maskgct / processors /data_augment.py
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# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import random
import os
import json
import numpy as np
import parselmouth
import torch
import torchaudio
from tqdm import tqdm
from audiomentations import TimeStretch
from pedalboard import (
Pedalboard,
HighShelfFilter,
LowShelfFilter,
PeakFilter,
PitchShift,
)
from utils.util import has_existed
PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT = 0.0
PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT = 1.0
PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT = 1.0
PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT = 1.0
PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT = 1.0
def wav_to_Sound(wav, sr: int) -> parselmouth.Sound:
"""Convert a waveform to a parselmouth.Sound object
Args:
wav (np.ndarray/torch.Tensor): waveform of shape (n_channels, n_samples)
sr (int, optional): sampling rate.
Returns:
parselmouth.Sound: a parselmouth.Sound object
"""
assert wav.shape == (1, len(wav[0])), "wav must be of shape (1, n_samples)"
sound = None
if isinstance(wav, np.ndarray):
sound = parselmouth.Sound(wav[0], sampling_frequency=sr)
elif isinstance(wav, torch.Tensor):
sound = parselmouth.Sound(wav[0].numpy(), sampling_frequency=sr)
assert sound is not None, "wav must be either np.ndarray or torch.Tensor"
return sound
def get_pitch_median(wav, sr: int):
"""Get the median pitch of a waveform
Args:
wav (np.ndarray/torch.Tensor): waveform of shape (n_channels, n_samples)
sr (int, optional): sampling rate.
Returns:
parselmouth.Pitch, float: a parselmouth.Pitch object and the median pitch
"""
if not isinstance(wav, parselmouth.Sound):
sound = wav_to_Sound(wav, sr)
else:
sound = wav
pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT
# To Pitch: Time step(s)(standard value: 0.0), Pitch floor (Hz)(standard value: 75), Pitch ceiling (Hz)(standard value: 600.0)
pitch = parselmouth.praat.call(sound, "To Pitch", 0.8 / 75, 75, 600)
# Get quantile: From time (s), To time (s), Quantile(0.5 is then the 50% quantile, i.e., the median), Units (Hertz or Bark)
pitch_median = parselmouth.praat.call(pitch, "Get quantile", 0.0, 0.0, 0.5, "Hertz")
return pitch, pitch_median
def change_gender(
sound,
pitch=None,
formant_shift_ratio: float = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT,
new_pitch_median: float = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT,
pitch_range_ratio: float = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT,
duration_factor: float = PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT,
) -> parselmouth.Sound:
"""Invoke change gender function in praat
Args:
sound (parselmouth.Sound): a parselmouth.Sound object
pitch (parselmouth.Pitch, optional): a parselmouth.Pitch object. Defaults to None.
formant_shift_ratio (float, optional): formant shift ratio. A value of 1.0 means no change. Greater than 1.0 means higher pitch. Less than 1.0 means lower pitch.
new_pitch_median (float, optional): new pitch median.
pitch_range_ratio (float, optional): pitch range ratio. A value of 1.0 means no change. Greater than 1.0 means higher pitch range. Less than 1.0 means lower pitch range.
duration_factor (float, optional): duration factor. A value of 1.0 means no change. Greater than 1.0 means longer duration. Less than 1.0 means shorter duration.
Returns:
parselmouth.Sound: a parselmouth.Sound object
"""
if pitch is None:
new_sound = parselmouth.praat.call(
sound,
"Change gender",
75,
600,
formant_shift_ratio,
new_pitch_median,
pitch_range_ratio,
duration_factor,
)
else:
new_sound = parselmouth.praat.call(
(sound, pitch),
"Change gender",
formant_shift_ratio,
new_pitch_median,
pitch_range_ratio,
duration_factor,
)
return new_sound
def apply_formant_and_pitch_shift(
sound: parselmouth.Sound,
formant_shift_ratio: float = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT,
pitch_shift_ratio: float = PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT,
pitch_range_ratio: float = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT,
duration_factor: float = PRAAT_CHANGEGENDER_DURATIONFACTOR_DEFAULT,
) -> parselmouth.Sound:
"""use Praat "Changer gender" command to manipulate pitch and formant
"Change gender": Praat -> Sound Object -> Convert -> Change gender
refer to Help of Praat for more details
# https://github.com/YannickJadoul/Parselmouth/issues/25#issuecomment-608632887 might help
"""
pitch = None
new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT
if pitch_shift_ratio != 1.0:
pitch, pitch_median = get_pitch_median(sound, sound.sampling_frequency)
new_pitch_median = pitch_median * pitch_shift_ratio
# refer to https://github.com/praat/praat/issues/1926#issuecomment-974909408
pitch_minimum = parselmouth.praat.call(
pitch, "Get minimum", 0.0, 0.0, "Hertz", "Parabolic"
)
new_median = pitch_median * pitch_shift_ratio
scaled_minimum = pitch_minimum * pitch_shift_ratio
result_minimum = new_median + (scaled_minimum - new_median) * pitch_range_ratio
if result_minimum < 0:
new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT
pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT
if math.isnan(new_pitch_median):
new_pitch_median = PRAAT_CHANGEGENDER_PITCHMEDIAN_DEFAULT
pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT
new_sound = change_gender(
sound,
pitch,
formant_shift_ratio,
new_pitch_median,
pitch_range_ratio,
duration_factor,
)
return new_sound
# Function used in EQ
def pedalboard_equalizer(wav: np.ndarray, sr: int) -> np.ndarray:
"""Use pedalboard to do equalizer"""
board = Pedalboard()
cutoff_low_freq = 60
cutoff_high_freq = 10000
q_min = 2
q_max = 5
random_all_freq = True
num_filters = 10
if random_all_freq:
key_freqs = [random.uniform(1, 12000) for _ in range(num_filters)]
else:
key_freqs = [
power_ratio(float(z) / (num_filters - 1), cutoff_low_freq, cutoff_high_freq)
for z in range(num_filters)
]
q_values = [
power_ratio(random.uniform(0, 1), q_min, q_max) for _ in range(num_filters)
]
gains = [random.uniform(-12, 12) for _ in range(num_filters)]
# low-shelving filter
board.append(
LowShelfFilter(
cutoff_frequency_hz=key_freqs[0], gain_db=gains[0], q=q_values[0]
)
)
# peaking filters
for i in range(1, 9):
board.append(
PeakFilter(
cutoff_frequency_hz=key_freqs[i], gain_db=gains[i], q=q_values[i]
)
)
# high-shelving filter
board.append(
HighShelfFilter(
cutoff_frequency_hz=key_freqs[9], gain_db=gains[9], q=q_values[9]
)
)
# Apply the pedalboard to the audio
processed_audio = board(wav, sr)
return processed_audio
def power_ratio(r: float, a: float, b: float):
return a * math.pow((b / a), r)
def audiomentations_time_stretch(wav: np.ndarray, sr: int) -> np.ndarray:
"""Use audiomentations to do time stretch"""
transform = TimeStretch(
min_rate=0.8, max_rate=1.25, leave_length_unchanged=False, p=1.0
)
augmented_wav = transform(wav, sample_rate=sr)
return augmented_wav
def formant_and_pitch_shift(
sound: parselmouth.Sound, fs: bool, ps: bool
) -> parselmouth.Sound:
""" """
formant_shift_ratio = PRAAT_CHANGEGENDER_FORMANTSHIFTRATIO_DEFAULT
pitch_shift_ratio = PRAAT_CHANGEGENDER_PITCHSHIFTRATIO_DEFAULT
pitch_range_ratio = PRAAT_CHANGEGENDER_PITCHRANGERATIO_DEFAULT
assert fs != ps, "fs, ps are mutually exclusive"
if fs:
formant_shift_ratio = random.uniform(1.0, 1.4)
use_reciprocal = random.uniform(-1, 1) > 0
if use_reciprocal:
formant_shift_ratio = 1.0 / formant_shift_ratio
# only use praat to change formant
new_sound = apply_formant_and_pitch_shift(
sound,
formant_shift_ratio=formant_shift_ratio,
)
return new_sound
if ps:
board = Pedalboard()
board.append(PitchShift(random.uniform(-12, 12)))
wav_numpy = sound.values
wav_numpy = board(wav_numpy, sound.sampling_frequency)
# use pedalboard to change pitch
new_sound = parselmouth.Sound(
wav_numpy, sampling_frequency=sound.sampling_frequency
)
return new_sound
def wav_manipulation(
wav: torch.Tensor,
sr: int,
aug_type: str = "None",
formant_shift: bool = False,
pitch_shift: bool = False,
time_stretch: bool = False,
equalizer: bool = False,
) -> torch.Tensor:
assert aug_type == "None" or aug_type in [
"formant_shift",
"pitch_shift",
"time_stretch",
"equalizer",
], "aug_type must be one of formant_shift, pitch_shift, time_stretch, equalizer"
assert aug_type == "None" or (
formant_shift == False
and pitch_shift == False
and time_stretch == False
and equalizer == False
), "if aug_type is specified, other argument must be False"
if aug_type != "None":
if aug_type == "formant_shift":
formant_shift = True
if aug_type == "pitch_shift":
pitch_shift = True
if aug_type == "equalizer":
equalizer = True
if aug_type == "time_stretch":
time_stretch = True
wav_numpy = wav.numpy()
if equalizer:
wav_numpy = pedalboard_equalizer(wav_numpy, sr)
if time_stretch:
wav_numpy = audiomentations_time_stretch(wav_numpy, sr)
sound = wav_to_Sound(wav_numpy, sr)
if formant_shift or pitch_shift:
sound = formant_and_pitch_shift(sound, formant_shift, pitch_shift)
wav = torch.from_numpy(sound.values).float()
# shape (1, n_samples)
return wav
def augment_dataset(cfg, dataset) -> list:
"""Augment dataset with formant_shift, pitch_shift, time_stretch, equalizer
Args:
cfg (dict): configuration
dataset (str): dataset name
Returns:
list: augmented dataset names
"""
# load metadata
dataset_path = os.path.join(cfg.preprocess.processed_dir, dataset)
split = ["train", "test"] if "eval" not in dataset else ["test"]
augment_datasets = []
aug_types = [
"formant_shift" if cfg.preprocess.use_formant_shift else None,
"pitch_shift" if cfg.preprocess.use_pitch_shift else None,
"time_stretch" if cfg.preprocess.use_time_stretch else None,
"equalizer" if cfg.preprocess.use_equalizer else None,
]
aug_types = filter(None, aug_types)
for aug_type in aug_types:
print("Augmenting {} with {}...".format(dataset, aug_type))
new_dataset = dataset + "_" + aug_type
augment_datasets.append(new_dataset)
new_dataset_path = os.path.join(cfg.preprocess.processed_dir, new_dataset)
for dataset_type in split:
metadata_path = os.path.join(dataset_path, "{}.json".format(dataset_type))
augmented_metadata = []
new_metadata_path = os.path.join(
new_dataset_path, "{}.json".format(dataset_type)
)
os.makedirs(new_dataset_path, exist_ok=True)
new_dataset_wav_dir = os.path.join(new_dataset_path, "wav")
os.makedirs(new_dataset_wav_dir, exist_ok=True)
if has_existed(new_metadata_path):
continue
with open(metadata_path, "r") as f:
metadata = json.load(f)
for utt in tqdm(metadata):
original_wav_path = utt["Path"]
original_wav, sr = torchaudio.load(original_wav_path)
new_wav = wav_manipulation(original_wav, sr, aug_type=aug_type)
new_wav_path = os.path.join(new_dataset_wav_dir, utt["Uid"] + ".wav")
torchaudio.save(new_wav_path, new_wav, sr)
new_utt = {
"Dataset": utt["Dataset"] + "_" + aug_type,
"index": utt["index"],
"Singer": utt["Singer"],
"Uid": utt["Uid"],
"Path": new_wav_path,
"Duration": utt["Duration"],
}
augmented_metadata.append(new_utt)
new_metadata_path = os.path.join(
new_dataset_path, "{}.json".format(dataset_type)
)
with open(new_metadata_path, "w") as f:
json.dump(augmented_metadata, f, indent=4, ensure_ascii=False)
return augment_datasets