refactor(audio.processor): remove duplicate quantization methods

TTS/bin/extract_tts_spectrograms.py

@@ -15,6 +15,7 @@ from TTS.tts.models import setup_model
 from TTS.tts.utils.speakers import SpeakerManager
 from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
+from TTS.utils.audio.numpy_transforms import quantize
 from TTS.utils.generic_utils import count_parameters
 
 use_cuda = torch.cuda.is_available()
@@ -197,7 +198,7 @@ def extract_spectrograms(
 
             # quantize and save wav
             if quantize_bits > 0:
-                wavq = ap.quantize(wav, quantize_bits)
+                wavq = quantize(wav, quantize_bits)
                 np.save(wavq_path, wavq)
 
             # save TTS mel

TTS/utils/audio/processor.py

@@ -631,43 +631,3 @@ class AudioProcessor(object):
             filename (str): Path to the wav file.
         """
         return librosa.get_duration(filename=filename)
-
-    @staticmethod
-    def mulaw_encode(wav: np.ndarray, qc: int) -> np.ndarray:
-        mu = 2**qc - 1
-        # wav_abs = np.minimum(np.abs(wav), 1.0)
-        signal = np.sign(wav) * np.log(1 + mu * np.abs(wav)) / np.log(1.0 + mu)
-        # Quantize signal to the specified number of levels.
-        signal = (signal + 1) / 2 * mu + 0.5
-        return np.floor(
-            signal,
-        )
-
-    @staticmethod
-    def mulaw_decode(wav, qc):
-        """Recovers waveform from quantized values."""
-        mu = 2**qc - 1
-        x = np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)
-        return x
-
-    @staticmethod
-    def encode_16bits(x):
-        return np.clip(x * 2**15, -(2**15), 2**15 - 1).astype(np.int16)
-
-    @staticmethod
-    def quantize(x: np.ndarray, bits: int) -> np.ndarray:
-        """Quantize a waveform to a given number of bits.
-
-        Args:
-            x (np.ndarray): Waveform to quantize. Must be normalized into the range `[-1, 1]`.
-            bits (int): Number of quantization bits.
-
-        Returns:
-            np.ndarray: Quantized waveform.
-        """
-        return (x + 1.0) * (2**bits - 1) / 2
-
-    @staticmethod
-    def dequantize(x, bits):
-        """Dequantize a waveform from the given number of bits."""
-        return 2 * x / (2**bits - 1) - 1

TTS/vocoder/datasets/preprocess.py

@@ -7,6 +7,7 @@ from coqpit import Coqpit
 from tqdm import tqdm
 
 from TTS.utils.audio import AudioProcessor
+from TTS.utils.audio.numpy_transforms import mulaw_encode, quantize
 
 
 def preprocess_wav_files(out_path: str, config: Coqpit, ap: AudioProcessor):
@@ -29,7 +30,11 @@ def preprocess_wav_files(out_path: str, config: Coqpit, ap: AudioProcessor):
         mel = ap.melspectrogram(y)
         np.save(mel_path, mel)
         if isinstance(config.mode, int):
-            quant = ap.mulaw_encode(y, qc=config.mode) if config.model_args.mulaw else ap.quantize(y, bits=config.mode)
+            quant = (
+                mulaw_encode(wav=y, mulaw_qc=config.mode)
+                if config.model_args.mulaw
+                else quantize(x=y, quantize_bits=config.mode)
+            )
             np.save(quant_path, quant)
 
 

TTS/vocoder/datasets/wavernn_dataset.py

@@ -2,6 +2,8 @@ import numpy as np
 import torch
 from torch.utils.data import Dataset
 
+from TTS.utils.audio.numpy_transforms import mulaw_encode, quantize
+
 
 class WaveRNNDataset(Dataset):
     """
@@ -66,7 +68,9 @@ class WaveRNNDataset(Dataset):
                 x_input = audio
             elif isinstance(self.mode, int):
                 x_input = (
-                    self.ap.mulaw_encode(audio, qc=self.mode) if self.mulaw else self.ap.quantize(audio, bits=self.mode)
+                    mulaw_encode(wav=audio, mulaw_qc=self.mode)
+                    if self.mulaw
+                    else quantize(x=audio, quantize_bits=self.mode)
                 )
             else:
                 raise RuntimeError("Unknown dataset mode - ", self.mode)

TTS/vocoder/models/wavernn.py

@@ -13,6 +13,7 @@ from torch.utils.data.distributed import DistributedSampler
 
 from TTS.tts.utils.visual import plot_spectrogram
 from TTS.utils.audio import AudioProcessor
+from TTS.utils.audio.numpy_transforms import mulaw_decode
 from TTS.utils.io import load_fsspec
 from TTS.vocoder.datasets.wavernn_dataset import WaveRNNDataset
 from TTS.vocoder.layers.losses import WaveRNNLoss
@@ -399,7 +400,7 @@ class Wavernn(BaseVocoder):
             output = output[0]
 
         if self.args.mulaw and isinstance(self.args.mode, int):
-            output = AudioProcessor.mulaw_decode(output, self.args.mode)
+            output = mulaw_decode(wav=output, mulaw_qc=self.args.mode)
 
         # Fade-out at the end to avoid signal cutting out suddenly
         fade_out = np.linspace(1, 0, 20 * self.config.audio.hop_length)

notebooks/ExtractTTSpectrogram.ipynb

@@ -34,6 +34,7 @@
     "from TTS.tts.utils.text.tokenizer import TTSTokenizer\n",
     "from TTS.tts.utils.visual import plot_spectrogram\n",
     "from TTS.utils.audio import AudioProcessor\n",
+    "from TTS.utils.audio.numpy_transforms import quantize\n",
     "\n",
     "%matplotlib inline\n",
     "\n",
@@ -190,7 +191,7 @@
     "\n",
     "                    # quantize and save wav\n",
     "                    if QUANTIZE_BITS > 0:\n",
-    "                        wavq = ap.quantize(wav, QUANTIZE_BITS)\n",
+    "                        wavq = quantize(wav, QUANTIZE_BITS)\n",
     "                        np.save(wavq_path, wavq)\n",
     "\n",
     "                    # save TTS mel\n",