File size: 3,106 Bytes
a9384d7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 |
# Tutorial For Nervous Beginners
## Installation
User friendly installation. Recommended only for synthesizing voice.
```bash
$ pip install TTS
```
Developer friendly installation.
```bash
$ git clone https://github.com/coqui-ai/TTS
$ cd TTS
$ pip install -e .
```
## Training a `tts` Model
A breakdown of a simple script that trains a GlowTTS model on the LJspeech dataset. See the comments for more details.
### Pure Python Way
0. Download your dataset.
In this example, we download and use the LJSpeech dataset. Set the download directory based on your preferences.
```bash
$ python -c 'from TTS.utils.downloaders import download_ljspeech; download_ljspeech("../recipes/ljspeech/");'
```
1. Define `train.py`.
```{literalinclude} ../../recipes/ljspeech/glow_tts/train_glowtts.py
```
2. Run the script.
```bash
CUDA_VISIBLE_DEVICES=0 python train.py
```
- Continue a previous run.
```bash
CUDA_VISIBLE_DEVICES=0 python train.py --continue_path path/to/previous/run/folder/
```
- Fine-tune a model.
```bash
CUDA_VISIBLE_DEVICES=0 python train.py --restore_path path/to/model/checkpoint.pth
```
- Run multi-gpu training.
```bash
CUDA_VISIBLE_DEVICES=0,1,2 python -m trainer.distribute --script train.py
```
### CLI Way
We still support running training from CLI like in the old days. The same training run can also be started as follows.
1. Define your `config.json`
```json
{
"run_name": "my_run",
"model": "glow_tts",
"batch_size": 32,
"eval_batch_size": 16,
"num_loader_workers": 4,
"num_eval_loader_workers": 4,
"run_eval": true,
"test_delay_epochs": -1,
"epochs": 1000,
"text_cleaner": "english_cleaners",
"use_phonemes": false,
"phoneme_language": "en-us",
"phoneme_cache_path": "phoneme_cache",
"print_step": 25,
"print_eval": true,
"mixed_precision": false,
"output_path": "recipes/ljspeech/glow_tts/",
"datasets":[{"formatter": "ljspeech", "meta_file_train":"metadata.csv", "path": "recipes/ljspeech/LJSpeech-1.1/"}]
}
```
2. Start training.
```bash
$ CUDA_VISIBLE_DEVICES="0" python TTS/bin/train_tts.py --config_path config.json
```
## Training a `vocoder` Model
```{literalinclude} ../../recipes/ljspeech/hifigan/train_hifigan.py
```
❗️ Note that you can also use ```train_vocoder.py``` as the ```tts``` models above.
## Synthesizing Speech
You can run `tts` and synthesize speech directly on the terminal.
```bash
$ tts -h # see the help
$ tts --list_models # list the available models.
```
You can call `tts-server` to start a local demo server that you can open it on
your favorite web browser and 🗣️.
```bash
$ tts-server -h # see the help
$ tts-server --list_models # list the available models.
```
|