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, VitsAudioConfig, 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, 513, spec_len) ref_inp_len = torch.randint(1, spec_effective_len, (1,)) ref_spk_id = torch.randint(1, num_speakers, (1,)).item() tgt_spk_id = torch.randint(1, num_speakers, (1,)).item() 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): """Upsampling by the decoder upsampling layers""" # setup the model with torch.autograd.set_detect_anomaly(True): audio_config = VitsAudioConfig(sample_rate=22050) 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, audio=audio_config) 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): """Upsampling by interpolation""" # setup the model with torch.autograd.set_detect_anomaly(True): audio_config = VitsAudioConfig(sample_rate=22050) model_args = VitsArgs( num_chars=32, spec_segment_size=10, encoder_sample_rate=11025, interpolate_z=True, upsample_rates_decoder=[8, 8, 2, 2], ) config = VitsConfig(model_args=model_args, audio=audio_config) 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)