refactor: move amp_to_db/db_to_amp into torch_transforms

TTS/tts/layers/tortoise/audio_utils.py

@@ -9,7 +9,7 @@ import torch
 import torchaudio
 from scipy.io.wavfile import read
 
-from TTS.utils.audio.torch_transforms import TorchSTFT
+from TTS.utils.audio.torch_transforms import TorchSTFT, amp_to_db
 from TTS.utils.generic_utils import is_pytorch_at_least_2_4
 
 logger = logging.getLogger(__name__)
@@ -88,24 +88,6 @@ def normalize_tacotron_mel(mel):
     return 2 * ((mel - TACOTRON_MEL_MIN) / (TACOTRON_MEL_MAX - TACOTRON_MEL_MIN)) - 1
 
 
-def dynamic_range_compression(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
 def get_voices(extra_voice_dirs: List[str] = []):
     dirs = extra_voice_dirs
     voices: Dict[str, List[str]] = {}
@@ -175,7 +157,7 @@ def wav_to_univnet_mel(wav, do_normalization=False, device="cuda"):
     )
     stft = stft.to(device)
     mel = stft(wav)
-    mel = dynamic_range_compression(mel)
+    mel = amp_to_db(mel)
     if do_normalization:
         mel = normalize_tacotron_mel(mel)
     return mel

TTS/tts/models/delightful_tts.py

@@ -32,6 +32,7 @@ from TTS.utils.audio.numpy_transforms import build_mel_basis, compute_f0
 from TTS.utils.audio.numpy_transforms import db_to_amp as db_to_amp_numpy
 from TTS.utils.audio.numpy_transforms import mel_to_wav as mel_to_wav_numpy
 from TTS.utils.audio.processor import AudioProcessor
+from TTS.utils.audio.torch_transforms import amp_to_db
 from TTS.vocoder.layers.losses import MultiScaleSTFTLoss
 from TTS.vocoder.models.hifigan_generator import HifiganGenerator
 from TTS.vocoder.utils.generic_utils import plot_results
@@ -136,24 +137,6 @@ def load_audio(file_path: str):
     return x, sr
 
 
-def _amp_to_db(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def _db_to_amp(x, C=1):
-    return torch.exp(x) / C
-
-
-def amp_to_db(magnitudes):
-    output = _amp_to_db(magnitudes)
-    return output
-
-
-def db_to_amp(magnitudes):
-    output = _db_to_amp(magnitudes)
-    return output
-
-
 def _wav_to_spec(y, n_fft, hop_length, win_length, center=False):
     y = y.squeeze(1)
 

TTS/tts/models/vits.py

@@ -35,6 +35,7 @@ from TTS.tts.utils.synthesis import synthesis
 from TTS.tts.utils.text.characters import BaseCharacters, BaseVocabulary, _characters, _pad, _phonemes, _punctuations
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.tts.utils.visual import plot_alignment
+from TTS.utils.audio.torch_transforms import amp_to_db
 from TTS.utils.samplers import BucketBatchSampler
 from TTS.vocoder.models.hifigan_generator import HifiganGenerator
 from TTS.vocoder.utils.generic_utils import plot_results
@@ -78,24 +79,6 @@ def load_audio(file_path):
     return x, sr
 
 
-def _amp_to_db(x, C=1, clip_val=1e-5):
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def _db_to_amp(x, C=1):
-    return torch.exp(x) / C
-
-
-def amp_to_db(magnitudes):
-    output = _amp_to_db(magnitudes)
-    return output
-
-
-def db_to_amp(magnitudes):
-    output = _db_to_amp(magnitudes)
-    return output
-
-
 def wav_to_spec(y, n_fft, hop_length, win_length, center=False):
     """
     Args Shapes:

TTS/utils/audio/numpy_transforms.py

@@ -59,7 +59,7 @@ def _exp(x, base):
     return np.exp(x)
 
 
-def amp_to_db(*, x: np.ndarray, gain: float = 1, base: int = 10, **kwargs) -> np.ndarray:
+def amp_to_db(*, x: np.ndarray, gain: float = 1, base: float = 10, **kwargs) -> np.ndarray:
     """Convert amplitude values to decibels.
 
     Args:

TTS/utils/audio/torch_transforms.py

@@ -3,6 +3,16 @@ import torch
 from torch import nn
 
 
+def amp_to_db(x: torch.Tensor, *, spec_gain: float = 1.0, clip_val: float = 1e-5) -> torch.Tensor:
+    """Spectral normalization / dynamic range compression."""
+    return torch.log(torch.clamp(x, min=clip_val) * spec_gain)
+
+
+def db_to_amp(x: torch.Tensor, *, spec_gain: float = 1.0) -> torch.Tensor:
+    """Spectral denormalization / dynamic range decompression."""
+    return torch.exp(x) / spec_gain
+
+
 class TorchSTFT(nn.Module):  # pylint: disable=abstract-method
     """Some of the audio processing funtions using Torch for faster batch processing.
 
@@ -157,11 +167,3 @@ class TorchSTFT(nn.Module):  # pylint: disable=abstract-method
             norm=self.mel_norm,
         )
         self.mel_basis = torch.from_numpy(mel_basis).float()
-
-    @staticmethod
-    def _amp_to_db(x, spec_gain=1.0):
-        return torch.log(torch.clamp(x, min=1e-5) * spec_gain)
-
-    @staticmethod
-    def _db_to_amp(x, spec_gain=1.0):
-        return torch.exp(x) / spec_gain

TTS/vc/modules/freevc/mel_processing.py

@@ -4,39 +4,12 @@ import torch
 import torch.utils.data
 from librosa.filters import mel as librosa_mel_fn
 
+from TTS.utils.audio.torch_transforms import amp_to_db
+
 logger = logging.getLogger(__name__)
 
 MAX_WAV_VALUE = 32768.0
 
-
-def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
-    """
-    PARAMS
-    ------
-    C: compression factor
-    """
-    return torch.log(torch.clamp(x, min=clip_val) * C)
-
-
-def dynamic_range_decompression_torch(x, C=1):
-    """
-    PARAMS
-    ------
-    C: compression factor used to compress
-    """
-    return torch.exp(x) / C
-
-
-def spectral_normalize_torch(magnitudes):
-    output = dynamic_range_compression_torch(magnitudes)
-    return output
-
-
-def spectral_de_normalize_torch(magnitudes):
-    output = dynamic_range_decompression_torch(magnitudes)
-    return output
-
-
 mel_basis = {}
 hann_window = {}
 
@@ -85,7 +58,7 @@ def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
         mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
         mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
     spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
+    spec = amp_to_db(spec)
     return spec
 
 
@@ -128,6 +101,6 @@ def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size,
     spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
 
     spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
-    spec = spectral_normalize_torch(spec)
+    spec = amp_to_db(spec)
 
     return spec

tests/aux_tests/test_torch_transforms.py

@@ -0,0 +1,16 @@
+import numpy as np
+import torch
+
+from TTS.utils.audio import numpy_transforms as np_transforms
+from TTS.utils.audio.torch_transforms import amp_to_db, db_to_amp
+
+
+def test_amplitude_db_conversion():
+    x = torch.rand(11)
+    o1 = amp_to_db(x=x, spec_gain=1.0)
+    o2 = db_to_amp(x=o1, spec_gain=1.0)
+    np_o1 = np_transforms.amp_to_db(x=x, base=np.e)
+    np_o2 = np_transforms.db_to_amp(x=np_o1, base=np.e)
+    assert torch.allclose(x, o2)
+    assert torch.allclose(o1, np_o1)
+    assert torch.allclose(o2, np_o2)

tests/tts_tests/test_vits.py

@@ -13,14 +13,13 @@ from TTS.tts.models.vits import (
     Vits,
     VitsArgs,
     VitsAudioConfig,
-    amp_to_db,
-    db_to_amp,
     load_audio,
     spec_to_mel,
     wav_to_mel,
     wav_to_spec,
 )
 from TTS.tts.utils.speakers import SpeakerManager
+from TTS.utils.audio.torch_transforms import amp_to_db, db_to_amp
 
 LANG_FILE = os.path.join(get_tests_input_path(), "language_ids.json")
 SPEAKER_ENCODER_CONFIG = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")