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on
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Running
on
Zero
| import os | |
| import torch | |
| from tests import get_tests_input_path, get_tests_output_path, get_tests_path | |
| from TTS.config import BaseAudioConfig | |
| from TTS.utils.audio import AudioProcessor | |
| from TTS.vocoder.layers.losses import MelganFeatureLoss, MultiScaleSTFTLoss, STFTLoss, TorchSTFT | |
| TESTS_PATH = get_tests_path() | |
| OUT_PATH = os.path.join(get_tests_output_path(), "audio_tests") | |
| os.makedirs(OUT_PATH, exist_ok=True) | |
| WAV_FILE = os.path.join(get_tests_input_path(), "example_1.wav") | |
| ap = AudioProcessor(**BaseAudioConfig().to_dict()) | |
| def test_torch_stft(): | |
| torch_stft = TorchSTFT(ap.fft_size, ap.hop_length, ap.win_length) | |
| # librosa stft | |
| wav = ap.load_wav(WAV_FILE) | |
| M_librosa = abs(ap._stft(wav)) # pylint: disable=protected-access | |
| # torch stft | |
| wav = torch.from_numpy(wav[None, :]).float() | |
| M_torch = torch_stft(wav) | |
| # check the difference b/w librosa and torch outputs | |
| assert (M_librosa - M_torch[0].data.numpy()).max() < 1e-5 | |
| def test_stft_loss(): | |
| stft_loss = STFTLoss(ap.fft_size, ap.hop_length, ap.win_length) | |
| wav = ap.load_wav(WAV_FILE) | |
| wav = torch.from_numpy(wav[None, :]).float() | |
| loss_m, loss_sc = stft_loss(wav, wav) | |
| assert loss_m + loss_sc == 0 | |
| loss_m, loss_sc = stft_loss(wav, torch.rand_like(wav)) | |
| assert loss_sc < 1.0 | |
| assert loss_m + loss_sc > 0 | |
| def test_multiscale_stft_loss(): | |
| stft_loss = MultiScaleSTFTLoss( | |
| [ap.fft_size // 2, ap.fft_size, ap.fft_size * 2], | |
| [ap.hop_length // 2, ap.hop_length, ap.hop_length * 2], | |
| [ap.win_length // 2, ap.win_length, ap.win_length * 2], | |
| ) | |
| wav = ap.load_wav(WAV_FILE) | |
| wav = torch.from_numpy(wav[None, :]).float() | |
| loss_m, loss_sc = stft_loss(wav, wav) | |
| assert loss_m + loss_sc == 0 | |
| loss_m, loss_sc = stft_loss(wav, torch.rand_like(wav)) | |
| assert loss_sc < 1.0 | |
| assert loss_m + loss_sc > 0 | |
| def test_melgan_feature_loss(): | |
| feats_real = [] | |
| feats_fake = [] | |
| # if all the features are different. | |
| for _ in range(5): # different scales | |
| scale_feats_real = [] | |
| scale_feats_fake = [] | |
| for _ in range(4): # different layers | |
| scale_feats_real.append(torch.rand([3, 5, 7])) | |
| scale_feats_fake.append(torch.rand([3, 5, 7])) | |
| feats_real.append(scale_feats_real) | |
| feats_fake.append(scale_feats_fake) | |
| loss_func = MelganFeatureLoss() | |
| loss = loss_func(feats_fake, feats_real) | |
| assert loss.item() <= 1.0 | |
| feats_real = [] | |
| feats_fake = [] | |
| # if all the features are the same | |
| for _ in range(5): # different scales | |
| scale_feats_real = [] | |
| scale_feats_fake = [] | |
| for _ in range(4): # different layers | |
| tensor = torch.rand([3, 5, 7]) | |
| scale_feats_real.append(tensor) | |
| scale_feats_fake.append(tensor) | |
| feats_real.append(scale_feats_real) | |
| feats_fake.append(scale_feats_fake) | |
| loss_func = MelganFeatureLoss() | |
| loss = loss_func(feats_fake, feats_real) | |
| assert loss.item() == 0 | |