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Add Torch-derived MelSpectrogramInjector

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This commit is contained in:
James Betker 2021-11-18 20:02:45 -07:00
parent 555b7e52ad
commit 687e0746b3
2 changed files with 27 additions and 3 deletions
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2
codes/scripts/audio/word_error_rate.py
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@ -57,7 +57,7 @@ class WordErrorRate:
if __name__ == '__main__':
inference_tsv = 'D:\\dlas\\codes\\46000ema_8beam.tsv'
inference_tsv = 'D:\\dlas\\codes\\results.tsv'
libri_base = 'Z:\\libritts\\test-clean'
wer = WordErrorRate()

28
codes/trainer/injectors/base_injectors.py
View File

@ -533,7 +533,6 @@ class DenormalizeInjector(Injector):
return {self.output: out}
# Performs normalization across fixed constants.
class MelSpectrogramInjector(Injector):
def __init__(self, opt, env):
super().__init__(opt, env)
@ -557,6 +556,31 @@ class MelSpectrogramInjector(Injector):
return {self.output: self.stft.mel_spectrogram(inp)}
class TorchMelSpectrogramInjector(Injector):
def __init__(self, opt, env):
super().__init__(opt, env)
# These are the default tacotron values for the MEL spectrogram.
self.filter_length = opt_get(opt, ['filter_length'], 1024)
self.hop_length = opt_get(opt, ['hop_length'], 256)
self.win_length = opt_get(opt, ['win_length'], 1024)
self.n_mel_channels = opt_get(opt, ['n_mel_channels'], 80)
self.mel_fmin = opt_get(opt, ['mel_fmin'], 0)
self.mel_fmax = opt_get(opt, ['mel_fmax'], 8000)
self.sampling_rate = opt_get(opt, ['sampling_rate'], 22050)
self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length,
win_length=self.win_length, power=2, normalized=True,
sample_rate=self.sampling_rate, f_min=self.mel_fmin,
f_max=self.mel_fmax, n_mels=self.n_mel_channels)
def forward(self, state):
inp = state[self.input]
if len(inp.shape) == 3: # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
inp = inp.squeeze(1)
assert len(inp.shape) == 2
mel = self.mel_stft(inp)
return {self.output: mel}
class RandomAudioCropInjector(Injector):
def __init__(self, opt, env):
super().__init__(opt, env)
@ -582,5 +606,5 @@ class AudioResampleInjector(Injector):
if __name__ == '__main__':
inj = MelSpectrogramInjector({'in': 'x', 'out': 'y'}, None)
inj = AudioResampleInjector({'in': 'x', 'out': 'y', 'input_sample_rate': 22050, 'output_sample_rate': '1'}, None)
print(inj({'x':torch.rand(10,1,40800)})['y'].shape)