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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import glob
import os
import os.path as osp
import sys
from multiprocessing import Pool
import mmengine
import numpy as np
from scipy.io import wavfile
try:
import librosa
import lws
except ImportError:
print('Please import librosa, lws first.')
sys.path.append('..')
SILENCE_THRESHOLD = 2
FMIN = 125
FMAX = 7600
FRAME_SHIFT_MS = None
MIN_LEVEL_DB = -100
REF_LEVEL_DB = 20
RESCALING = True
RESCALING_MAX = 0.999
ALLOW_CLIPPING_IN_NORMALIZATION = True
LOG_SCALE_MIN = -32.23619130191664
NORM_AUDIO = True
class AudioTools:
"""All methods related to audio feature extraction. Code Reference:
<https://github.com/r9y9/deepvoice3_pytorch>`_,
<https://pypi.org/project/lws/1.2.6/>`_.
Args:
frame_rate (int): The frame rate per second of the video.
Defaults to 30.
sample_rate (int): The sample rate for audio sampling.
Defaults to 16000.
num_mels (int): Number of channels of the melspectrogram.
Defaults to 80.
fft_size (int): fft_size / sample_rate is window size.
Defaults to 1280.
hop_size (int): hop_size / sample_rate is step size.
Defaults to 320.
"""
def __init__(self,
frame_rate=30,
sample_rate=16000,
num_mels=80,
fft_size=1280,
hop_size=320,
spectrogram_type='lws'):
self.frame_rate = frame_rate
self.sample_rate = sample_rate
self.silence_threshold = SILENCE_THRESHOLD
self.num_mels = num_mels
self.fmin = FMIN
self.fmax = FMAX
self.fft_size = fft_size
self.hop_size = hop_size
self.frame_shift_ms = FRAME_SHIFT_MS
self.min_level_db = MIN_LEVEL_DB
self.ref_level_db = REF_LEVEL_DB
self.rescaling = RESCALING
self.rescaling_max = RESCALING_MAX
self.allow_clipping_in_normalization = ALLOW_CLIPPING_IN_NORMALIZATION
self.log_scale_min = LOG_SCALE_MIN
self.norm_audio = NORM_AUDIO
self.spectrogram_type = spectrogram_type
assert spectrogram_type in ['lws', 'librosa']
def load_wav(self, path):
"""Load an audio file into numpy array."""
return librosa.core.load(path, sr=self.sample_rate)[0]
@staticmethod
def audio_normalize(samples, desired_rms=0.1, eps=1e-4):
"""RMS normalize the audio data."""
rms = np.maximum(eps, np.sqrt(np.mean(samples**2)))
samples = samples * (desired_rms / rms)
return samples
def generate_spectrogram_magphase(self, audio, with_phase=False):
"""Separate a complex-valued spectrogram D into its magnitude (S)
and phase (P) components, so that D = S * P.
Args:
audio (np.ndarray): The input audio signal.
with_phase (bool): Determines whether to output the
phase components. Default: False.
Returns:
np.ndarray: magnitude and phase component of the complex-valued
spectrogram.
"""
spectro = librosa.core.stft(
audio,
hop_length=self.get_hop_size(),
n_fft=self.fft_size,
center=True)
spectro_mag, spectro_phase = librosa.core.magphase(spectro)
spectro_mag = np.expand_dims(spectro_mag, axis=0)
if with_phase:
spectro_phase = np.expand_dims(np.angle(spectro_phase), axis=0)
return spectro_mag, spectro_phase
return spectro_mag
def save_wav(self, wav, path):
"""Save the wav to disk."""
# 32767 = (2 ^ 15 - 1) maximum of int16
wav *= 32767 / max(0.01, np.max(np.abs(wav)))
wavfile.write(path, self.sample_rate, wav.astype(np.int16))
def trim(self, quantized):
"""Trim the audio wavfile."""
start, end = self.start_and_end_indices(quantized,
self.silence_threshold)
return quantized[start:end]
def adjust_time_resolution(self, quantized, mel):
"""Adjust time resolution by repeating features.
Args:
quantized (np.ndarray): (T,)
mel (np.ndarray): (N, D)
Returns:
tuple: Tuple of (T,) and (T, D)
"""
assert quantized.ndim == 1
assert mel.ndim == 2
upsample_factor = quantized.size // mel.shape[0]
mel = np.repeat(mel, upsample_factor, axis=0)
n_pad = quantized.size - mel.shape[0]
if n_pad != 0:
assert n_pad > 0
mel = np.pad(
mel, [(0, n_pad), (0, 0)], mode='constant', constant_values=0)
# trim
start, end = self.start_and_end_indices(quantized,
self.silence_threshold)
return quantized[start:end], mel[start:end, :]
@staticmethod
def start_and_end_indices(quantized, silence_threshold=2):
"""Trim the audio file when reaches the silence threshold."""
for start in range(quantized.size):
if abs(quantized[start] - 127) > silence_threshold:
break
for end in range(quantized.size - 1, 1, -1):
if abs(quantized[end] - 127) > silence_threshold:
break
assert abs(quantized[start] - 127) > silence_threshold
assert abs(quantized[end] - 127) > silence_threshold
return start, end
def melspectrogram(self, y):
"""Generate the melspectrogram."""
D = self._lws_processor().stft(y).T
S = self._amp_to_db(self._linear_to_mel(np.abs(D))) - self.ref_level_db
if not self.allow_clipping_in_normalization:
assert S.max() <= 0 and S.min() - self.min_level_db >= 0
return self._normalize(S)
def get_hop_size(self):
"""Calculate the hop size."""
hop_size = self.hop_size
if hop_size is None:
assert self.frame_shift_ms is not None
hop_size = int(self.frame_shift_ms / 1000 * self.sample_rate)
return hop_size
def _lws_processor(self):
"""Perform local weighted sum.
Please refer to <https://pypi.org/project/lws/1.2.6/>`_.
"""
return lws.lws(self.fft_size, self.get_hop_size(), mode='speech')
@staticmethod
def lws_num_frames(length, fsize, fshift):
"""Compute number of time frames of lws spectrogram.
Please refer to <https://pypi.org/project/lws/1.2.6/>`_.
"""
pad = (fsize - fshift)
if length % fshift == 0:
M = (length + pad * 2 - fsize) // fshift + 1
else:
M = (length + pad * 2 - fsize) // fshift + 2
return M
def lws_pad_lr(self, x, fsize, fshift):
"""Compute left and right padding lws internally uses.
Please refer to <https://pypi.org/project/lws/1.2.6/>`_.
"""
M = self.lws_num_frames(len(x), fsize, fshift)
pad = (fsize - fshift)
T = len(x) + 2 * pad
r = (M - 1) * fshift + fsize - T
return pad, pad + r
def _linear_to_mel(self, spectrogram):
"""Warp linear scale spectrograms to the mel scale.
Please refer to <https://github.com/r9y9/deepvoice3_pytorch>`_
"""
global _mel_basis
_mel_basis = self._build_mel_basis()
return np.dot(_mel_basis, spectrogram)
def _build_mel_basis(self):
"""Build mel filters.
Please refer to <https://github.com/r9y9/deepvoice3_pytorch>`_
"""
assert self.fmax <= self.sample_rate // 2
return librosa.filters.mel(
self.sample_rate,
self.fft_size,
fmin=self.fmin,
fmax=self.fmax,
n_mels=self.num_mels)
def _amp_to_db(self, x):
min_level = np.exp(self.min_level_db / 20 * np.log(10))
return 20 * np.log10(np.maximum(min_level, x))
@staticmethod
def _db_to_amp(x):
return np.power(10.0, x * 0.05)
def _normalize(self, S):
return np.clip((S - self.min_level_db) / -self.min_level_db, 0, 1)
def _denormalize(self, S):
return (np.clip(S, 0, 1) * -self.min_level_db) + self.min_level_db
def read_audio(self, audio_path):
wav = self.load_wav(audio_path)
if self.norm_audio:
wav = self.audio_normalize(wav)
else:
wav = wav / np.abs(wav).max()
return wav
def audio_to_spectrogram(self, wav):
if self.spectrogram_type == 'lws':
spectrogram = self.melspectrogram(wav).astype(np.float32).T
elif self.spectrogram_type == 'librosa':
spectrogram = self.generate_spectrogram_magphase(wav)
return spectrogram
def extract_audio_feature(wav_path, audio_tools, mel_out_dir):
file_name, _ = osp.splitext(osp.basename(wav_path))
# Write the spectrograms to disk:
mel_filename = os.path.join(mel_out_dir, file_name + '.npy')
if not os.path.exists(mel_filename):
try:
wav = audio_tools.read_audio(wav_path)
spectrogram = audio_tools.audio_to_spectrogram(wav)
np.save(
mel_filename,
spectrogram.astype(np.float32),
allow_pickle=False)
except BaseException:
print(f'Read audio [{wav_path}] failed.')
if __name__ == '__main__':
audio_tools = AudioTools(
fft_size=512, hop_size=256) # window_size:32ms hop_size:16ms
parser = argparse.ArgumentParser()
parser.add_argument('audio_home_path', type=str)
parser.add_argument('spectrogram_save_path', type=str)
parser.add_argument('--level', type=int, default=1)
parser.add_argument('--ext', default='m4a')
parser.add_argument('--num-workers', type=int, default=4)
parser.add_argument('--part', type=str, default='1/1')
args = parser.parse_args()
mmengine.mkdir_or_exist(args.spectrogram_save_path)
files = glob.glob(args.audio_home_path + '/*' * args.level + '.' +
args.ext)
print(f'found {len(files)} files.')
files = sorted(files)
if args.part is not None:
[this_part, num_parts] = [int(i) for i in args.part.split('/')]
part_len = len(files) // num_parts
p = Pool(args.num_workers)
for file in files[part_len * (this_part - 1):(
part_len * this_part) if this_part != num_parts else len(files)]:
p.apply_async(
extract_audio_feature,
args=(file, audio_tools, args.spectrogram_save_path))
p.close()
p.join()
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