from io import BytesIO
from typing import Tuple
import librosa
import numpy as np
import scipy
import soundfile as sf
from librosa import magphase, pyin
# For using kwargs
# pylint: disable=unused-argument
def build_mel_basis(
*,
sample_rate: int = None,
fft_size: int = None,
num_mels: int = None,
mel_fmax: int = None,
mel_fmin: int = None,
**kwargs,
) -> np.ndarray:
"""Build melspectrogram basis.
Returns:
np.ndarray: melspectrogram basis.
"""
if mel_fmax is not None:
assert mel_fmax <= sample_rate // 2
assert mel_fmax - mel_fmin > 0
return librosa.filters.mel(sr=sample_rate, n_fft=fft_size, n_mels=num_mels, fmin=mel_fmin, fmax=mel_fmax)
def millisec_to_length(
*, frame_length_ms: int = None, frame_shift_ms: int = None, sample_rate: int = None, **kwargs
) -> Tuple[int, int]:
"""Compute hop and window length from milliseconds.
Returns:
Tuple[int, int]: hop length and window length for STFT.
"""
factor = frame_length_ms / frame_shift_ms
assert (factor).is_integer(), " [!] frame_shift_ms should divide frame_length_ms"
win_length = int(frame_length_ms / 1000.0 * sample_rate)
hop_length = int(win_length / float(factor))
return win_length, hop_length
def _log(x, base):
if base == 10:
return np.log10(x)
return np.log(x)
def _exp(x, base):
if base == 10:
return np.power(10, x)
return np.exp(x)
def amp_to_db(*, x: np.ndarray = None, gain: float = 1, base: int = 10, **kwargs) -> np.ndarray:
"""Convert amplitude values to decibels.
Args:
x (np.ndarray): Amplitude spectrogram.
gain (float): Gain factor. Defaults to 1.
base (int): Logarithm base. Defaults to 10.
Returns:
np.ndarray: Decibels spectrogram.
"""
assert (x < 0).sum() == 0, " [!] Input values must be non-negative."
return gain * _log(np.maximum(1e-8, x), base)
# pylint: disable=no-self-use
def db_to_amp(*, x: np.ndarray = None, gain: float = 1, base: int = 10, **kwargs) -> np.ndarray:
"""Convert decibels spectrogram to amplitude spectrogram.
Args:
x (np.ndarray): Decibels spectrogram.
gain (float): Gain factor. Defaults to 1.
base (int): Logarithm base. Defaults to 10.
Returns:
np.ndarray: Amplitude spectrogram.
"""
return _exp(x / gain, base)
def preemphasis(*, x: np.ndarray, coef: float = 0.97, **kwargs) -> 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.
"""
if coef == 0:
raise RuntimeError(" [!] Preemphasis is set 0.0.")
return scipy.signal.lfilter([1, -coef], [1], x)
def deemphasis(*, x: np.ndarray = None, coef: float = 0.97, **kwargs) -> np.ndarray:
"""Reverse pre-emphasis."""
if coef == 0:
raise RuntimeError(" [!] Preemphasis is set 0.0.")
return scipy.signal.lfilter([1], [1, -coef], x)
def spec_to_mel(*, spec: np.ndarray, mel_basis: np.ndarray = None, **kwargs) -> np.ndarray:
"""Convert a full scale linear spectrogram output of a network to a melspectrogram.
Args:
spec (np.ndarray): Normalized full scale linear spectrogram.
Shapes:
- spec: :math:`[C, T]`
Returns:
np.ndarray: Normalized melspectrogram.
"""
return np.dot(mel_basis, spec)
def mel_to_spec(*, mel: np.ndarray = None, mel_basis: np.ndarray = None, **kwargs) -> np.ndarray:
"""Convert a melspectrogram to full scale spectrogram."""
assert (mel < 0).sum() == 0, " [!] Input values must be non-negative."
inv_mel_basis = np.linalg.pinv(mel_basis)
return np.maximum(1e-10, np.dot(inv_mel_basis, mel))
def wav_to_spec(*, wav: np.ndarray = None, **kwargs) -> np.ndarray:
"""Compute a spectrogram from a waveform.
Args:
wav (np.ndarray): Waveform. Shape :math:`[T_wav,]`
Returns:
np.ndarray: Spectrogram. Shape :math:`[C, T_spec]`. :math:`T_spec == T_wav / hop_length`
"""
D = stft(y=wav, **kwargs)
S = np.abs(D)
return S.astype(np.float32)
def wav_to_mel(*, wav: np.ndarray = None, mel_basis=None, **kwargs) -> np.ndarray:
"""Compute a melspectrogram from a waveform."""
D = stft(y=wav, **kwargs)
S = spec_to_mel(spec=np.abs(D), mel_basis=mel_basis, **kwargs)
return S.astype(np.float32)
def spec_to_wav(*, spec: np.ndarray, power: float = 1.5, **kwargs) -> np.ndarray:
"""Convert a spectrogram to a waveform using Griffi-Lim vocoder."""
S = spec.copy()
return griffin_lim(spec=S**power, **kwargs)
def mel_to_wav(*, mel: np.ndarray = None, power: float = 1.5, **kwargs) -> np.ndarray:
"""Convert a melspectrogram to a waveform using Griffi-Lim vocoder."""
S = mel.copy()
S = mel_to_spec(mel=S, mel_basis=kwargs["mel_basis"]) # Convert back to linear
return griffin_lim(spec=S**power, **kwargs)
### STFT and ISTFT ###
def stft(
*,
y: np.ndarray = None,
fft_size: int = None,
hop_length: int = None,
win_length: int = None,
pad_mode: str = "reflect",
window: str = "hann",
center: bool = True,
**kwargs,
) -> np.ndarray:
"""Librosa STFT wrapper.
Check http://librosa.org/doc/main/generated/librosa.stft.html argument details.
Returns:
np.ndarray: Complex number array.
"""
return librosa.stft(
y=y,
n_fft=fft_size,
hop_length=hop_length,
win_length=win_length,
pad_mode=pad_mode,
window=window,
center=center,
)
def istft(
*,
y: np.ndarray = None,
hop_length: int = None,
win_length: int = None,
window: str = "hann",
center: bool = True,
**kwargs,
) -> np.ndarray:
"""Librosa iSTFT wrapper.
Check http://librosa.org/doc/main/generated/librosa.istft.html argument details.
Returns:
np.ndarray: Complex number array.
"""
return librosa.istft(y, hop_length=hop_length, win_length=win_length, center=center, window=window)
def griffin_lim(*, spec: np.ndarray = None, num_iter=60, **kwargs) -> np.ndarray:
angles = np.exp(2j * np.pi * np.random.rand(*spec.shape))
S_complex = np.abs(spec).astype(complex)
y = istft(y=S_complex * angles, **kwargs)
if not np.isfinite(y).all():
print(" [!] Waveform is not finite everywhere. Skipping the GL.")
return np.array([0.0])
for _ in range(num_iter):
angles = np.exp(1j * np.angle(stft(y=y, **kwargs)))
y = istft(y=S_complex * angles, **kwargs)
return y
def compute_stft_paddings(
*, x: np.ndarray = None, hop_length: int = None, pad_two_sides: bool = False, **kwargs
) -> Tuple[int, int]:
"""Compute paddings used by Librosa's STFT. Compute right padding (final frame) or both sides padding
(first and final frames)"""
pad = (x.shape[0] // hop_length + 1) * hop_length - x.shape[0]
if not pad_two_sides:
return 0, pad
return pad // 2, pad // 2 + pad % 2
def compute_f0(
*,
x: np.ndarray = None,
pitch_fmax: float = None,
pitch_fmin: float = None,
hop_length: int = None,
win_length: int = None,
sample_rate: int = None,
stft_pad_mode: str = "reflect",
center: bool = True,
**kwargs,
) -> np.ndarray:
"""Compute pitch (f0) of a waveform using the same parameters used for computing melspectrogram.
Args:
x (np.ndarray): Waveform. Shape :math:`[T_wav,]`
pitch_fmax (float): Pitch max value.
pitch_fmin (float): Pitch min value.
hop_length (int): Number of frames between STFT columns.
win_length (int): STFT window length.
sample_rate (int): Audio sampling rate.
stft_pad_mode (str): Padding mode for STFT.
center (bool): Centered padding.
Returns:
np.ndarray: Pitch. Shape :math:`[T_pitch,]`. :math:`T_pitch == T_wav / hop_length`
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)
"""
assert pitch_fmax is not None, " [!] Set `pitch_fmax` before caling `compute_f0`."
assert pitch_fmin is not None, " [!] Set `pitch_fmin` before caling `compute_f0`."
f0, voiced_mask, _ = pyin(
y=x.astype(np.double),
fmin=pitch_fmin,
fmax=pitch_fmax,
sr=sample_rate,
frame_length=win_length,
win_length=win_length // 2,
hop_length=hop_length,
pad_mode=stft_pad_mode,
center=center,
n_thresholds=100,
beta_parameters=(2, 18),
boltzmann_parameter=2,
resolution=0.1,
max_transition_rate=35.92,
switch_prob=0.01,
no_trough_prob=0.01,
)
f0[~voiced_mask] = 0.0
return f0
def compute_energy(y: np.ndarray, **kwargs) -> np.ndarray:
"""Compute energy of a waveform using the same parameters used for computing melspectrogram.
Args:
x (np.ndarray): Waveform. Shape :math:`[T_wav,]`
Returns:
np.ndarray: energy. Shape :math:`[T_energy,]`. :math:`T_energy == T_wav / hop_length`
Examples:
>>> WAV_FILE = filename = librosa.example('vibeace')
>>> from TTS.config import BaseAudioConfig
>>> from TTS.utils.audio import AudioProcessor
>>> conf = BaseAudioConfig()
>>> ap = AudioProcessor(**conf)
>>> wav = ap.load_wav(WAV_FILE, sr=ap.sample_rate)[:5 * ap.sample_rate]
>>> energy = ap.compute_energy(wav)
"""
x = stft(y=y, **kwargs)
mag, _ = magphase(x)
energy = np.sqrt(np.sum(mag**2, axis=0))
return energy
### Audio Processing ###
def find_endpoint(
*,
wav: np.ndarray = None,
trim_db: float = -40,
sample_rate: int = None,
min_silence_sec=0.8,
gain: float = None,
base: int = None,
**kwargs,
) -> 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.
gian (float, optional): Gain to be used to convert trim_db to trim_amp. Defaults to None.
base (int, optional): Base of the logarithm used to convert trim_db to trim_amp. Defaults to 10.
Returns:
int: Last point without silence.
"""
window_length = int(sample_rate * min_silence_sec)
hop_length = int(window_length / 4)
threshold = db_to_amp(x=-trim_db, gain=gain, base=base)
for x in range(hop_length, len(wav) - window_length, hop_length):
if np.max(wav[x : x + window_length]) < threshold:
return x + hop_length
return len(wav)
def trim_silence(
*,
wav: np.ndarray = None,
sample_rate: int = None,
trim_db: float = None,
win_length: int = None,
hop_length: int = None,
**kwargs,
) -> np.ndarray:
"""Trim silent parts with a threshold and 0.01 sec margin"""
margin = int(sample_rate * 0.01)
wav = wav[margin:-margin]
return librosa.effects.trim(wav, top_db=trim_db, frame_length=win_length, hop_length=hop_length)[0]
def volume_norm(*, x: np.ndarray = None, coef: float = 0.95, **kwargs) -> np.ndarray:
"""Normalize the volume of an audio signal.
Args:
x (np.ndarray): Raw waveform.
coef (float): Coefficient to rescale the maximum value. Defaults to 0.95.
Returns:
np.ndarray: Volume normalized waveform.
"""
return x / abs(x).max() * coef
def rms_norm(*, wav: np.ndarray = None, db_level: float = -27.0, **kwargs) -> np.ndarray:
r = 10 ** (db_level / 20)
a = np.sqrt((len(wav) * (r**2)) / np.sum(wav**2))
return wav * a
def rms_volume_norm(*, x: np.ndarray, db_level: float = -27.0, **kwargs) -> np.ndarray:
"""Normalize the volume based on RMS of the signal.
Args:
x (np.ndarray): Raw waveform.
db_level (float): Target dB level in RMS. Defaults to -27.0.
Returns:
np.ndarray: RMS normalized waveform.
"""
assert -99 <= db_level <= 0, " [!] db_level should be between -99 and 0"
wav = rms_norm(wav=x, db_level=db_level)
return wav
def load_wav(*, filename: str, sample_rate: int = None, resample: bool = False, **kwargs) -> 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.
resample (bool, optional): Resample the audio file when loading. Slows down the I/O time. Defaults to False.
Returns:
np.ndarray: Loaded waveform.
"""
if resample:
# loading with resampling. It is significantly slower.
x, _ = librosa.load(filename, sr=sample_rate)
else:
# SF is faster than librosa for loading files
x, _ = sf.read(filename)
return x
def save_wav(*, wav: np.ndarray, path: str, sample_rate: int = None, pipe_out=None, **kwargs) -> None:
"""Save float waveform to a file using Scipy.
Args:
wav (np.ndarray): Waveform with float values in range [-1, 1] 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.
"""
wav_norm = wav * (32767 / max(0.01, np.max(np.abs(wav))))
wav_norm = wav_norm.astype(np.int16)
if pipe_out:
wav_buffer = BytesIO()
scipy.io.wavfile.write(wav_buffer, sample_rate, wav_norm)
wav_buffer.seek(0)
pipe_out.buffer.write(wav_buffer.read())
scipy.io.wavfile.write(path, sample_rate, wav_norm)
def mulaw_encode(*, wav: np.ndarray, mulaw_qc: int, **kwargs) -> np.ndarray:
mu = 2**mulaw_qc - 1
signal = np.sign(wav) * np.log(1 + mu * np.abs(wav)) / np.log(1.0 + mu)
signal = (signal + 1) / 2 * mu + 0.5
return np.floor(
signal,
)
def mulaw_decode(*, wav, mulaw_qc: int, **kwargs) -> np.ndarray:
"""Recovers waveform from quantized values."""
mu = 2**mulaw_qc - 1
x = np.sign(wav) / mu * ((1 + mu) ** np.abs(wav) - 1)
return x
def encode_16bits(*, x: np.ndarray, **kwargs) -> np.ndarray:
return np.clip(x * 2**15, -(2**15), 2**15 - 1).astype(np.int16)
def quantize(*, x: np.ndarray, quantize_bits: int, **kwargs) -> 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]`.
quantize_bits (int): Number of quantization bits.
Returns:
np.ndarray: Quantized waveform.
"""
return (x + 1.0) * (2**quantize_bits - 1) / 2
def dequantize(*, x, quantize_bits, **kwargs) -> np.ndarray:
"""Dequantize a waveform from the given number of bits."""
return 2 * x / (2**quantize_bits - 1) - 1