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# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from scipy.interpolate import interp1d
import torchaudio
from fairseq.tasks.text_to_speech import (
batch_compute_distortion, compute_rms_dist
)
def batch_mel_spectral_distortion(
y1, y2, sr, normalize_type="path", mel_fn=None
):
"""
https://arxiv.org/pdf/2011.03568.pdf
Same as Mel Cepstral Distortion, but computed on log-mel spectrograms.
"""
if mel_fn is None or mel_fn.sample_rate != sr:
mel_fn = torchaudio.transforms.MelSpectrogram(
sr, n_fft=int(0.05 * sr), win_length=int(0.05 * sr),
hop_length=int(0.0125 * sr), f_min=20, n_mels=80,
window_fn=torch.hann_window
).to(y1[0].device)
offset = 1e-6
return batch_compute_distortion(
y1, y2, sr, lambda y: torch.log(mel_fn(y) + offset).transpose(-1, -2),
compute_rms_dist, normalize_type
)
# This code is based on
# "https://github.com/bastibe/MAPS-Scripts/blob/master/helper.py"
def _same_t_in_true_and_est(func):
def new_func(true_t, true_f, est_t, est_f):
assert type(true_t) is np.ndarray
assert type(true_f) is np.ndarray
assert type(est_t) is np.ndarray
assert type(est_f) is np.ndarray
interpolated_f = interp1d(
est_t, est_f, bounds_error=False, kind='nearest', fill_value=0
)(true_t)
return func(true_t, true_f, true_t, interpolated_f)
return new_func
@_same_t_in_true_and_est
def gross_pitch_error(true_t, true_f, est_t, est_f):
"""The relative frequency in percent of pitch estimates that are
outside a threshold around the true pitch. Only frames that are
considered pitched by both the ground truth and the estimator (if
applicable) are considered.
"""
correct_frames = _true_voiced_frames(true_t, true_f, est_t, est_f)
gross_pitch_error_frames = _gross_pitch_error_frames(
true_t, true_f, est_t, est_f
)
return np.sum(gross_pitch_error_frames) / np.sum(correct_frames)
def _gross_pitch_error_frames(true_t, true_f, est_t, est_f, eps=1e-8):
voiced_frames = _true_voiced_frames(true_t, true_f, est_t, est_f)
true_f_p_eps = [x + eps for x in true_f]
pitch_error_frames = np.abs(est_f / true_f_p_eps - 1) > 0.2
return voiced_frames & pitch_error_frames
def _true_voiced_frames(true_t, true_f, est_t, est_f):
return (est_f != 0) & (true_f != 0)
def _voicing_decision_error_frames(true_t, true_f, est_t, est_f):
return (est_f != 0) != (true_f != 0)
@_same_t_in_true_and_est
def f0_frame_error(true_t, true_f, est_t, est_f):
gross_pitch_error_frames = _gross_pitch_error_frames(
true_t, true_f, est_t, est_f
)
voicing_decision_error_frames = _voicing_decision_error_frames(
true_t, true_f, est_t, est_f
)
return (np.sum(gross_pitch_error_frames) +
np.sum(voicing_decision_error_frames)) / (len(true_t))
@_same_t_in_true_and_est
def voicing_decision_error(true_t, true_f, est_t, est_f):
voicing_decision_error_frames = _voicing_decision_error_frames(
true_t, true_f, est_t, est_f
)
return np.sum(voicing_decision_error_frames) / (len(true_t))
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