import logging
from typing import Optional
import librosa
import numpy as np
from TTS.tts.utils.helpers import StandardScaler
from TTS.utils.audio.numpy_transforms import (
amp_to_db,
build_mel_basis,
compute_f0,
db_to_amp,
deemphasis,
find_endpoint,
griffin_lim,
load_wav,
mel_to_spec,
millisec_to_length,
preemphasis,
rms_volume_norm,
save_wav,
spec_to_mel,
stft,
trim_silence,
volume_norm,
)
logger = logging.getLogger(__name__)
# pylint: disable=too-many-public-methods
class AudioProcessor:
"""Audio Processor for TTS.
Note:
All the class arguments are set to default values to enable a flexible initialization
of the class with the model config. They are not meaningful for all the arguments.
Args:
sample_rate (int, optional):
target audio sampling rate. Defaults to None.
resample (bool, optional):
enable/disable resampling of the audio clips when the target sampling rate does not match the original sampling rate. Defaults to False.
num_mels (int, optional):
number of melspectrogram dimensions. Defaults to None.
log_func (int, optional):
log exponent used for converting spectrogram aplitude to DB.
min_level_db (int, optional):
minimum db threshold for the computed melspectrograms. Defaults to None.
frame_shift_ms (int, optional):
milliseconds of frames between STFT columns. Defaults to None.
frame_length_ms (int, optional):
milliseconds of STFT window length. Defaults to None.
hop_length (int, optional):
number of frames between STFT columns. Used if ```frame_shift_ms``` is None. Defaults to None.
win_length (int, optional):
STFT window length. Used if ```frame_length_ms``` is None. Defaults to None.
ref_level_db (int, optional):
reference DB level to avoid background noise. In general <20DB corresponds to the air noise. Defaults to None.
fft_size (int, optional):
FFT window size for STFT. Defaults to 1024.
power (int, optional):
Exponent value applied to the spectrogram before GriffinLim. Defaults to None.
preemphasis (float, optional):
Preemphasis coefficient. Preemphasis is disabled if == 0.0. Defaults to 0.0.
signal_norm (bool, optional):
enable/disable signal normalization. Defaults to None.
symmetric_norm (bool, optional):
enable/disable symmetric normalization. If set True normalization is performed in the range [-k, k] else [0, k], Defaults to None.
max_norm (float, optional):
```k``` defining the normalization range. Defaults to None.
mel_fmin (int, optional):
minimum filter frequency for computing melspectrograms. Defaults to None.
mel_fmax (int, optional):
maximum filter frequency for computing melspectrograms. Defaults to None.
pitch_fmin (int, optional):
minimum filter frequency for computing pitch. Defaults to None.
pitch_fmax (int, optional):
maximum filter frequency for computing pitch. Defaults to None.
spec_gain (int, optional):
gain applied when converting amplitude to DB. Defaults to 20.
stft_pad_mode (str, optional):
Padding mode for STFT. Defaults to 'reflect'.
clip_norm (bool, optional):
enable/disable clipping the our of range values in the normalized audio signal. Defaults to True.
griffin_lim_iters (int, optional):
Number of GriffinLim iterations. Defaults to None.
do_trim_silence (bool, optional):
enable/disable silence trimming when loading the audio signal. Defaults to False.
trim_db (int, optional):
DB threshold used for silence trimming. Defaults to 60.
do_sound_norm (bool, optional):
enable/disable signal normalization. Defaults to False.
do_amp_to_db_linear (bool, optional):
enable/disable amplitude to dB conversion of linear spectrograms. Defaults to True.
do_amp_to_db_mel (bool, optional):
enable/disable amplitude to dB conversion of mel spectrograms. Defaults to True.
do_rms_norm (bool, optional):
enable/disable RMS volume normalization when loading an audio file. Defaults to False.
db_level (int, optional):
dB level used for rms normalization. The range is -99 to 0. Defaults to None.
stats_path (str, optional):
Path to the computed stats file. Defaults to None.
"""
def __init__(
self,
sample_rate=None,
resample=False,
num_mels=None,
log_func="np.log10",
min_level_db=None,
frame_shift_ms=None,
frame_length_ms=None,
hop_length=None,
win_length=None,
ref_level_db=None,
fft_size=1024,
power=None,
preemphasis=0.0,
signal_norm=None,
symmetric_norm=None,
max_norm=None,
mel_fmin=None,
mel_fmax=None,
pitch_fmax=None,
pitch_fmin=None,
spec_gain=20,
stft_pad_mode="reflect",
clip_norm=True,
griffin_lim_iters=None,
do_trim_silence=False,
trim_db=60,
do_sound_norm=False,
do_amp_to_db_linear=True,
do_amp_to_db_mel=True,
do_rms_norm=False,
db_level=None,
stats_path=None,
**_,
) -> None:
# setup class attributed
self.sample_rate = sample_rate
self.resample = resample
self.num_mels = num_mels
self.log_func = log_func
self.min_level_db = min_level_db or 0
self.frame_shift_ms = frame_shift_ms
self.frame_length_ms = frame_length_ms
self.ref_level_db = ref_level_db
self.fft_size = fft_size
self.power = power
self.preemphasis = preemphasis
self.griffin_lim_iters = griffin_lim_iters
self.signal_norm = signal_norm
self.symmetric_norm = symmetric_norm
self.mel_fmin = mel_fmin or 0
self.mel_fmax = mel_fmax
self.pitch_fmin = pitch_fmin
self.pitch_fmax = pitch_fmax
self.spec_gain = float(spec_gain)
self.stft_pad_mode = stft_pad_mode
self.max_norm = 1.0 if max_norm is None else float(max_norm)
self.clip_norm = clip_norm
self.do_trim_silence = do_trim_silence
self.trim_db = trim_db
self.do_sound_norm = do_sound_norm
self.do_amp_to_db_linear = do_amp_to_db_linear
self.do_amp_to_db_mel = do_amp_to_db_mel
self.do_rms_norm = do_rms_norm
self.db_level = db_level
self.stats_path = stats_path
# setup exp_func for db to amp conversion
if log_func == "np.log":
self.base = np.e
elif log_func == "np.log10":
self.base = 10
else:
msg = " [!] unknown `log_func` value."
raise ValueError(msg)
# setup stft parameters
if hop_length is None:
# compute stft parameters from given time values
self.win_length, self.hop_length = millisec_to_length(
frame_length_ms=self.frame_length_ms, frame_shift_ms=self.frame_shift_ms, sample_rate=self.sample_rate
)
else:
# use stft parameters from config file
self.hop_length = hop_length
self.win_length = win_length
assert min_level_db != 0.0, " [!] min_level_db is 0"
assert (
self.win_length <= self.fft_size
), f" [!] win_length cannot be larger than fft_size - {self.win_length} vs {self.fft_size}"
members = vars(self)
logger.info("Setting up Audio Processor...")
for key, value in members.items():
logger.info(" | %s: %s", key, value)
# create spectrogram utils
self.mel_basis = build_mel_basis(
sample_rate=self.sample_rate,
fft_size=self.fft_size,
num_mels=self.num_mels,
mel_fmax=self.mel_fmax,
mel_fmin=self.mel_fmin,
)
# setup scaler
if stats_path and signal_norm:
mel_mean, mel_std, linear_mean, linear_std, _ = self.load_stats(stats_path)
self.setup_scaler(mel_mean, mel_std, linear_mean, linear_std)
self.signal_norm = True
self.max_norm = None
self.clip_norm = None
self.symmetric_norm = None
@staticmethod
def init_from_config(config: "Coqpit"):
if "audio" in config:
return AudioProcessor(**config.audio)
return AudioProcessor(**config)
### normalization ###
def normalize(self, S: np.ndarray) -> np.ndarray:
"""Normalize values into `[0, self.max_norm]` or `[-self.max_norm, self.max_norm]`.
Args:
S (np.ndarray): Spectrogram to normalize.
Raises:
RuntimeError: Mean and variance is computed from incompatible parameters.
Returns:
np.ndarray: Normalized spectrogram.
"""
# pylint: disable=no-else-return
S = S.copy()
if self.signal_norm:
# mean-var scaling
if hasattr(self, "mel_scaler"):
if S.shape[0] == self.num_mels:
return self.mel_scaler.transform(S.T).T
if S.shape[0] == self.fft_size / 2:
return self.linear_scaler.transform(S.T).T
msg = " [!] Mean-Var stats does not match the given feature dimensions."
raise RuntimeError(msg)
# range normalization
S -= self.ref_level_db # discard certain range of DB assuming it is air noise
S_norm = (S - self.min_level_db) / (-self.min_level_db)
if self.symmetric_norm:
S_norm = ((2 * self.max_norm) * S_norm) - self.max_norm
if self.clip_norm:
S_norm = np.clip(
S_norm, -self.max_norm, self.max_norm # pylint: disable=invalid-unary-operand-type
)
return S_norm
S_norm = self.max_norm * S_norm
if self.clip_norm:
S_norm = np.clip(S_norm, 0, self.max_norm)
return S_norm
return S
def denormalize(self, S: np.ndarray) -> np.ndarray:
"""Denormalize spectrogram values.
Args:
S (np.ndarray): Spectrogram to denormalize.
Raises:
RuntimeError: Mean and variance are incompatible.
Returns:
np.ndarray: Denormalized spectrogram.
"""
# pylint: disable=no-else-return
S_denorm = S.copy()
if self.signal_norm:
# mean-var scaling
if hasattr(self, "mel_scaler"):
if S_denorm.shape[0] == self.num_mels:
return self.mel_scaler.inverse_transform(S_denorm.T).T
if S_denorm.shape[0] == self.fft_size / 2:
return self.linear_scaler.inverse_transform(S_denorm.T).T
msg = " [!] Mean-Var stats does not match the given feature dimensions."
raise RuntimeError(msg)
if self.symmetric_norm:
if self.clip_norm:
S_denorm = np.clip(
S_denorm, -self.max_norm, self.max_norm # pylint: disable=invalid-unary-operand-type
)
S_denorm = ((S_denorm + self.max_norm) * -self.min_level_db / (2 * self.max_norm)) + self.min_level_db
return S_denorm + self.ref_level_db
if self.clip_norm:
S_denorm = np.clip(S_denorm, 0, self.max_norm)
S_denorm = (S_denorm * -self.min_level_db / self.max_norm) + self.min_level_db
return S_denorm + self.ref_level_db
return S_denorm
### Mean-STD scaling ###
def load_stats(self, stats_path: str) -> tuple[np.array, np.array, np.array, np.array, dict]:
"""Loading mean and variance statistics from a `npy` file.
Args:
stats_path (str): Path to the `npy` file containing
Returns:
Tuple[np.array, np.array, np.array, np.array, Dict]: loaded statistics and the config used to
compute them.
"""
stats = np.load(stats_path, allow_pickle=True).item() # pylint: disable=unexpected-keyword-arg
mel_mean = stats["mel_mean"]
mel_std = stats["mel_std"]
linear_mean = stats["linear_mean"]
linear_std = stats["linear_std"]
stats_config = stats["audio_config"]
# check all audio parameters used for computing stats
skip_parameters = ["griffin_lim_iters", "stats_path", "do_trim_silence", "ref_level_db", "power"]
for key in stats_config:
if key in skip_parameters:
continue
if key not in ["sample_rate", "trim_db"]:
assert (
stats_config[key] == self.__dict__[key]
), f" [!] Audio param {key} does not match the value used for computing mean-var stats. {stats_config[key]} vs {self.__dict__[key]}"
return mel_mean, mel_std, linear_mean, linear_std, stats_config
# pylint: disable=attribute-defined-outside-init
def setup_scaler(
self, mel_mean: np.ndarray, mel_std: np.ndarray, linear_mean: np.ndarray, linear_std: np.ndarray
) -> None:
"""Initialize scaler objects used in mean-std normalization.
Args:
mel_mean (np.ndarray): Mean for melspectrograms.
mel_std (np.ndarray): STD for melspectrograms.
linear_mean (np.ndarray): Mean for full scale spectrograms.
linear_std (np.ndarray): STD for full scale spectrograms.
"""
self.mel_scaler = StandardScaler()
self.mel_scaler.set_stats(mel_mean, mel_std)
self.linear_scaler = StandardScaler()
self.linear_scaler.set_stats(linear_mean, linear_std)
### Preemphasis ###
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.
Args:
x (np.ndarray): Audio signal.
Raises:
RuntimeError: Preemphasis coeff is set to 0.
Returns:
np.ndarray: Decorrelated audio signal.
"""
return preemphasis(x=x, coef=self.preemphasis)
def apply_inv_preemphasis(self, x: np.ndarray) -> np.ndarray:
"""Reverse pre-emphasis."""
return deemphasis(x=x, coef=self.preemphasis)
### SPECTROGRAMs ###
def spectrogram(self, y: np.ndarray) -> np.ndarray:
"""Compute a spectrogram from a waveform.
Args:
y (np.ndarray): Waveform.
Returns:
np.ndarray: Spectrogram.
"""
if self.preemphasis != 0:
y = self.apply_preemphasis(y)
D = stft(
y=y,
fft_size=self.fft_size,
hop_length=self.hop_length,
win_length=self.win_length,
pad_mode=self.stft_pad_mode,
)
S = amp_to_db(x=np.abs(D), gain=self.spec_gain, base=self.base) if self.do_amp_to_db_linear else np.abs(D)
return self.normalize(S).astype(np.float32)
def melspectrogram(self, y: np.ndarray) -> np.ndarray:
"""Compute a melspectrogram from a waveform."""
if self.preemphasis != 0:
y = self.apply_preemphasis(y)
D = stft(
y=y,
fft_size=self.fft_size,
hop_length=self.hop_length,
win_length=self.win_length,
pad_mode=self.stft_pad_mode,
)
S = spec_to_mel(spec=np.abs(D), mel_basis=self.mel_basis)
if self.do_amp_to_db_mel:
S = amp_to_db(x=S, gain=self.spec_gain, base=self.base)
return self.normalize(S).astype(np.float32)
def inv_spectrogram(self, spectrogram: np.ndarray) -> np.ndarray:
"""Convert a spectrogram to a waveform using Griffi-Lim vocoder."""
S = self.denormalize(spectrogram)
S = db_to_amp(x=S, gain=self.spec_gain, base=self.base)
# Reconstruct phase
W = self._griffin_lim(S**self.power)
return self.apply_inv_preemphasis(W) if self.preemphasis != 0 else W
def inv_melspectrogram(self, mel_spectrogram: np.ndarray) -> np.ndarray:
"""Convert a melspectrogram to a waveform using Griffi-Lim vocoder."""
D = self.denormalize(mel_spectrogram)
S = db_to_amp(x=D, gain=self.spec_gain, base=self.base)
S = mel_to_spec(mel=S, mel_basis=self.mel_basis) # Convert back to linear
W = self._griffin_lim(S**self.power)
return self.apply_inv_preemphasis(W) if self.preemphasis != 0 else W
def out_linear_to_mel(self, linear_spec: np.ndarray) -> np.ndarray:
"""Convert a full scale linear spectrogram output of a network to a melspectrogram.
Args:
linear_spec (np.ndarray): Normalized full scale linear spectrogram.
Returns:
np.ndarray: Normalized melspectrogram.
"""
S = self.denormalize(linear_spec)
S = db_to_amp(x=S, gain=self.spec_gain, base=self.base)
S = spec_to_mel(spec=np.abs(S), mel_basis=self.mel_basis)
S = amp_to_db(x=S, gain=self.spec_gain, base=self.base)
return self.normalize(S)
def _griffin_lim(self, S):
return griffin_lim(
spec=S,
num_iter=self.griffin_lim_iters,
hop_length=self.hop_length,
win_length=self.win_length,
fft_size=self.fft_size,
pad_mode=self.stft_pad_mode,
)
def compute_f0(self, x: np.ndarray) -> np.ndarray:
"""Compute pitch (f0) of a waveform using the same parameters used for computing melspectrogram.
Args:
x (np.ndarray): Waveform.
Returns:
np.ndarray: Pitch.
Examples:
>>> WAV_FILE = filename = librosa.example('vibeace')
>>> from TTS.config import BaseAudioConfig
>>> from TTS.utils.audio import AudioProcessor
>>> conf = BaseAudioConfig(pitch_fmax=640, pitch_fmin=1)
>>> ap = AudioProcessor(**conf)
>>> wav = ap.load_wav(WAV_FILE, sr=ap.sample_rate)[:5 * ap.sample_rate]
>>> pitch = ap.compute_f0(wav)
"""
# align F0 length to the spectrogram length
if len(x) % self.hop_length == 0:
x = np.pad(x, (0, self.hop_length // 2), mode=self.stft_pad_mode)
return compute_f0(
x=x,
pitch_fmax=self.pitch_fmax,
pitch_fmin=self.pitch_fmin,
hop_length=self.hop_length,
win_length=self.win_length,
sample_rate=self.sample_rate,
stft_pad_mode=self.stft_pad_mode,
center=True,
)
### Audio Processing ###
def find_endpoint(self, wav: np.ndarray, min_silence_sec=0.8) -> int:
"""Find the last point without silence at the end of a audio signal.
Args:
wav (np.ndarray): Audio signal.
threshold_db (int, optional): Silence threshold in decibels. Defaults to -40.
min_silence_sec (float, optional): Ignore silences that are shorter then this in secs. Defaults to 0.8.
Returns:
int: Last point without silence.
"""
return find_endpoint(
wav=wav,
trim_db=self.trim_db,
sample_rate=self.sample_rate,
min_silence_sec=min_silence_sec,
gain=self.spec_gain,
base=self.base,
)
def trim_silence(self, wav):
"""Trim silent parts with a threshold and 0.01 sec margin."""
return trim_silence(
wav=wav,
sample_rate=self.sample_rate,
trim_db=self.trim_db,
win_length=self.win_length,
hop_length=self.hop_length,
)
@staticmethod
def sound_norm(x: np.ndarray) -> np.ndarray:
"""Normalize the volume of an audio signal.
Args:
x (np.ndarray): Raw waveform.
Returns:
np.ndarray: Volume normalized waveform.
"""
return volume_norm(x=x)
### save and load ###
def load_wav(self, filename: str, sr: Optional[int] = None) -> np.ndarray:
"""Read a wav file using Librosa and optionally resample, silence trim, volume normalize.
Resampling slows down loading the file significantly. Therefore it is recommended to resample the file before.
Args:
filename (str): Path to the wav file.
sr (int, optional): Sampling rate for resampling. Defaults to None.
Returns:
np.ndarray: Loaded waveform.
"""
if sr is not None:
x = load_wav(filename=filename, sample_rate=sr, resample=True)
else:
x = load_wav(filename=filename, sample_rate=self.sample_rate, resample=self.resample)
if self.do_trim_silence:
try:
x = self.trim_silence(x)
except ValueError:
logger.exception("File cannot be trimmed for silence - %s", filename)
if self.do_sound_norm:
x = self.sound_norm(x)
if self.do_rms_norm:
x = rms_volume_norm(x=x, db_level=self.db_level)
return x
def save_wav(self, wav: np.ndarray, path: str, sr: Optional[int] = None, pipe_out=None) -> None:
"""Save a waveform to a file using Scipy.
Args:
wav (np.ndarray): Waveform to save.
path (str): Path to a output file.
sr (int, optional): Sampling rate used for saving to the file. Defaults to None.
pipe_out (BytesIO, optional): Flag to stdout the generated TTS wav file for shell pipe.
"""
save_wav(
wav=wav,
path=path,
sample_rate=sr if sr else self.sample_rate,
pipe_out=pipe_out,
do_rms_norm=self.do_rms_norm,
db_level=self.db_level,
)
def get_duration(self, filename: str) -> float:
"""Get the duration of a wav file using Librosa.
Args:
filename (str): Path to the wav file.
"""
return librosa.get_duration(filename=filename)