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TTS/vc/modules/freevc/speaker_encoder/audio.py

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+import struct
+from pathlib import Path
+from typing import Optional, Union
+
+# import webrtcvad
+import librosa
+import numpy as np
+from scipy.ndimage.morphology import binary_dilation
+
+from TTS.vc.modules.freevc.speaker_encoder.hparams import *
+
+int16_max = (2**15) - 1
+
+
+def preprocess_wav(fpath_or_wav: Union[str, Path, np.ndarray], source_sr: Optional[int] = None):
+    """
+    Applies the preprocessing operations used in training the Speaker Encoder to a waveform
+    either on disk or in memory. The waveform will be resampled to match the data hyperparameters.
+
+    :param fpath_or_wav: either a filepath to an audio file (many extensions are supported, not
+    just .wav), either the waveform as a numpy array of floats.
+    :param source_sr: if passing an audio waveform, the sampling rate of the waveform before
+    preprocessing. After preprocessing, the waveform's sampling rate will match the data
+    hyperparameters. If passing a filepath, the sampling rate will be automatically detected and
+    this argument will be ignored.
+    """
+    # Load the wav from disk if needed
+    if isinstance(fpath_or_wav, str) or isinstance(fpath_or_wav, Path):
+        wav, source_sr = librosa.load(fpath_or_wav, sr=None)
+    else:
+        wav = fpath_or_wav
+
+    # Resample the wav if needed
+    if source_sr is not None and source_sr != sampling_rate:
+        wav = librosa.resample(wav, source_sr, sampling_rate)
+
+    # Apply the preprocessing: normalize volume and shorten long silences
+    wav = normalize_volume(wav, audio_norm_target_dBFS, increase_only=True)
+    wav = trim_long_silences(wav)
+
+    return wav
+
+
+def wav_to_mel_spectrogram(wav):
+    """
+    Derives a mel spectrogram ready to be used by the encoder from a preprocessed audio waveform.
+    Note: this not a log-mel spectrogram.
+    """
+    frames = librosa.feature.melspectrogram(
+        y=wav,
+        sr=sampling_rate,
+        n_fft=int(sampling_rate * mel_window_length / 1000),
+        hop_length=int(sampling_rate * mel_window_step / 1000),
+        n_mels=mel_n_channels,
+    )
+    return frames.astype(np.float32).T
+
+
+def normalize_volume(wav, target_dBFS, increase_only=False, decrease_only=False):
+    if increase_only and decrease_only:
+        raise ValueError("Both increase only and decrease only are set")
+    dBFS_change = target_dBFS - 10 * np.log10(np.mean(wav**2))
+    if (dBFS_change < 0 and increase_only) or (dBFS_change > 0 and decrease_only):
+        return wav
+    return wav * (10 ** (dBFS_change / 20))