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