mirror of https://github.com/coqui-ai/TTS.git
Update ForwardTTSe2e tests
This commit is contained in:
Eren Gölge
Eren G??lge
parent
0b585b46c1
commit
edd59c81e8
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@ -6,8 +6,8 @@ import torch
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from trainer.logging.tensorboard_logger import TensorboardLogger
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from tests import assertHasAttr, assertHasNotAttr, get_tests_data_path, get_tests_input_path, get_tests_output_path
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from TTS.tts.configs.fast_pitch_e2e_config import FastPitchE2EConfig
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from TTS.tts.models.forward_tts_e2e import ForwardTTSE2E, ForwardTTSE2EArgs
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from TTS.tts.configs.fast_pitch_e2e_config import FastPitchE2eConfig
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from TTS.tts.models.forward_tts_e2e import ForwardTTSE2e, ForwardTTSE2eArgs
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LANG_FILE = os.path.join(get_tests_input_path(), "language_ids.json")
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SPEAKER_ENCODER_CONFIG = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
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@ -18,17 +18,20 @@ torch.manual_seed(1)
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use_cuda = torch.cuda.is_available()
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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MAX_INPUT_LEN = 57
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MAX_SPEC_LEN = 33
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# pylint: disable=no-self-use
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class TestFastPitchE2E(unittest.TestCase):
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def _create_inputs(self, config, batch_size=2):
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input_dummy = torch.randint(0, 24, (batch_size, 128)).long().to(device)
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input_lengths = torch.randint(100, 129, (batch_size,)).long().to(device)
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input_lengths[-1] = 128
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spec = torch.rand(batch_size, 30, config.audio["num_mels"]).to(device)
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# spec = torch.rand(batch_size, config.audio["num_mels"], 30).to(device)
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spec_lengths = torch.randint(20, 30, (batch_size,)).long().to(device)
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spec_lengths[-1] = spec.size(1)
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input_dummy = torch.randint(0, 24, (batch_size, MAX_INPUT_LEN)).long().to(device)
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input_lengths = torch.randint(10, MAX_INPUT_LEN, (batch_size,)).long().to(device)
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input_lengths[-1] = MAX_INPUT_LEN
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spec = torch.rand(batch_size, MAX_SPEC_LEN, config.audio["num_mels"]).to(device)
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spec_lengths = torch.randint(20, MAX_SPEC_LEN, (batch_size,)).long().to(device)
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spec_lengths[-1] = MAX_SPEC_LEN
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waveform = torch.rand(batch_size, 1, spec.size(1) * config.audio["hop_length"]).to(device)
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pitch = torch.rand(batch_size, 1, spec.size(1)).to(device)
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return input_dummy, input_lengths, spec, spec_lengths, waveform, pitch
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@ -37,7 +40,7 @@ class TestFastPitchE2E(unittest.TestCase):
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self.assertEqual(
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output_dict["model_outputs"].shape[2], config.model_args.spec_segment_size * config.audio["hop_length"]
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)
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self.assertEqual(output_dict["alignments"].shape, (batch_size, 30, 128))
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self.assertEqual(output_dict["alignments"].shape, (batch_size, MAX_SPEC_LEN, MAX_INPUT_LEN))
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self.assertEqual(output_dict["alignments"].max(), 1)
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self.assertEqual(output_dict["alignments"].min(), 0)
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self.assertEqual(
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@ -45,7 +48,9 @@ class TestFastPitchE2E(unittest.TestCase):
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)
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def _check_inference_outputs(self, outputs, input_dummy, batch_size=1):
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feat_len = outputs["encoder_outputs"].shape[1]
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feat_dim = 256 # hard-coded based on model architecture
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feat_len = outputs["o_en_ex"].shape[2]
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self.assertEqual(outputs["o_en_ex"].shape, (batch_size, feat_dim, feat_len))
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self.assertEqual(outputs["model_outputs"].shape[:2], (batch_size, 1)) # we don't know the channel dimension
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self.assertEqual(outputs["alignments"].shape, (batch_size, input_dummy.shape[1], feat_len))
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@ -68,194 +73,188 @@ class TestFastPitchE2E(unittest.TestCase):
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batch["text_lengths"] = input_lengths
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batch["mel_lengths"] = spec_lengths
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batch["mel_input"] = spec
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batch["waveform"] = waveform.transpose(1, 2) # B x C X T -> B x T x C
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batch["waveform"] = waveform # B x C X T
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batch["d_vectors"] = None
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batch["speaker_ids"] = None
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batch["language_ids"] = None
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batch["pitch"] = pitch
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return batch
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# def test_init_multispeaker(self):
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def test_init_multispeaker(self):
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# num_speakers = 10
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# model_args = ForwardTTSE2EArgs()
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# model_args.num_speakers = num_speakers
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# model_args.use_speaker_embedding = True
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# model = ForwardTTSE2E(model_args)
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# assertHasAttr(self, model.encoder_model, "emb_g")
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num_speakers = 10
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model_args = ForwardTTSE2eArgs()
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model_args.num_speakers = num_speakers
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model_args.use_speaker_embedding = True
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model = ForwardTTSE2e(model_args)
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assertHasAttr(self, model.encoder_model, "emb_g")
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# model_args = ForwardTTSE2EArgs()
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# model_args.num_speakers = 0
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# model_args.use_speaker_embedding = True
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# model = ForwardTTSE2E(model_args)
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# assertHasNotAttr(self, model.encoder_model, "emb_g")
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model_args = ForwardTTSE2eArgs()
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model_args.num_speakers = 0
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model_args.use_speaker_embedding = True
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model = ForwardTTSE2e(model_args)
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assertHasNotAttr(self, model.encoder_model, "emb_g")
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# model_args = ForwardTTSE2EArgs()
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# model_args.num_speakers = 10
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# model_args.use_speaker_embedding = False
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# model = ForwardTTSE2E(model_args)
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# assertHasNotAttr(self, model.encoder_model, "emb_g")
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model_args = ForwardTTSE2eArgs()
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model_args.num_speakers = 10
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model_args.use_speaker_embedding = False
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model = ForwardTTSE2e(model_args)
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assertHasNotAttr(self, model.encoder_model, "emb_g")
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# model_args = ForwardTTSE2EArgs(d_vector_dim=101, use_d_vector_file=True)
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# model = ForwardTTSE2E(model_args)
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# self.assertEqual(model.encoder_model.embedded_speaker_dim, 101)
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model_args = ForwardTTSE2eArgs(d_vector_dim=101, use_d_vector_file=True)
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model = ForwardTTSE2e(model_args)
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self.assertEqual(model.encoder_model.embedded_speaker_dim, 101)
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# def test_init_multilingual(self):
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def test_init_multilingual(self):
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"""TODO"""
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def test_get_aux_input(self):
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aux_input = {"speaker_ids": None, "style_wav": None, "d_vectors": None, "language_ids": None}
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model_args = ForwardTTSE2eArgs()
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model = ForwardTTSE2e(model_args)
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aux_out = model.get_aux_input(aux_input)
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speaker_id = torch.randint(10, (1,))
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language_id = torch.randint(10, (1,))
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d_vector = torch.rand(1, 128)
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aux_input = {"speaker_ids": speaker_id, "style_wav": None, "d_vectors": d_vector, "language_ids": language_id}
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aux_out = model.get_aux_input(aux_input)
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self.assertEqual(aux_out["speaker_ids"].shape, speaker_id.shape)
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self.assertEqual(aux_out["language_ids"].shape, language_id.shape)
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self.assertEqual(aux_out["d_vectors"].shape, d_vector.unsqueeze(0).transpose(2, 1).shape)
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def test_forward(self):
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model_args = ForwardTTSE2eArgs(spec_segment_size=10)
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config = FastPitchE2eConfig(model_args=model_args)
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input_dummy, input_lengths, spec, spec_lengths, waveform, pitch = self._create_inputs(config)
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model = ForwardTTSE2e(config).to(device)
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output_dict = model.forward(
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x=input_dummy, x_lengths=input_lengths, spec=spec, spec_lengths=spec_lengths, waveform=waveform, pitch=pitch
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)
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self._check_forward_outputs(config, output_dict)
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def test_multispeaker_forward(self):
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batch_size = 2
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num_speakers = 10
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model_args = ForwardTTSE2eArgs(spec_segment_size=10, num_speakers=num_speakers, use_speaker_embedding=True)
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config = FastPitchE2eConfig(model_args=model_args)
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config.model_args.spec_segment_size = 10
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input_dummy, input_lengths, spec, spec_lengths, waveform, pitch = self._create_inputs(
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config, batch_size=batch_size
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)
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speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
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model = ForwardTTSE2e(config).to(device)
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output_dict = model.forward(
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x=input_dummy,
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x_lengths=input_lengths,
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spec=spec,
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spec_lengths=spec_lengths,
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waveform=waveform,
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pitch=pitch,
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aux_input={"speaker_ids": speaker_ids},
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)
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self._check_forward_outputs(config, output_dict)
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def test_d_vector_forward(self):
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batch_size = 2
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model_args = ForwardTTSE2eArgs(spec_segment_size=10, use_d_vector_file=True, d_vector_dim=256)
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config = FastPitchE2eConfig(model_args=model_args)
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config.model_args.spec_segment_size = 10
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model = ForwardTTSE2e(config).to(device)
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model.train()
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input_dummy, input_lengths, spec, spec_lengths, waveform, pitch = self._create_inputs(
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config, batch_size=batch_size
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)
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d_vectors = torch.randn(batch_size, 256).to(device)
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output_dict = model.forward(
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x=input_dummy,
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x_lengths=input_lengths,
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spec=spec,
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spec_lengths=spec_lengths,
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waveform=waveform,
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pitch=pitch,
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aux_input={"d_vectors": d_vectors},
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)
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self._check_forward_outputs(config, output_dict)
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# def test_multilingual_forward(self):
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# """TODO"""
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# def test_get_aux_input(self):
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# aux_input = {"speaker_ids": None, "style_wav": None, "d_vectors": None, "language_ids": None}
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# model_args = ForwardTTSE2EArgs()
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# model = ForwardTTSE2E(model_args)
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# aux_out = model.get_aux_input(aux_input)
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def test_inference(self):
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model_args = ForwardTTSE2eArgs(spec_segment_size=10)
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config = FastPitchE2eConfig(model_args=model_args)
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model = ForwardTTSE2e(config).to(device)
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model.eval()
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# speaker_id = torch.randint(10, (1,))
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# language_id = torch.randint(10, (1,))
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# d_vector = torch.rand(1, 128)
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# aux_input = {"speaker_ids": speaker_id, "style_wav": None, "d_vectors": d_vector, "language_ids": language_id}
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# aux_out = model.get_aux_input(aux_input)
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# self.assertEqual(aux_out["speaker_ids"].shape, speaker_id.shape)
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# self.assertEqual(aux_out["language_ids"].shape, language_id.shape)
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# self.assertEqual(aux_out["d_vectors"].shape, d_vector.unsqueeze(0).transpose(2, 1).shape)
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batch_size = 1
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input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
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outputs = model.inference(input_dummy.to(device))
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self._check_inference_outputs(outputs, input_dummy, batch_size=batch_size)
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# def test_forward(self):
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# model_args = ForwardTTSE2EArgs(spec_segment_size=10)
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# config = FastPitchE2EConfig(model_args=model_args)
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# input_dummy, input_lengths, spec, spec_lengths, waveform, pitch = self._create_inputs(config)
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# model = ForwardTTSE2E(config).to(device)
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# output_dict = model.forward(
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# x=input_dummy, x_lengths=input_lengths, spec=spec, spec_lengths=spec_lengths, waveform=waveform, pitch=pitch
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# )
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# self._check_forward_outputs(config, output_dict)
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# TODO implemented batched inferenece
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# batch_size = 2
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# input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
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# outputs = model.inference(input_dummy, aux_input={"x_lengths": input_lengths})
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# self._check_inference_outputs(outputs, input_dummy, batch_size=batch_size)
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# def test_multispeaker_forward(self):
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# batch_size = 2
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# num_speakers = 10
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# model_args = ForwardTTSE2EArgs(
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# spec_segment_size=10, num_speakers=num_speakers, use_speaker_embedding=True
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# )
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# config = FastPitchE2EConfig(model_args=model_args)
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# config.model_args.spec_segment_size = 10
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def test_multispeaker_inference(self):
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num_speakers = 10
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model_args = ForwardTTSE2eArgs(spec_segment_size=10, num_speakers=num_speakers, use_speaker_embedding=True)
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config = FastPitchE2eConfig(model_args=model_args)
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model = ForwardTTSE2e(config).to(device)
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# input_dummy, input_lengths, spec, spec_lengths, waveform, pitch = self._create_inputs(
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# config, batch_size=batch_size
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# )
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# speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
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batch_size = 1
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input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
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speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
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outputs = model.inference(input_dummy, {"speaker_ids": speaker_ids})
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self._check_inference_outputs(outputs, input_dummy, batch_size=batch_size)
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# model = ForwardTTSE2E(config).to(device)
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# output_dict = model.forward(
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# x=input_dummy,
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# x_lengths=input_lengths,
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# spec=spec,
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# spec_lengths=spec_lengths,
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# waveform=waveform,
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# pitch=pitch,
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# aux_input={"speaker_ids": speaker_ids},
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# )
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# self._check_forward_outputs(config, output_dict)
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# batch_size = 2
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# input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
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# speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
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# outputs = model.inference(input_dummy, {"x_lengths": input_lengths, "speaker_ids": speaker_ids})
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# self._check_inference_outputs(outputs, input_dummy, batch_size=batch_size)
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# def test_d_vector_forward(self):
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# batch_size = 2
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# model_args = ForwardTTSE2EArgs(
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# spec_segment_size=10, use_d_vector_file=True, d_vector_dim=256
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# )
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# config = FastPitchE2EConfig(model_args=model_args)
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# config.model_args.spec_segment_size = 10
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# model = ForwardTTSE2E(config).to(device)
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# model.train()
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# input_dummy, input_lengths, spec, spec_lengths, waveform, pitch = self._create_inputs(
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# config, batch_size=batch_size
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# )
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# d_vectors = torch.randn(batch_size, 256).to(device)
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# output_dict = model.forward(
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# x=input_dummy,
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# x_lengths=input_lengths,
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# spec=spec,
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# spec_lengths=spec_lengths,
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# waveform=waveform,
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# pitch=pitch,
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# aux_input={"d_vectors": d_vectors},
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# )
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# self._check_forward_outputs(config, output_dict)
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# def test_multilingual_inference(self):
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# """TODO"""
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# # def test_multilingual_forward(self):
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# # """TODO"""
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# def test_inference(self):
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# model_args = ForwardTTSE2EArgs(spec_segment_size=10)
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# config = FastPitchE2EConfig(model_args=model_args)
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# model = ForwardTTSE2E(config).to(device)
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# model.eval()
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# batch_size = 1
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# input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
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# outputs = model.inference(input_dummy.to(device))
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# self._check_inference_outputs(outputs, input_dummy, batch_size=batch_size)
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# # TODO implemented batched inferenece
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# # batch_size = 2
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# # input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
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# # outputs = model.inference(input_dummy, aux_input={"x_lengths": input_lengths})
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# # self._check_inference_outputs(outputs, input_dummy, batch_size=batch_size)
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# def test_multispeaker_inference(self):
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# num_speakers = 10
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# model_args = ForwardTTSE2EArgs(
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# spec_segment_size=10, num_speakers=num_speakers, use_speaker_embedding=True
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# )
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# config = FastPitchE2EConfig(model_args=model_args)
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# model = ForwardTTSE2E(config).to(device)
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# batch_size = 1
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# input_dummy, *_ = self._create_inputs(config, batch_size=batch_size)
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# speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
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# outputs = model.inference(input_dummy, {"speaker_ids": speaker_ids})
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# self._check_inference_outputs(outputs, input_dummy, batch_size=batch_size)
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# # batch_size = 2
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# # input_dummy, input_lengths, *_ = self._create_inputs(config, batch_size=batch_size)
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# # speaker_ids = torch.randint(0, num_speakers, (batch_size,)).long().to(device)
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# # outputs = model.inference(input_dummy, {"x_lengths": input_lengths, "speaker_ids": speaker_ids})
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# # self._check_inference_outputs(outputs, input_dummy, batch_size=batch_size)
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# # def test_multilingual_inference(self):
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# # """TODO"""
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# def test_d_vector_inference(self):
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# model_args = ForwardTTSE2EArgs(
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# spec_segment_size=10,
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# num_chars=32,
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# use_d_vector_file=True,
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# d_vector_dim=256,
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# d_vector_file=os.path.join(get_tests_data_path(), "dummy_speakers.json"),
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# )
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# config = FastPitchE2EConfig(model_args=model_args)
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# model = ForwardTTSE2E(config).to(device)
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# model.eval()
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# # batch size = 1
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# input_dummy = torch.randint(0, 24, (1, 128)).long().to(device)
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# d_vectors = torch.randn(1, 256).to(device)
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# outputs = model.inference(input_dummy, aux_input={"d_vectors": d_vectors})
|
||||
# self._check_inference_outputs(outputs, input_dummy)
|
||||
# # batch size = 2
|
||||
# # input_dummy, input_lengths, *_ = self._create_inputs(config)
|
||||
# # d_vectors = torch.randn(2, 256).to(device)
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# # outputs = model.inference(input_dummy, aux_input={"x_lengths": input_lengths, "d_vectors": d_vectors})
|
||||
# # self._check_inference_outputs(outputs, input_dummy, batch_size=2)
|
||||
def test_d_vector_inference(self):
|
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model_args = ForwardTTSE2eArgs(
|
||||
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 = FastPitchE2eConfig(model_args=model_args)
|
||||
model = ForwardTTSE2e(config).to(device)
|
||||
model.eval()
|
||||
# batch size = 1
|
||||
input_dummy, *_ = self._create_inputs(config, batch_size=1)
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||||
d_vectors = torch.randn(1, 256).to(device)
|
||||
outputs = model.inference(input_dummy, aux_input={"d_vectors": d_vectors})
|
||||
self._check_inference_outputs(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(outputs, input_dummy, batch_size=2)
|
||||
|
||||
def test_train_step(self):
|
||||
# setup the model
|
||||
with torch.autograd.set_detect_anomaly(True):
|
||||
model_args = ForwardTTSE2EArgs(spec_segment_size=10)
|
||||
config = FastPitchE2EConfig(model_args=model_args)
|
||||
model = ForwardTTSE2E(config).to(device)
|
||||
model_args = ForwardTTSE2eArgs(spec_segment_size=10)
|
||||
config = FastPitchE2eConfig(model_args=model_args)
|
||||
model = ForwardTTSE2e(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 = ForwardTTSE2E(config).to(device)
|
||||
model_ref = ForwardTTSE2e(config).to(device)
|
||||
# # pass the state to ref model
|
||||
model_ref.load_state_dict(copy.deepcopy(model.state_dict()))
|
||||
count = 0
|
||||
|
|
@ -277,10 +276,11 @@ class TestFastPitchE2E(unittest.TestCase):
|
|||
|
||||
def test_train_eval_log(self):
|
||||
batch_size = 2
|
||||
model_args = ForwardTTSE2EArgs(spec_segment_size=10)
|
||||
config = FastPitchE2EConfig(model_args=model_args)
|
||||
model = ForwardTTSE2E.init_from_config(config, verbose=False).to(device)
|
||||
model_args = ForwardTTSE2eArgs(spec_segment_size=10)
|
||||
config = FastPitchE2eConfig(model_args=model_args)
|
||||
model = ForwardTTSE2e.init_from_config(config, verbose=False).to(device)
|
||||
model.train()
|
||||
model.on_init_start(trainer=None) # create mel_basis
|
||||
batch = self._create_batch(config, batch_size)
|
||||
logger = TensorboardLogger(
|
||||
log_dir=os.path.join(get_tests_output_path(), "dummy_fast_pitch_e2e_logs"),
|
||||
|
|
@ -296,19 +296,20 @@ class TestFastPitchE2E(unittest.TestCase):
|
|||
logger.finish()
|
||||
|
||||
def test_test_run(self):
|
||||
model_args = ForwardTTSE2EArgs(spec_segment_size=10)
|
||||
config = FastPitchE2EConfig(model_args=model_args)
|
||||
model = ForwardTTSE2E.init_from_config(config, verbose=False).to(device)
|
||||
model_args = ForwardTTSE2eArgs(spec_segment_size=10)
|
||||
config = FastPitchE2eConfig(model_args=model_args)
|
||||
model = ForwardTTSE2e.init_from_config(config, verbose=False).to(device)
|
||||
model.eval()
|
||||
model.on_init_start(trainer=None) # create mel_basis
|
||||
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_fast_pitch_e2e_tts_checkpoint.pth")
|
||||
model_args = ForwardTTSE2EArgs(spec_segment_size=10)
|
||||
config = FastPitchE2EConfig(model_args=model_args)
|
||||
model = ForwardTTSE2E.init_from_config(config, verbose=False).to(device)
|
||||
model_args = ForwardTTSE2eArgs(spec_segment_size=10)
|
||||
config = FastPitchE2eConfig(model_args=model_args)
|
||||
model = ForwardTTSE2e.init_from_config(config, verbose=False).to(device)
|
||||
chkp = {}
|
||||
chkp["model"] = model.state_dict()
|
||||
torch.save(chkp, chkp_path)
|
||||
|
|
@ -318,49 +319,47 @@ class TestFastPitchE2E(unittest.TestCase):
|
|||
self.assertFalse(model.training)
|
||||
|
||||
def test_get_criterion(self):
|
||||
model_args = ForwardTTSE2EArgs(spec_segment_size=10)
|
||||
config = FastPitchE2EConfig(model_args=model_args)
|
||||
model = ForwardTTSE2E.init_from_config(config, verbose=False).to(device)
|
||||
model_args = ForwardTTSE2eArgs(spec_segment_size=10)
|
||||
config = FastPitchE2eConfig(model_args=model_args)
|
||||
model = ForwardTTSE2e.init_from_config(config, verbose=False).to(device)
|
||||
criterion = model.get_criterion()
|
||||
self.assertTrue(criterion is not None)
|
||||
|
||||
def test_init_from_config(self):
|
||||
model_args = ForwardTTSE2EArgs(spec_segment_size=10)
|
||||
config = FastPitchE2EConfig(model_args=model_args)
|
||||
model = ForwardTTSE2E.init_from_config(config, verbose=False).to(device)
|
||||
model_args = ForwardTTSE2eArgs(spec_segment_size=10)
|
||||
config = FastPitchE2eConfig(model_args=model_args)
|
||||
model = ForwardTTSE2e.init_from_config(config, verbose=False).to(device)
|
||||
|
||||
model_args = ForwardTTSE2EArgs(spec_segment_size=10, num_speakers=2)
|
||||
config = FastPitchE2EConfig(model_args=model_args)
|
||||
model = ForwardTTSE2E.init_from_config(config, verbose=False).to(device)
|
||||
model_args = ForwardTTSE2eArgs(spec_segment_size=10, num_speakers=2)
|
||||
config = FastPitchE2eConfig(model_args=model_args)
|
||||
model = ForwardTTSE2e.init_from_config(config, verbose=False).to(device)
|
||||
self.assertTrue(not hasattr(model, "emb_g"))
|
||||
|
||||
model_args = ForwardTTSE2EArgs(
|
||||
spec_segment_size=10, num_speakers=2, use_speaker_embedding=True
|
||||
)
|
||||
config = FastPitchE2EConfig(model_args=model_args)
|
||||
model = ForwardTTSE2E.init_from_config(config, verbose=False).to(device)
|
||||
model_args = ForwardTTSE2eArgs(spec_segment_size=10, num_speakers=2, use_speaker_embedding=True)
|
||||
config = FastPitchE2eConfig(model_args=model_args)
|
||||
model = ForwardTTSE2e.init_from_config(config, verbose=False).to(device)
|
||||
self.assertEqual(model.num_speakers, 2)
|
||||
self.assertTrue(hasattr(model, "emb_g"))
|
||||
|
||||
model_args = ForwardTTSE2EArgs(
|
||||
spec_segment_size=10,
|
||||
model_args = ForwardTTSE2eArgs(
|
||||
spec_segment_size=10,
|
||||
num_speakers=2,
|
||||
use_speaker_embedding=True,
|
||||
speakers_file=os.path.join(get_tests_data_path(), "ljspeech", "speakers.json"),
|
||||
)
|
||||
config = FastPitchE2EConfig(model_args=model_args)
|
||||
model = ForwardTTSE2E.init_from_config(config, verbose=False).to(device)
|
||||
config = FastPitchE2eConfig(model_args=model_args)
|
||||
model = ForwardTTSE2e.init_from_config(config, verbose=False).to(device)
|
||||
self.assertEqual(model.num_speakers, 10)
|
||||
self.assertTrue(hasattr(model, "emb_g"))
|
||||
|
||||
model_args = ForwardTTSE2EArgs(
|
||||
spec_segment_size=10,
|
||||
model_args = ForwardTTSE2eArgs(
|
||||
spec_segment_size=10,
|
||||
use_d_vector_file=True,
|
||||
d_vector_dim=256,
|
||||
d_vector_file=os.path.join(get_tests_data_path(), "ljspeech", "speakers.json"),
|
||||
)
|
||||
config = FastPitchE2EConfig(model_args=model_args)
|
||||
model = ForwardTTSE2E.init_from_config(config, verbose=False).to(device)
|
||||
config = FastPitchE2eConfig(model_args=model_args)
|
||||
model = ForwardTTSE2e.init_from_config(config, verbose=False).to(device)
|
||||
self.assertTrue(model.num_speakers == 10)
|
||||
self.assertTrue(not hasattr(model, "emb_g"))
|
||||
self.assertTrue(model.embedded_speaker_dim == config.model_args.d_vector_dim)
|
||||
|
|
|
|||
Loading…
Reference in New Issue