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