PaddleSpeech/data_utils/featurizer/audio_featurizer.py

"""Contains the audio featurizer class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
from data_utils.utility import read_manifest
from data_utils.audio import AudioSegment
from python_speech_features import mfcc
from python_speech_features import delta


class AudioFeaturizer(object):
    """Audio featurizer, for extracting features from audio contents of
    AudioSegment or SpeechSegment.

    Currently, it supports feature types of linear spectrogram and mfcc.

    :param specgram_type: Specgram feature type. Options: 'linear'.
    :type specgram_type: str
    :param stride_ms: Striding size (in milliseconds) for generating frames.
    :type stride_ms: float
    :param window_ms: Window size (in milliseconds) for generating frames.
    :type window_ms: float
    :param max_freq: When specgram_type is 'linear', only FFT bins
                     corresponding to frequencies between [0, max_freq] are
                     returned; when specgram_type is 'mfcc', max_feq is the
                     highest band edge of mel filters.
    :types max_freq: None|float
    :param target_sample_rate: Audio are resampled (if upsampling or
                               downsampling is allowed) to this before
                               extracting spectrogram features.
    :type target_sample_rate: float
    :param use_dB_normalization: Whether to normalize the audio to a certain
                                 decibels before extracting the features.
    :type use_dB_normalization: bool
    :param target_dB: Target audio decibels for normalization.
    :type target_dB: float
    """

    def __init__(self,
                 specgram_type='linear',
                 stride_ms=10.0,
                 window_ms=20.0,
                 max_freq=None,
                 target_sample_rate=16000,
                 use_dB_normalization=True,
                 target_dB=-20):
        self._specgram_type = specgram_type
        self._stride_ms = stride_ms
        self._window_ms = window_ms
        self._max_freq = max_freq
        self._target_sample_rate = target_sample_rate
        self._use_dB_normalization = use_dB_normalization
        self._target_dB = target_dB

    def featurize(self,
                  audio_segment,
                  allow_downsampling=True,
                  allow_upsampling=True):
        """Extract audio features from AudioSegment or SpeechSegment.

        :param audio_segment: Audio/speech segment to extract features from.
        :type audio_segment: AudioSegment|SpeechSegment
        :param allow_downsampling: Whether to allow audio downsampling before
                                   featurizing.
        :type allow_downsampling: bool
        :param allow_upsampling: Whether to allow audio upsampling before
                                 featurizing.
        :type allow_upsampling: bool
        :return: Spectrogram audio feature in 2darray.
        :rtype: ndarray
        :raises ValueError: If audio sample rate is not supported.
        """
        # upsampling or downsampling
        if ((audio_segment.sample_rate > self._target_sample_rate and
             allow_downsampling) or
            (audio_segment.sample_rate < self._target_sample_rate and
             allow_upsampling)):
            audio_segment.resample(self._target_sample_rate)
        if audio_segment.sample_rate != self._target_sample_rate:
            raise ValueError("Audio sample rate is not supported. "
                             "Turn allow_downsampling or allow up_sampling on.")
        # decibel normalization
        if self._use_dB_normalization:
            audio_segment.normalize(target_db=self._target_dB)
        # extract spectrogram
        return self._compute_specgram(audio_segment.samples,
                                      audio_segment.sample_rate)

    def _compute_specgram(self, samples, sample_rate):
        """Extract various audio features."""
        if self._specgram_type == 'linear':
            return self._compute_linear_specgram(
                samples, sample_rate, self._stride_ms, self._window_ms,
                self._max_freq)
        elif self._specgram_type == 'mfcc':
            return self._compute_mfcc(samples, sample_rate, self._stride_ms,
                                      self._window_ms, self._max_freq)
        else:
            raise ValueError("Unknown specgram_type %s. "
                             "Supported values: linear." % self._specgram_type)

    def _compute_linear_specgram(self,
                                 samples,
                                 sample_rate,
                                 stride_ms=10.0,
                                 window_ms=20.0,
                                 max_freq=None,
                                 eps=1e-14):
        """Compute the linear spectrogram from FFT energy."""
        if max_freq is None:
            max_freq = sample_rate / 2
        if max_freq > sample_rate / 2:
            raise ValueError("max_freq must be greater than half of "
                             "sample rate.")
        if stride_ms > window_ms:
            raise ValueError("Stride size must not be greater than "
                             "window size.")
        stride_size = int(0.001 * sample_rate * stride_ms)
        window_size = int(0.001 * sample_rate * window_ms)
        specgram, freqs = self._specgram_real(
            samples,
            window_size=window_size,
            stride_size=stride_size,
            sample_rate=sample_rate)
        ind = np.where(freqs <= max_freq)[0][-1] + 1
        return np.log(specgram[:ind, :] + eps)

    def _specgram_real(self, samples, window_size, stride_size, sample_rate):
        """Compute the spectrogram for samples from a real signal."""
        # extract strided windows
        truncate_size = (len(samples) - window_size) % stride_size
        samples = samples[:len(samples) - truncate_size]
        nshape = (window_size, (len(samples) - window_size) // stride_size + 1)
        nstrides = (samples.strides[0], samples.strides[0] * stride_size)
        windows = np.lib.stride_tricks.as_strided(
            samples, shape=nshape, strides=nstrides)
        assert np.all(
            windows[:, 1] == samples[stride_size:(stride_size + window_size)])
        # window weighting, squared Fast Fourier Transform (fft), scaling
        weighting = np.hanning(window_size)[:, None]
        fft = np.fft.rfft(windows * weighting, axis=0)
        fft = np.absolute(fft)
        fft = fft**2
        scale = np.sum(weighting**2) * sample_rate
        fft[1:-1, :] *= (2.0 / scale)
        fft[(0, -1), :] /= scale
        # prepare fft frequency list
        freqs = float(sample_rate) / window_size * np.arange(fft.shape[0])
        return fft, freqs

    def _compute_mfcc(self,
                      samples,
                      sample_rate,
                      stride_ms=10.0,
                      window_ms=20.0,
                      max_freq=None):
        """Compute mfcc from samples."""
        if max_freq is None:
            max_freq = sample_rate / 2
        if max_freq > sample_rate / 2:
            raise ValueError("max_freq must not be greater than half of "
                             "sample rate.")
        if stride_ms > window_ms:
            raise ValueError("Stride size must not be greater than "
                             "window size.")
        # compute the 13 cepstral coefficients, and the first one is replaced
        # by log(frame energy)
        mfcc_feat = mfcc(
            signal=samples,
            samplerate=sample_rate,
            winlen=0.001 * window_ms,
            winstep=0.001 * stride_ms,
            highfreq=max_freq)
        # Deltas
        d_mfcc_feat = delta(mfcc_feat, 2)
        # Deltas-Deltas
        dd_mfcc_feat = delta(d_mfcc_feat, 2)
        # transpose
        mfcc_feat = np.transpose(mfcc_feat)
        d_mfcc_feat = np.transpose(d_mfcc_feat)
        dd_mfcc_feat = np.transpose(dd_mfcc_feat)
        # concat above three features
        concat_mfcc_feat = np.concatenate(
            (mfcc_feat, d_mfcc_feat, dd_mfcc_feat))
        return concat_mfcc_feat