mirror of
https://github.com/coqui-ai/TTS.git
synced 2025-12-24 20:29:30 +01:00
3c2d5a9e03
* chore: remove unused argument * refactor(audio.processor): remove duplicate stft+griffin_lim * chore(audio.processor): remove unused compute_stft_paddings Same function available in numpy_transforms * refactor(audio.processor): remove duplicate db_to_amp * refactor(audio.processor): remove duplicate amp_to_db * refactor(audio.processor): remove duplicate linear_to_mel * refactor(audio.processor): remove duplicate mel_to_linear * refactor(audio.processor): remove duplicate build_mel_basis * refactor(audio.processor): remove duplicate stft_parameters * refactor(audio.processor): use pre-/deemphasis from numpy_transforms * refactor(audio.processor): use rms_volume_norm from numpy_transforms * chore(audio.processor): remove duplicate assert Already checked in numpy_transforms.compute_f0 * refactor(audio.processor): use find_endpoint from numpy_transforms * refactor(audio.processor): use trim_silence from numpy_transforms * refactor(audio.processor): use volume_norm from numpy_transforms * refactor(audio.processor): use load_wav from numpy_transforms * fix(bin.extract_tts_spectrograms): set quantization bits * fix(ExtractTTSpectrogram.ipynb): adapt to current TTS code Fixes #2447, #2574 * refactor(audio.processor): remove duplicate quantization methods
94 lines
3.0 KiB
Python
94 lines
3.0 KiB
Python
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.utils.audio.numpy_transforms import stft
|
|
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(stft(y=wav, fft_size=ap.fft_size, hop_length=ap.hop_length, win_length=ap.win_length))
|
|
# 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
|