coqui-tts/docs/source/training_a_model.md

Training a Model

1. Decide what model you want to use.

   Each model has a different set of pros and cons that define the run-time efficiency and the voice quality. It is up to you to decide what model servers your needs. Other than referring to the papers, one easy way is to test the 🐸TTS community models and see how fast and good each of the models. Or you can start a discussion on our communication channels.

2. Understand the configuration, its fields and values of your model.

   For instance, if you want to train a Tacotron model then see the TacotronConfig class and make sure you understand it.

3. Go to the recipes and check the recipe of your target model.

   Recipes do not promise perfect models but they provide a good start point for Nervous Beginners. A recipe script for GlowTTS using LJSpeech dataset looks like below. Let's be creative and call this train_glowtts.py.

   # train_glowtts.py
   
   import os
   
   from TTS.trainer import Trainer, TrainingArgs
   from TTS.tts.configs import BaseDatasetConfig, GlowTTSConfig
   from TTS.tts.datasets import load_tts_samples
   from TTS.tts.models.glow_tts import GlowTTS
   from TTS.utils.audio import AudioProcessor
   
   output_path = os.path.dirname(os.path.abspath(__file__))
   dataset_config = BaseDatasetConfig(
       name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
   )
   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="phoneme_cleaners",
       use_phonemes=True,
       phoneme_language="en-us",
       phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
       print_step=25,
       print_eval=False,
       mixed_precision=True,
       output_path=output_path,
       datasets=[dataset_config],
   )
   
   # init audio processor
   ap = AudioProcessor(**config.audio.to_dict())
   
   # load training samples
   train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
   
   # init model
   model = GlowTTS(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()
   
   

   You need to change fields of the BaseDatasetConfig to match your dataset and then update GlowTTSConfig fields as you need.

4. Run the training.

   $ CUDA_VISIBLE_DEVICES="0" python train_glowtts.py
   

   Notice that we set the GPU for the training by CUDA_VISIBLE_DEVICES environment variable. To see available GPUs on your system, you can use nvidia-smi command on the terminal.

   If you like to run a multi-gpu training using DDP back-end,

   $ CUDA_VISIBLE_DEVICES="0, 1, 2" python TTS/bin/distribute.py --script <path_to_your_script>/train_glowtts.py
   

   The example above runs a multi-gpu training using GPUs 0, 1, 2.

   Beginning of a training log looks like this:

   > Experiment folder: /your/output_path/-Juni-23-2021_02+52-78899209
   > Using CUDA:  True
   > Number of GPUs:  1
   > Setting up Audio Processor...
   | > sample_rate:22050
   | > resample:False
   | > num_mels:80
   | > min_level_db:-100
   | > frame_shift_ms:None
   | > frame_length_ms:None
   | > ref_level_db:20
   | > fft_size:1024
   | > power:1.5
   | > preemphasis:0.0
   | > griffin_lim_iters:60
   | > signal_norm:True
   | > symmetric_norm:True
   | > mel_fmin:0
   | > mel_fmax:None
   | > spec_gain:20.0
   | > stft_pad_mode:reflect
   | > max_norm:4.0
   | > clip_norm:True
   | > do_trim_silence:True
   | > trim_db:45
   | > do_sound_norm:False
   | > stats_path:None
   | > base:10
   | > hop_length:256
   | > win_length:1024
   | > Found 13100 files in /your/dataset/path/ljspeech/LJSpeech-1.1
   > Using model: glow_tts
   
   > Model has 28356129 parameters
   
   > EPOCH: 0/1000
   
   > DataLoader initialization
   | > Use phonemes: False
   | > Number of instances : 12969
   | > Max length sequence: 187
   | > Min length sequence: 5
   | > Avg length sequence: 98.3403500655409
   | > Num. instances discarded by max-min (max=500, min=3) seq limits: 0
   | > Batch group size: 0.
   
   > TRAINING (2021-06-23 14:52:54)
   
   --> STEP: 0/405 -- GLOBAL_STEP: 0
       | > loss: 2.34670
       | > log_mle: 1.61872
       | > loss_dur: 0.72798
       | > align_error: 0.52744
       | > current_lr: 2.5e-07
       | > grad_norm: 5.036039352416992
       | > step_time: 5.8815
       | > loader_time: 0.0065
   ...
   

5. Run the Tensorboard.

   $ tensorboard --logdir=<path to your training directory>
   

6. Monitor the training process.

   On the terminal and Tensorboard, you can monitor the progress of your model. Also Tensorboard provides certain figures and sample outputs.

   Note that different models have different metrics, visuals and outputs.

   You should also check the FAQ page for common problems and solutions that occur in a training.

7. Use your best model for inference.

   Use tts or tts-server commands for testing your models.

   $ tts --text "Text for TTS" \
         --model_path path/to/checkpoint_x.pth.tar \
         --config_path path/to/config.json \
         --out_path folder/to/save/output.wav
   

8. Return to the step 1 and reiterate for training a vocoder model.

   In the example above, we trained a GlowTTS model, but the same workflow applies to all the other 🐸TTS models.