def test_init_multispeaker(self):
config = GlowTTSConfig(num_chars=32)
model = GlowTTS(config)
# speaker embedding with default speaker_embedding_dim
config.use_speaker_embedding = True
config.num_speakers = 5
config.d_vector_dim = None
model.init_multispeaker(config)
self.assertEqual(model.c_in_channels, model.hidden_channels_enc)
# use external speaker embeddings with speaker_embedding_dim = 301
config = GlowTTSConfig(num_chars=32)
config.use_d_vector_file = True
config.d_vector_dim = 301
model = GlowTTS(config)
model.init_multispeaker(config)
self.assertEqual(model.c_in_channels, 301)
# use speaker embedddings by the provided speaker_manager
config = GlowTTSConfig(num_chars=32)
config.use_speaker_embedding = True
config.speakers_file = os.path.join(get_tests_data_path(), "ljspeech",
"speakers.json")
speaker_manager = SpeakerManager.init_from_config(config)
model = GlowTTS(config)
model.speaker_manager = speaker_manager
model.init_multispeaker(config)
self.assertEqual(model.c_in_channels, model.hidden_channels_enc)
self.assertEqual(model.num_speakers, speaker_manager.num_speakers)
# use external speaker embeddings by the provided speaker_manager
config = GlowTTSConfig(num_chars=32)
config.use_d_vector_file = True
config.d_vector_dim = 256
config.d_vector_file = os.path.join(get_tests_data_path(),
"dummy_speakers.json")
speaker_manager = SpeakerManager.init_from_config(config)
model = GlowTTS(config)
model.speaker_manager = speaker_manager
model.init_multispeaker(config)
self.assertEqual(model.c_in_channels, speaker_manager.embedding_dim)
self.assertEqual(model.num_speakers, speaker_manager.num_speakers)
# You can define your custom sample loader returning the list of samples.
# Or define your custom formatter and pass it to the `load_tts_samples`.
# Check `TTS.tts.datasets.load_tts_samples` for more details.
train_samples, eval_samples = load_tts_samples(
dataset_config,
eval_split=True,
eval_split_max_size=config.eval_split_max_size,
eval_split_size=config.eval_split_size,
)
# init speaker manager for multi-speaker training
# it maps speaker-id to speaker-name in the model and data-loader
speaker_manager = SpeakerManager()
speaker_manager.set_ids_from_data(train_samples + eval_samples,
parse_key="speaker_name")
config.num_speakers = speaker_manager.num_speakers
# init model
model = GlowTTS(config, ap, tokenizer, speaker_manager=speaker_manager)
# INITIALIZE 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(TrainerArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples)
# AND... 3,2,1... 🚀