# 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 training a GlowTTS model on LJspeech dataset. See the comments for the explanation of
each line.
### Pure Python Way
1. Define `train.py`.
```python
import os
# GlowTTSConfig: all model related values for training, validating and testing.
from TTS.tts.configs import GlowTTSConfig
# BaseDatasetConfig: defines name, formatter and path of the dataset.
from TTS.tts.configs import BaseDatasetConfig
# init_training: Initialize and setup the training environment.
# Trainer: Where the ✨️ happens.
# TrainingArgs: Defines the set of arguments of the Trainer.
from TTS.trainer import init_training, Trainer, TrainingArgs
# we use the same path as this script as our training folder.
output_path = os.path.dirname(os.path.abspath(__file__))
# set LJSpeech as our target dataset and define its path so that the Trainer knows what data formatter it needs.
dataset_config = BaseDatasetConfig(name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/"))
# Configure the model. Every config class inherits the BaseTTSConfig to have all the fields defined for the Trainer.
config = GlowTTSConfig(
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=os.path.join(output_path, "phoneme_cache"),
print_step=25,
print_eval=True,
mixed_precision=False,
output_path=output_path,
datasets=[dataset_config]
)
# initialize the audio processor used for feature extraction and audio I/O.
# It is mainly used by the dataloader and the training loggers.
ap = AudioProcessor(**config.audio.to_dict())
# load a list of training samples
# Each sample is a list of ```[text, audio_file_path, speaker_name]```
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
# initialize the model
# Models only takes the config object as input.
model = GlowTTS(config)
# Initiate the Trainer.
# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
# distributed training etc.
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
)
# And kick it 🚀
trainer.fit()
```
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.tar
```
- Run multi-gpu training.
```bash
CUDA_VISIBLE_DEVICES=0,1,2 python TTS/bin/distribute.py --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":[{"name": "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
```python
import os
from TTS.trainer import Trainer, TrainingArgs
from TTS.utils.audio import AudioProcessor
from TTS.vocoder.configs import HifiganConfig
from TTS.vocoder.datasets.preprocess import load_wav_data
from TTS.vocoder.models.gan import GAN
output_path = os.path.dirname(os.path.abspath(__file__))
config = HifiganConfig(
batch_size=32,
eval_batch_size=16,
num_loader_workers=4,
num_eval_loader_workers=4,
run_eval=True,
test_delay_epochs=5,
epochs=1000,
seq_len=8192,
pad_short=2000,
use_noise_augment=True,
eval_split_size=10,
print_step=25,
print_eval=False,
mixed_precision=False,
lr_gen=1e-4,
lr_disc=1e-4,
data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
output_path=output_path,
)
# init audio processor
ap = AudioProcessor(**config.audio.to_dict())
# load training samples
eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
# init model
model = GAN(config)
# init the trainer and 🚀
trainer = Trainer(
TrainingArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
)
trainer.fit()
```
❗️ 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.
```
