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Doc update (#889)
* Link source files from the docs * Update glowTTS recipes for docs * Add dataset downloaders
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Eren Gölge
@@ -18,80 +18,21 @@ $ pip install -e .
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## Training a `tts` Model
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A breakdown of a simple script training a GlowTTS model on LJspeech dataset. See the comments for the explanation of
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each line.
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A breakdown of a simple script that trains a GlowTTS model on the LJspeech dataset. See the comments for more details.
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### Pure Python Way
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0. Download your dataset.
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In this example, we download and use the LJSpeech dataset. Set the download directory based on your preferences.
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```bash
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$ python -c 'from TTS.utils.downloaders import download_ljspeech; download_ljspeech("../recipes/ljspeech/");'
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```
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1. Define `train.py`.
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```python
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import os
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# GlowTTSConfig: all model related values for training, validating and testing.
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from TTS.tts.configs.glow_tts_config import GlowTTSConfig
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# BaseDatasetConfig: defines name, formatter and path of the dataset.
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from TTS.tts.configs.shared_config import BaseDatasetConfig
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# init_training: Initialize and setup the training environment.
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# Trainer: Where the ✨️ happens.
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# TrainingArgs: Defines the set of arguments of the Trainer.
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from TTS.trainer import init_training, Trainer, TrainingArgs
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# we use the same path as this script as our training folder.
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output_path = os.path.dirname(os.path.abspath(__file__))
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# set LJSpeech as our target dataset and define its path so that the Trainer knows what data formatter it needs.
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dataset_config = BaseDatasetConfig(name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/"))
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# Configure the model. Every config class inherits the BaseTTSConfig to have all the fields defined for the Trainer.
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config = GlowTTSConfig(
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batch_size=32,
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eval_batch_size=16,
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num_loader_workers=4,
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num_eval_loader_workers=4,
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run_eval=True,
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test_delay_epochs=-1,
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epochs=1000,
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text_cleaner="english_cleaners",
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use_phonemes=False,
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phoneme_language="en-us",
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phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
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print_step=25,
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print_eval=True,
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mixed_precision=False,
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output_path=output_path,
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datasets=[dataset_config]
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)
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# initialize the audio processor used for feature extraction and audio I/O.
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# It is mainly used by the dataloader and the training loggers.
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ap = AudioProcessor(**config.audio.to_dict())
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# load a list of training samples
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# Each sample is a list of ```[text, audio_file_path, speaker_name]```
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train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
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# initialize the model
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# Models only takes the config object as input.
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model = GlowTTS(config)
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# Initiate the Trainer.
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# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
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# distributed training, etc.
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trainer = Trainer(
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TrainingArgs(),
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config,
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output_path,
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model=model,
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train_samples=train_samples,
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eval_samples=eval_samples,
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training_assets={"audio_processor": ap},
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)
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# And kick it 🚀
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trainer.fit()
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```{literalinclude} ../../recipes/ljspeech/glow_tts/train_glowtts.py
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```
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2. Run the script.
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@@ -154,58 +95,7 @@ We still support running training from CLI like in the old days. The same traini
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## Training a `vocoder` Model
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```python
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import os
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from TTS.trainer import Trainer, TrainingArgs
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from TTS.utils.audio import AudioProcessor
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from TTS.vocoder.configs import HifiganConfig
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from TTS.vocoder.datasets.preprocess import load_wav_data
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from TTS.vocoder.models.gan import GAN
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output_path = os.path.dirname(os.path.abspath(__file__))
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config = HifiganConfig(
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batch_size=32,
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eval_batch_size=16,
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num_loader_workers=4,
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num_eval_loader_workers=4,
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run_eval=True,
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test_delay_epochs=5,
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epochs=1000,
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seq_len=8192,
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pad_short=2000,
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use_noise_augment=True,
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eval_split_size=10,
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print_step=25,
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print_eval=False,
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mixed_precision=False,
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lr_gen=1e-4,
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lr_disc=1e-4,
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data_path=os.path.join(output_path, "../LJSpeech-1.1/wavs/"),
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output_path=output_path,
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)
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# init audio processor
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ap = AudioProcessor(**config.audio.to_dict())
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# load training samples
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eval_samples, train_samples = load_wav_data(config.data_path, config.eval_split_size)
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# init model
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model = GAN(config)
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# init the trainer and 🚀
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trainer = Trainer(
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TrainingArgs(),
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config,
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output_path,
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model=model,
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train_samples=train_samples,
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eval_samples=eval_samples,
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training_assets={"audio_processor": ap},
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)
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trainer.fit()
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```{literalinclude} ../../recipes/ljspeech/hifigan/train_hifigan.py
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```
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❗️ Note that you can also use ```train_vocoder.py``` as the ```tts``` models above.
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