File size: 15,546 Bytes
9b2107c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
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