mirror of https://github.com/coqui-ai/TTS.git
Move upsampling tests to test_vits.py
This commit is contained in:
Edresson Casanova
parent
f4e53295b1
commit
af98ec8eb9
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@ -420,6 +420,76 @@ class TestVits(unittest.TestCase):
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# check parameter changes
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# check parameter changes
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self._check_parameter_changes(model, model_ref)
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self._check_parameter_changes(model, model_ref)
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def test_train_step_upsampling(self):
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# setup the model
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with torch.autograd.set_detect_anomaly(True):
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model_args = VitsArgs(
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num_chars=32,
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spec_segment_size=10,
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encoder_sample_rate=11025,
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interpolate_z=False,
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upsample_rates_decoder=[8, 8, 4, 2],
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)
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config = VitsConfig(model_args=model_args)
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model = Vits(config).to(device)
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model.train()
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# model to train
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optimizers = model.get_optimizer()
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criterions = model.get_criterion()
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criterions = [criterions[0].to(device), criterions[1].to(device)]
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# reference model to compare model weights
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model_ref = Vits(config).to(device)
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# # pass the state to ref model
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model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
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count = 0
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for param, param_ref in zip(model.parameters(), model_ref.parameters()):
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assert (param - param_ref).sum() == 0, param
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count = count + 1
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for _ in range(5):
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batch = self._create_batch(config, 2)
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for idx in [0, 1]:
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outputs, loss_dict = model.train_step(batch, criterions, idx)
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self.assertFalse(not outputs)
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self.assertFalse(not loss_dict)
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loss_dict["loss"].backward()
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optimizers[idx].step()
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optimizers[idx].zero_grad()
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# check parameter changes
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self._check_parameter_changes(model, model_ref)
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def test_train_step_upsampling_interpolation(self):
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# setup the model
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with torch.autograd.set_detect_anomaly(True):
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model_args = VitsArgs(num_chars=32, spec_segment_size=10, encoder_sample_rate=11025, interpolate_z=True)
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config = VitsConfig(model_args=model_args)
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model = Vits(config).to(device)
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model.train()
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# model to train
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optimizers = model.get_optimizer()
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criterions = model.get_criterion()
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criterions = [criterions[0].to(device), criterions[1].to(device)]
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# reference model to compare model weights
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model_ref = Vits(config).to(device)
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# # pass the state to ref model
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model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
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count = 0
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for param, param_ref in zip(model.parameters(), model_ref.parameters()):
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assert (param - param_ref).sum() == 0, param
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count = count + 1
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for _ in range(5):
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batch = self._create_batch(config, 2)
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for idx in [0, 1]:
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outputs, loss_dict = model.train_step(batch, criterions, idx)
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self.assertFalse(not outputs)
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self.assertFalse(not loss_dict)
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loss_dict["loss"].backward()
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optimizers[idx].step()
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optimizers[idx].zero_grad()
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# check parameter changes
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self._check_parameter_changes(model, model_ref)
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def test_train_eval_log(self):
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def test_train_eval_log(self):
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batch_size = 2
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batch_size = 2
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config = VitsConfig(model_args=VitsArgs(num_chars=32, spec_segment_size=10))
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config = VitsConfig(model_args=VitsArgs(num_chars=32, spec_segment_size=10))
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@ -1,90 +0,0 @@
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import glob
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import json
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import os
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import shutil
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from trainer import get_last_checkpoint
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from tests import get_device_id, get_tests_output_path, run_cli
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from TTS.tts.configs.vits_config import VitsConfig
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config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
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output_path = os.path.join(get_tests_output_path(), "train_outputs")
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config = VitsConfig(
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batch_size=2,
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eval_batch_size=2,
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num_loader_workers=0,
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num_eval_loader_workers=0,
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text_cleaner="english_cleaners",
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use_phonemes=True,
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phoneme_language="en-us",
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phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
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run_eval=True,
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test_delay_epochs=-1,
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epochs=1,
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print_step=1,
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print_eval=True,
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test_sentences=[
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["Be a voice, not an echo.", "ljspeech-1"],
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],
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)
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# set audio config
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config.audio.do_trim_silence = True
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config.audio.trim_db = 60
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# active multispeaker d-vec mode
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config.model_args.use_speaker_embedding = True
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config.model_args.use_d_vector_file = False
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config.model_args.d_vector_file = None
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config.model_args.d_vector_dim = 256
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# test upsample interpolation approach
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config.model_args.encoder_sample_rate = 11025
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config.model_args.interpolate_z = True
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config.model_args.upsample_rates_decoder = [8, 8, 2, 2]
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config.model_args.periods_multi_period_discriminator = [2, 3, 5, 7]
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config.save_json(config_path)
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# train the model for one epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
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f"--coqpit.output_path {output_path} "
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"--coqpit.datasets.0.name ljspeech_test "
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"--coqpit.datasets.0.meta_file_train metadata.csv "
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"--coqpit.datasets.0.meta_file_val metadata.csv "
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"--coqpit.datasets.0.path tests/data/ljspeech "
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"--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
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"--coqpit.test_delay_epochs 0"
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)
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run_cli(command_train)
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# Find latest folder
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continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
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# Inference using TTS API
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continue_config_path = os.path.join(continue_path, "config.json")
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continue_restore_path, _ = get_last_checkpoint(continue_path)
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out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
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speaker_id = "ljspeech-1"
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continue_speakers_path = os.path.join(continue_path, "speakers.json")
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# Check integrity of the config
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with open(continue_config_path, "r", encoding="utf-8") as f:
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config_loaded = json.load(f)
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assert config_loaded["characters"] is not None
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assert config_loaded["output_path"] in continue_path
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assert config_loaded["test_delay_epochs"] == 0
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# Load the model and run inference
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inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
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run_cli(inference_command)
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# restore the model and continue training for one more epoch
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command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
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run_cli(command_train)
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shutil.rmtree(continue_path)
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@ -1,90 +0,0 @@
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import glob
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import json
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import os
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import shutil
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from trainer import get_last_checkpoint
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from tests import get_device_id, get_tests_output_path, run_cli
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from TTS.tts.configs.vits_config import VitsConfig
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config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
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output_path = os.path.join(get_tests_output_path(), "train_outputs")
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config = VitsConfig(
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batch_size=2,
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eval_batch_size=2,
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num_loader_workers=0,
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num_eval_loader_workers=0,
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text_cleaner="english_cleaners",
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use_phonemes=True,
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phoneme_language="en-us",
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phoneme_cache_path="tests/data/ljspeech/phoneme_cache/",
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run_eval=True,
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test_delay_epochs=-1,
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epochs=1,
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print_step=1,
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print_eval=True,
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test_sentences=[
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["Be a voice, not an echo.", "ljspeech-1"],
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],
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)
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# set audio config
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config.audio.do_trim_silence = True
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config.audio.trim_db = 60
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# active multispeaker d-vec mode
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config.model_args.use_speaker_embedding = True
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config.model_args.use_d_vector_file = False
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config.model_args.d_vector_file = None
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config.model_args.d_vector_dim = 256
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# test upsample
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config.model_args.encoder_sample_rate = 11025
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config.model_args.interpolate_z = False
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config.model_args.upsample_rates_decoder = [8, 8, 4, 2]
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config.model_args.periods_multi_period_discriminator = [2, 3, 5, 7]
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config.save_json(config_path)
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# train the model for one epoch
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command_train = (
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f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
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f"--coqpit.output_path {output_path} "
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"--coqpit.datasets.0.name ljspeech_test "
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"--coqpit.datasets.0.meta_file_train metadata.csv "
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"--coqpit.datasets.0.meta_file_val metadata.csv "
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"--coqpit.datasets.0.path tests/data/ljspeech "
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"--coqpit.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt "
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"--coqpit.test_delay_epochs 0"
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)
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run_cli(command_train)
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# Find latest folder
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continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime)
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# Inference using TTS API
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continue_config_path = os.path.join(continue_path, "config.json")
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continue_restore_path, _ = get_last_checkpoint(continue_path)
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out_wav_path = os.path.join(get_tests_output_path(), "output.wav")
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speaker_id = "ljspeech-1"
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continue_speakers_path = os.path.join(continue_path, "speakers.json")
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# Check integrity of the config
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with open(continue_config_path, "r", encoding="utf-8") as f:
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config_loaded = json.load(f)
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assert config_loaded["characters"] is not None
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assert config_loaded["output_path"] in continue_path
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assert config_loaded["test_delay_epochs"] == 0
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# Load the model and run inference
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inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --speaker_idx {speaker_id} --speakers_file_path {continue_speakers_path} --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}"
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run_cli(inference_command)
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# restore the model and continue training for one more epoch
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command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} "
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run_cli(command_train)
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shutil.rmtree(continue_path)
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