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refactor(audio.processor): remove duplicate db_to_amp

This commit is contained in:
Enno Hermann 2023-11-13 21:49:45 +01:00
parent 5a5da76260
commit 794f41c611
1 changed files with 5 additions and 17 deletions
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22
TTS/utils/audio/processor.py
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@ -8,7 +8,7 @@ import scipy.signal
import soundfile as sf import soundfile as sf
from TTS.tts.utils.helpers import StandardScaler from TTS.tts.utils.helpers import StandardScaler
from TTS.utils.audio.numpy_transforms import compute_f0, stft, griffin_lim from TTS.utils.audio.numpy_transforms import compute_f0, db_to_amp, stft, griffin_lim
# pylint: disable=too-many-public-methods # pylint: disable=too-many-public-methods
@ -399,18 +399,6 @@ class AudioProcessor(object):
""" """
return self.spec_gain * _log(np.maximum(1e-5, x), self.base) return self.spec_gain * _log(np.maximum(1e-5, x), self.base)
# pylint: disable=no-self-use
def _db_to_amp(self, x: np.ndarray) -> np.ndarray:
"""Convert decibels spectrogram to amplitude spectrogram.
Args:
x (np.ndarray): Decibels spectrogram.
Returns:
np.ndarray: Amplitude spectrogram.
"""
return _exp(x / self.spec_gain, self.base)
### Preemphasis ### ### Preemphasis ###
def apply_preemphasis(self, x: np.ndarray) -> np.ndarray: def apply_preemphasis(self, x: np.ndarray) -> np.ndarray:
"""Apply pre-emphasis to the audio signal. Useful to reduce the correlation between neighbouring signal values. """Apply pre-emphasis to the audio signal. Useful to reduce the correlation between neighbouring signal values.
@ -494,7 +482,7 @@ class AudioProcessor(object):
def inv_spectrogram(self, spectrogram: np.ndarray) -> np.ndarray: def inv_spectrogram(self, spectrogram: np.ndarray) -> np.ndarray:
"""Convert a spectrogram to a waveform using Griffi-Lim vocoder.""" """Convert a spectrogram to a waveform using Griffi-Lim vocoder."""
S = self.denormalize(spectrogram) S = self.denormalize(spectrogram)
S = self._db_to_amp(S) S = db_to_amp(x=S, gain=self.spec_gain, base=self.base)
# Reconstruct phase # Reconstruct phase
W = self._griffin_lim(S**self.power) W = self._griffin_lim(S**self.power)
return self.apply_inv_preemphasis(W) if self.preemphasis != 0 else W return self.apply_inv_preemphasis(W) if self.preemphasis != 0 else W
@ -502,7 +490,7 @@ class AudioProcessor(object):
def inv_melspectrogram(self, mel_spectrogram: np.ndarray) -> np.ndarray: def inv_melspectrogram(self, mel_spectrogram: np.ndarray) -> np.ndarray:
"""Convert a melspectrogram to a waveform using Griffi-Lim vocoder.""" """Convert a melspectrogram to a waveform using Griffi-Lim vocoder."""
D = self.denormalize(mel_spectrogram) D = self.denormalize(mel_spectrogram)
S = self._db_to_amp(D) S = db_to_amp(x=D, gain=self.spec_gain, base=self.base)
S = self._mel_to_linear(S) # Convert back to linear S = self._mel_to_linear(S) # Convert back to linear
W = self._griffin_lim(S**self.power) W = self._griffin_lim(S**self.power)
return self.apply_inv_preemphasis(W) if self.preemphasis != 0 else W return self.apply_inv_preemphasis(W) if self.preemphasis != 0 else W
@ -517,7 +505,7 @@ class AudioProcessor(object):
np.ndarray: Normalized melspectrogram. np.ndarray: Normalized melspectrogram.
""" """
S = self.denormalize(linear_spec) S = self.denormalize(linear_spec)
S = self._db_to_amp(S) S = db_to_amp(x=S, gain=self.spec_gain, base=self.base)
S = self._linear_to_mel(np.abs(S)) S = self._linear_to_mel(np.abs(S))
S = self._amp_to_db(S) S = self._amp_to_db(S)
mel = self.normalize(S) mel = self.normalize(S)
@ -584,7 +572,7 @@ class AudioProcessor(object):
""" """
window_length = int(self.sample_rate * min_silence_sec) window_length = int(self.sample_rate * min_silence_sec)
hop_length = int(window_length / 4) hop_length = int(window_length / 4)
threshold = self._db_to_amp(-self.trim_db) threshold = db_to_amp(x=-self.trim_db, gain=self.spec_gain, base=self.base)
for x in range(hop_length, len(wav) - window_length, hop_length): for x in range(hop_length, len(wav) - window_length, hop_length):
if np.max(wav[x : x + window_length]) < threshold: if np.max(wav[x : x + window_length]) < threshold:
return x + hop_length return x + hop_length