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import os |
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import unittest |
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import torch |
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from tests import get_tests_input_path |
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from TTS.vc.configs.freevc_config import FreeVCConfig |
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from TTS.vc.models.freevc import FreeVC |
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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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c = FreeVCConfig() |
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WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav") |
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BATCH_SIZE = 3 |
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def count_parameters(model): |
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r"""Count number of trainable parameters in a network""" |
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return sum(p.numel() for p in model.parameters() if p.requires_grad) |
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class TestFreeVC(unittest.TestCase): |
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def _create_inputs(self, config, batch_size=2): |
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input_dummy = torch.rand(batch_size, 30 * config.audio["hop_length"]).to(device) |
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input_lengths = torch.randint(100, 30 * config.audio["hop_length"], (batch_size,)).long().to(device) |
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input_lengths[-1] = 30 * config.audio["hop_length"] |
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spec = torch.rand(batch_size, 30, config.audio["filter_length"] // 2 + 1).to(device) |
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mel = torch.rand(batch_size, 30, config.audio["n_mel_channels"]).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(2) |
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waveform = torch.rand(batch_size, spec.size(2) * config.audio["hop_length"]).to(device) |
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return input_dummy, input_lengths, mel, spec, spec_lengths, waveform |
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@staticmethod |
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def _create_inputs_inference(): |
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source_wav = torch.rand(16000) |
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target_wav = torch.rand(16000) |
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return source_wav, target_wav |
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@staticmethod |
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def _check_parameter_changes(model, model_ref): |
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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).any(), "param {} with shape {} not updated!! \n{}\n{}".format( |
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count, param.shape, param, param_ref |
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) |
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count += 1 |
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def test_methods(self): |
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config = FreeVCConfig() |
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model = FreeVC(config).to(device) |
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model.load_pretrained_speaker_encoder() |
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model.init_multispeaker(config) |
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wavlm_feats = model.extract_wavlm_features(torch.rand(1, 16000)) |
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assert wavlm_feats.shape == (1, 1024, 49), wavlm_feats.shape |
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def test_load_audio(self): |
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config = FreeVCConfig() |
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model = FreeVC(config).to(device) |
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wav = model.load_audio(WAV_FILE) |
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wav2 = model.load_audio(wav) |
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assert all(torch.isclose(wav, wav2)) |
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def _test_forward(self, batch_size): |
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config = FreeVCConfig() |
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model = FreeVC(config).to(device) |
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model.train() |
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print(" > Num parameters for FreeVC model:%s" % (count_parameters(model))) |
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_, _, mel, spec, spec_lengths, waveform = self._create_inputs(config, batch_size) |
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wavlm_vec = model.extract_wavlm_features(waveform) |
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wavlm_vec_lengths = torch.ones(batch_size, dtype=torch.long) |
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y = model.forward(wavlm_vec, spec, None, mel, spec_lengths, wavlm_vec_lengths) |
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def test_forward(self): |
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self._test_forward(1) |
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self._test_forward(3) |
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def _test_inference(self, batch_size): |
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config = FreeVCConfig() |
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model = FreeVC(config).to(device) |
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model.eval() |
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_, _, mel, _, _, waveform = self._create_inputs(config, batch_size) |
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wavlm_vec = model.extract_wavlm_features(waveform) |
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wavlm_vec_lengths = torch.ones(batch_size, dtype=torch.long) |
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output_wav = model.inference(wavlm_vec, None, mel, wavlm_vec_lengths) |
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assert ( |
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output_wav.shape[-1] // config.audio.hop_length == wavlm_vec.shape[-1] |
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), f"{output_wav.shape[-1] // config.audio.hop_length} != {wavlm_vec.shape}" |
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def test_inference(self): |
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self._test_inference(1) |
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self._test_inference(3) |
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def test_voice_conversion(self): |
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config = FreeVCConfig() |
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model = FreeVC(config).to(device) |
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model.eval() |
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source_wav, target_wav = self._create_inputs_inference() |
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output_wav = model.voice_conversion(source_wav, target_wav) |
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assert ( |
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output_wav.shape[0] + config.audio.hop_length == source_wav.shape[0] |
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), f"{output_wav.shape} != {source_wav.shape}" |
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def test_train_step(self): |
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... |
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def test_train_eval_log(self): |
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... |
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def test_test_run(self): |
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... |
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def test_load_checkpoint(self): |
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... |
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def test_get_criterion(self): |
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... |
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def test_init_from_config(self): |
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... |
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