 1.2
* Python >= 3.0
## Dataset
[The LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset)
## Pre-process
Download and unzip the LJ Speech Dataset. Run:
```
python prepro.py
```
Note: Make sure that we have unzipped the dataset into the same foler of `prepro.py`.
After this, we would get three new folders:
```
├── dones [New]
├── mags [New]
├── mels [New]
├── metadata.csv
├── README
└── wavs
```
## Training
Training data is loaded from `./LJSpeech-1.0/metadata.csv`, `./LJSpeech-1.0/mels`, `./LJSpeech-1.0/dones`, `./LJSpeech-1.0/mags` as default. If we want to change the loading path, we could change the config in `class Hyperparams`.
To train the model, we use this command:
```
python train.py
```
## Pre-trained Model
Currently, we can not get good result. However, we still provide our pre-trained model in case someone is interested in it.
[Pre-trained Model](https://cnbj1.fds.api.xiaomi.com/tts/ExternalLink/Github/pre_trained_model.tar.gz).
Its attention figure is as follows:
All the attention figures generated at training are included in the pre-trained model zipped file.
## File Description
* hyperparams.py: hyper parameters
* prepro.py: creates inputs and targets, i.e., mel spectrogram, magnitude, and dones.
* data_load.py
* utils.py: several custom operational functions.
* modules.py: building blocks for the networks.
* networks.py: encoder, decoder, and converter
* train.py: train
* synthesize.py: inference
* test_sents.txt: some test sentences in the paper.
## Reference
Most of the code is borrowed from [Kyubyong/deepvoice3](https://github.com/Kyubyong/deepvoice3).