import copy
import os
import unittest
import torch
from trainer.logging.tensorboard_logger import TensorboardLogger
from tests import assertHasAttr, assertHasNotAttr, get_tests_data_path, get_tests_input_path, get_tests_output_path
from TTS.config import load_config
from TTS.encoder.utils.generic_utils import setup_encoder_model
from TTS.tts.configs.vits_config import VitsConfig
from TTS.tts.models.vits import Vits, VitsArgs, amp_to_db, db_to_amp, load_audio, spec_to_mel, wav_to_mel, wav_to_spec
from TTS.tts.utils.speakers import SpeakerManager
LANG_FILE = os.path.join(get_tests_input_path(), "language_ids.json")
SPEAKER_ENCODER_CONFIG = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav")
torch.manual_seed(1)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# pylint: disable=no-self-use
class TestVits(unittest.TestCase):
def test_load_audio(self):
wav, sr = load_audio(WAV_FILE)
self.assertEqual(wav.shape, (1, 41885))
self.assertEqual(sr, 22050)
spec = wav_to_spec(wav, n_fft=1024, hop_length=512, win_length=1024, center=False)
mel = wav_to_mel(
wav,
n_fft=1024,
num_mels=80,
sample_rate=sr,
hop_length=512,
win_length=1024,
fmin=0,
fmax=8000,
center=False,
)
mel2 = spec_to_mel(spec, n_fft=1024, num_mels=80, sample_rate=sr, fmin=0, fmax=8000)
self.assertEqual((mel - mel2).abs().max(), 0)
self.assertEqual(spec.shape[0], mel.shape[0])
self.assertEqual(spec.shape[2], mel.shape[2])
spec_db = amp_to_db(spec)
spec_amp = db_to_amp(spec_db)
self.assertAlmostEqual((spec - spec_amp).abs().max(), 0, delta=1e-4)
def test_dataset(self):
"""TODO:"""
...
def test_init_multispeaker(self):
num_speakers = 10
args = VitsArgs(num_speakers=num_speakers, use_speaker_embedding=True)
model = Vits(args)
assertHasAttr(self, model, "emb_g")
args = VitsArgs(num_speakers=0, use_speaker_embedding=True)
model = Vits(args)
assertHasNotAttr(self, model, "emb_g")
args = VitsArgs(num_speakers=10, use_speaker_embedding=False)
model = Vits(args)
assertHasNotAttr(self, model, "emb_g")
args = VitsArgs(d_vector_dim=101, use_d_vector_file=True)
model = Vits(args)
self.assertEqual(model.embedded_speaker_dim, 101)
def test_init_multilingual(self):
args = VitsArgs(language_ids_file=None, use_language_embedding=False)
model = Vits(args)
self.assertEqual(model.language_manager, None)
self.assertEqual(model.embedded_language_dim, 0)
assertHasNotAttr(self, model, "emb_l")
args = VitsArgs(language_ids_file=LANG_FILE)
model = Vits(args)
self.assertNotEqual(model.language_manager, None)
self.assertEqual(model.embedded_language_dim, 0)
assertHasNotAttr(self, model, "emb_l")
args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True)
model = Vits(args)
self.assertNotEqual(model.language_manager, None)
self.assertEqual(model.embedded_language_dim, args.embedded_language_dim)
assertHasAttr(self, model, "emb_l")
args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True, embedded_language_dim=102)
model = Vits(args)
self.assertNotEqual(model.language_manager, None)
self.assertEqual(model.embedded_language_dim, args.embedded_language_dim)
assertHasAttr(self, model, "emb_l")
def test_get_aux_input(self):
aux_input = {"speaker_ids": None, "style_wav": None, "d_vectors": None, "language_ids": None}
args = VitsArgs()
model = Vits(args)
aux_out = model.get_aux_input(aux_input)
speaker_id = torch.randint(10, (1,))
language_id = torch.randint(10, (1,))
d_vector = torch.rand(1, 128)
aux_input = {"speaker_ids": speaker_id, "style_wav": None, "d_vectors": d_vector, "language_ids": language_id}
aux_out = model.get_aux_input(aux_input)
self.assertEqual(aux_out["speaker_ids"].shape, speaker_id.shape)
self.assertEqual(aux_out["language_ids"].shape, language_id.shape)
self.assertEqual(aux_out["d_vectors"].shape, d_vector.unsqueeze(0).transpose(2, 1).shape)
def test_voice_conversion(self):
num_speakers = 10
spec_len = 101
spec_effective_len = 50
args = VitsArgs(num_speakers=num_speakers, use_speaker_embedding=True)
model = Vits(args)
ref_inp = torch.randn(1, spec_len, 513)
ref_inp_len = torch.randint(1, spec_effective_len, (1,))
ref_spk_id = torch.randint(1, num_speakers, (1,))
tgt_spk_id = torch.randint(1, num_speakers, (1,))
o_hat, y_mask, (z, z_p, z_hat) = model.voice_conversion(ref_inp, ref_inp_len, ref_spk_id, tgt_spk_id)
self.assertEqual(o_hat.shape, (1, 1, spec_len * 256))
self.assertEqual(y_mask.shape, (1, 1, spec_len))
self.assertEqual(y_mask.sum(), ref_inp_len[0])
self.assertEqual(z.shape, (1, args.hidden_channels, spec_len))
self.assertEqual(z_p.shape, (1, args.hidden_channels, spec_len))
self.assertEqual(z_hat.shape, (1, args.hidden_channels, spec_len))
def _create_inputs(self, config, batch_size=2):
input_dummy = torch.randint(0, 24, (batch_size, 128)).long().to(device)
input_lengths = torch.randint(100, 129, (batch_size,)).long().to(device)
input_lengths[-1] = 128
spec = torch.rand(batch_size, config.audio["fft_size"] // 2 + 1, 30).to(device)
mel = torch.rand(batch_size, config.audio["num_mels"], 30).to(device)
spec_lengths = torch.randint(20, 30, (batch_size,)).long().to(device)
spec_lengths[-1] = spec.size(2)
waveform = torch.rand(batch_size, 1, spec.size(2) * config.audio["hop_length"]).to(device)
return input_dummy, input_lengths, mel, spec, spec_lengths, waveform
def _check_forward_outputs(self, config, output_dict, encoder_config=None, batch_size=2):
self.assertEqual(
output_dict["model_outputs"].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"]
)
self.assertEqual(output_dict["alignments"].shape, (batch_size, 128, 30))
self.assertEqual(output_dict["alignments"].max(), 1)
self.assertEqual(output_dict["alignments"].min(), 0)
self.assertEqual(output_dict["z"].shape, (batch_size, config.model_args.hidden_channels, 30))
self.assertEqual(output_dict["z_p"].shape, (batch_size, config.model_args.hidden_channels, 30))
self.assertEqual(output_dict["m_p"].shape, (batch_size, config.model_args.hidden_channels, 30))
self.assertEqual(output_dict["logs_p"].shape, (batch_size, config.model_args.hidden_channels, 30))
self.assertEqual(output_dict["m_q"].shape, (batch_size, config.model_args.hidden_channels, 30))
self.assertEqual(output_dict["logs_q"].shape, (batch_size, config.model_args.hidden_channels, 30))
self.assertEqual(
output_dict["waveform_seg"].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"]
)
if encoder_config:
self.assertEqual(output_dict["gt_spk_emb"].shape, (batch_size, encoder_config.model_params["proj_dim"]))
self.assertEqual(output_dict["syn_spk_emb"].shape, (batch_size, encoder_config.model_params["proj_dim"]))
else:
self.assertEqual(output_dict["gt_spk_emb"], None)
self.assertEqual(output_dict["syn_spk_emb"], None)
def test_forward(self):
num_speakers = 0
config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
config.model_args.spec_segment_size = 10
input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config)
model = Vits(config).to(device)
output_dict = model.forward(input_dummy, input_lengths, spec, spec_lengths, waveform)
self._check_forward_outputs(config, output_dict)
def test_multispeaker_forward(self):
num_speakers = 10
config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
config.model_args.spec_segment_size = 10
input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config)
speaker_ids = torch.randint(0, num_speakers, (8,)).long().to(device)
model = Vits(config).to(device)
output_dict = model.forward(
input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"speaker_ids": speaker_ids}
)
self._check_forward_outputs(config, output_dict)
def test_d_vector_forward(self):
batch_size = 2
args = VitsArgs(
spec_segment_size=10,
num_chars=32,
use_d_vector_file=True,
d_vector_dim=256,
d_vector_file=os.path.join(get_tests_data_path(), "dummy_speakers.json"),
)
config = VitsConfig(model_args=args)
model = Vits.init_from_config(config, verbose=False).to(device)
model.train()
input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config, batch_size=batch_size)
d_vectors = torch.randn(batch_size, 256).to(device)
output_dict = model.forward(
input_dummy, input_lengths, spec, spec_lengths, waveform, aux_input={"d_vectors": d_vectors}
)
self._check_forward_outputs(config, output_dict)
def test_multilingual_forward(self):
num_speakers = 10
num_langs = 3
batch_size = 2
args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True, spec_segment_size=10)
config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True, model_args=args)
input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
model = Vits(config).to(device)
output_dict = model.forward(
input_dummy,
input_lengths,
spec,
spec_lengths,
waveform,
aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids},
)
self._check_forward_outputs(config, output_dict)
def test_secl_forward(self):
num_speakers = 10
num_langs = 3
batch_size = 2
speaker_encoder_config = load_config(SPEAKER_ENCODER_CONFIG)
speaker_encoder_config.model_params["use_torch_spec"] = True
speaker_encoder = setup_encoder_model(speaker_encoder_config).to(device)
speaker_manager = SpeakerManager()
speaker_manager.encoder = speaker_encoder
args = VitsArgs(
language_ids_file=LANG_FILE,
use_language_embedding=True,
spec_segment_size=10,
use_speaker_encoder_as_loss=True,
)
config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True, model_args=args)
config.audio.sample_rate = 16000
input_dummy, input_lengths, _, spec, spec_lengths, waveform = self._create_inputs(config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
model = Vits(config, speaker_manager=speaker_manager).to(device)
output_dict = model.forward(
input_dummy,
input_lengths,
spec,
spec_lengths,
waveform,
aux_input={"speaker_ids": speaker_ids, "language_ids": lang_ids},
)
self._check_forward_outputs(config, output_dict, speaker_encoder_config)
def _check_inference_outputs(self, config, outputs, input_dummy, batch_size=1):
feat_len = outputs["z"].shape[2]
self.assertEqual(outputs["model_outputs"].shape[:2], (batch_size, 1)) # we don't know the channel dimension
self.assertEqual(outputs["alignments"].shape, (batch_size, input_dummy.shape[1], feat_len))
self.assertEqual(outputs["z"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
self.assertEqual(outputs["z_p"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
self.assertEqual(outputs["m_p"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
self.assertEqual(outputs["logs_p"].shape, (batch_size, config.model_args.hidden_channels, feat_len))
def test_inference(self):
num_speakers = 0
config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
model = Vits(config).to(device)
batch_size = 1
input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
outputs = model.inference(input_dummy)
self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
batch_size = 2
input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
outputs = model.inference(input_dummy, aux_input={"x_lengths": input_lengths})
self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
def test_multispeaker_inference(self):
num_speakers = 10
config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True)
model = Vits(config).to(device)
batch_size = 1
input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
outputs = model.inference(input_dummy, {"speaker_ids": speaker_ids})
self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
batch_size = 2
input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
outputs = model.inference(input_dummy, {"x_lengths": input_lengths, "speaker_ids": speaker_ids})
self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
def test_multilingual_inference(self):
num_speakers = 10
num_langs = 3
args = VitsArgs(language_ids_file=LANG_FILE, use_language_embedding=True, spec_segment_size=10)
config = VitsConfig(num_speakers=num_speakers, use_speaker_embedding=True, model_args=args)
model = Vits(config).to(device)
input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
speaker_ids = torch.randint(0, num_speakers, (1,)).long().to(device)
lang_ids = torch.randint(0, num_langs, (1,)).long().to(device)
_ = model.inference(input_dummy, {"speaker_ids": speaker_ids, "language_ids": lang_ids})
batch_size = 1
input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
outputs = model.inference(input_dummy, {"speaker_ids": speaker_ids, "language_ids": lang_ids})
self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
batch_size = 2
input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
lang_ids = torch.randint(0, num_langs, (batch_size,)).long().to(device)
outputs = model.inference(
input_dummy, {"x_lengths": input_lengths, "speaker_ids": speaker_ids, "language_ids": lang_ids}
)
self._check_inference_outputs(config, outputs, input_dummy, batch_size=batch_size)
def test_d_vector_inference(self):
args = VitsArgs(
spec_segment_size=10,
num_chars=32,
use_d_vector_file=True,
d_vector_dim=256,
d_vector_file=os.path.join(get_tests_data_path(), "dummy_speakers.json"),
)
config = VitsConfig(model_args=args)
model = Vits.init_from_config(config, verbose=False).to(device)
model.eval()
# batch size = 1
input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
d_vectors = torch.randn(1, 256).to(device)
outputs = model.inference(input_dummy, aux_input={"d_vectors": d_vectors})
self._check_inference_outputs(config, outputs, input_dummy)
# batch size = 2
input_dummy, input_lengths, *_ = self._create_inputs(config)
d_vectors = torch.randn(2, 256).to(device)
outputs = model.inference(input_dummy, aux_input={"x_lengths": input_lengths, "d_vectors": d_vectors})
self._check_inference_outputs(config, outputs, input_dummy, batch_size=2)
@staticmethod
def _check_parameter_changes(model, model_ref):
count = 0
for item1, item2 in zip(model.named_parameters(), model_ref.named_parameters()):
name = item1[0]
param = item1[1]
param_ref = item2[1]
assert (param != param_ref).any(), "param {} with shape {} not updated!! \n{}\n{}".format(
name, param.shape, param, param_ref
)
count = count + 1
def _create_batch(self, config, batch_size):
input_dummy, input_lengths, mel, spec, mel_lengths, _ = self._create_inputs(config, batch_size)
batch = {}
batch["tokens"] = input_dummy
batch["token_lens"] = input_lengths
batch["spec_lens"] = mel_lengths
batch["mel_lens"] = mel_lengths
batch["spec"] = spec
batch["mel"] = mel
batch["waveform"] = torch.rand(batch_size, 1, config.audio["sample_rate"] * 10).to(device)
batch["d_vectors"] = None
batch["speaker_ids"] = None
batch["language_ids"] = None
return batch
def test_train_step(self):
# setup the model
with torch.autograd.set_detect_anomaly(True):
config = VitsConfig(model_args=VitsArgs(num_chars=32, spec_segment_size=10))
model = Vits(config).to(device)
model.train()
# model to train
optimizers = model.get_optimizer()
criterions = model.get_criterion()
criterions = [criterions[0].to(device), criterions[1].to(device)]
# reference model to compare model weights
model_ref = Vits(config).to(device)
# # pass the state to ref model
model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
count = 0
for param, param_ref in zip(model.parameters(), model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count = count + 1
for _ in range(5):
batch = self._create_batch(config, 2)
for idx in [0, 1]:
outputs, loss_dict = model.train_step(batch, criterions, idx)
self.assertFalse(not outputs)
self.assertFalse(not loss_dict)
loss_dict["loss"].backward()
optimizers[idx].step()
optimizers[idx].zero_grad()
# check parameter changes
self._check_parameter_changes(model, model_ref)
def test_train_step_upsampling(self):
# setup the model
with torch.autograd.set_detect_anomaly(True):
model_args = VitsArgs(
num_chars=32,
spec_segment_size=10,
encoder_sample_rate=11025,
interpolate_z=False,
upsample_rates_decoder=[8, 8, 4, 2],
)
config = VitsConfig(model_args=model_args)
model = Vits(config).to(device)
model.train()
# model to train
optimizers = model.get_optimizer()
criterions = model.get_criterion()
criterions = [criterions[0].to(device), criterions[1].to(device)]
# reference model to compare model weights
model_ref = Vits(config).to(device)
# # pass the state to ref model
model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
count = 0
for param, param_ref in zip(model.parameters(), model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count = count + 1
for _ in range(5):
batch = self._create_batch(config, 2)
for idx in [0, 1]:
outputs, loss_dict = model.train_step(batch, criterions, idx)
self.assertFalse(not outputs)
self.assertFalse(not loss_dict)
loss_dict["loss"].backward()
optimizers[idx].step()
optimizers[idx].zero_grad()
# check parameter changes
self._check_parameter_changes(model, model_ref)
def test_train_step_upsampling_interpolation(self):
# setup the model
with torch.autograd.set_detect_anomaly(True):
model_args = VitsArgs(num_chars=32, spec_segment_size=10, encoder_sample_rate=11025, interpolate_z=True)
config = VitsConfig(model_args=model_args)
model = Vits(config).to(device)
model.train()
# model to train
optimizers = model.get_optimizer()
criterions = model.get_criterion()
criterions = [criterions[0].to(device), criterions[1].to(device)]
# reference model to compare model weights
model_ref = Vits(config).to(device)
# # pass the state to ref model
model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
count = 0
for param, param_ref in zip(model.parameters(), model_ref.parameters()):
assert (param - param_ref).sum() == 0, param
count = count + 1
for _ in range(5):
batch = self._create_batch(config, 2)
for idx in [0, 1]:
outputs, loss_dict = model.train_step(batch, criterions, idx)
self.assertFalse(not outputs)
self.assertFalse(not loss_dict)
loss_dict["loss"].backward()
optimizers[idx].step()
optimizers[idx].zero_grad()
# check parameter changes
self._check_parameter_changes(model, model_ref)
def test_train_eval_log(self):
batch_size = 2
config = VitsConfig(model_args=VitsArgs(num_chars=32, spec_segment_size=10))
model = Vits.init_from_config(config, verbose=False).to(device)
model.run_data_dep_init = False
model.train()
batch = self._create_batch(config, batch_size)
logger = TensorboardLogger(
log_dir=os.path.join(get_tests_output_path(), "dummy_vits_logs"), model_name="vits_test_train_log"
)
criterion = model.get_criterion()
criterion = [criterion[0].to(device), criterion[1].to(device)]
outputs = [None] * 2
outputs[0], _ = model.train_step(batch, criterion, 0)
outputs[1], _ = model.train_step(batch, criterion, 1)
model.train_log(batch, outputs, logger, None, 1)
model.eval_log(batch, outputs, logger, None, 1)
logger.finish()
def test_test_run(self):
config = VitsConfig(model_args=VitsArgs(num_chars=32))
model = Vits.init_from_config(config, verbose=False).to(device)
model.run_data_dep_init = False
model.eval()
test_figures, test_audios = model.test_run(None)
self.assertTrue(test_figures is not None)
self.assertTrue(test_audios is not None)
def test_load_checkpoint(self):
chkp_path = os.path.join(get_tests_output_path(), "dummy_glow_tts_checkpoint.pth")
config = VitsConfig(VitsArgs(num_chars=32))
model = Vits.init_from_config(config, verbose=False).to(device)
chkp = {}
chkp["model"] = model.state_dict()
torch.save(chkp, chkp_path)
model.load_checkpoint(config, chkp_path)
self.assertTrue(model.training)
model.load_checkpoint(config, chkp_path, eval=True)
self.assertFalse(model.training)
def test_get_criterion(self):
config = VitsConfig(VitsArgs(num_chars=32))
model = Vits.init_from_config(config, verbose=False).to(device)
criterion = model.get_criterion()
self.assertTrue(criterion is not None)
def test_init_from_config(self):
config = VitsConfig(model_args=VitsArgs(num_chars=32))
model = Vits.init_from_config(config, verbose=False).to(device)
config = VitsConfig(model_args=VitsArgs(num_chars=32, num_speakers=2))
model = Vits.init_from_config(config, verbose=False).to(device)
self.assertTrue(not hasattr(model, "emb_g"))
config = VitsConfig(model_args=VitsArgs(num_chars=32, num_speakers=2, use_speaker_embedding=True))
model = Vits.init_from_config(config, verbose=False).to(device)
self.assertEqual(model.num_speakers, 2)
self.assertTrue(hasattr(model, "emb_g"))
config = VitsConfig(
model_args=VitsArgs(
num_chars=32,
num_speakers=2,
use_speaker_embedding=True,
speakers_file=os.path.join(get_tests_data_path(), "ljspeech", "speakers.json"),
)
)
model = Vits.init_from_config(config, verbose=False).to(device)
self.assertEqual(model.num_speakers, 10)
self.assertTrue(hasattr(model, "emb_g"))
config = VitsConfig(
model_args=VitsArgs(
num_chars=32,
use_d_vector_file=True,
d_vector_dim=256,
d_vector_file=os.path.join(get_tests_data_path(), "dummy_speakers.json"),
)
)
model = Vits.init_from_config(config, verbose=False).to(device)
self.assertTrue(model.num_speakers == 1)
self.assertTrue(not hasattr(model, "emb_g"))
self.assertTrue(model.embedded_speaker_dim == config.d_vector_dim)