| # Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| # SPDX-License-Identifier: MIT |
| |
| """Class used to extract the Mel-frequency cepstral coefficients from a given audio frame.""" |
| |
| import numpy as np |
| import collections |
| |
| MFCCParams = collections.namedtuple('MFCCParams', ['sampling_freq', 'num_fbank_bins', 'mel_lo_freq', 'mel_hi_freq', |
| 'num_mfcc_feats', 'frame_len', 'use_htk_method', 'n_fft']) |
| |
| |
| class MFCC: |
| |
| def __init__(self, mfcc_params): |
| self.mfcc_params = mfcc_params |
| self.FREQ_STEP = 200.0 / 3 |
| self.MIN_LOG_HZ = 1000.0 |
| self.MIN_LOG_MEL = self.MIN_LOG_HZ / self.FREQ_STEP |
| self.LOG_STEP = 1.8562979903656 / 27.0 |
| self._frame_len_padded = int(2 ** (np.ceil((np.log(self.mfcc_params.frame_len) / np.log(2.0))))) |
| self._filter_bank_initialised = False |
| self.__frame = np.zeros(self._frame_len_padded) |
| self.__buffer = np.zeros(self._frame_len_padded) |
| self._filter_bank_filter_first = np.zeros(self.mfcc_params.num_fbank_bins) |
| self._filter_bank_filter_last = np.zeros(self.mfcc_params.num_fbank_bins) |
| self.__mel_energies = np.zeros(self.mfcc_params.num_fbank_bins) |
| self._dct_matrix = self.create_dct_matrix(self.mfcc_params.num_fbank_bins, self.mfcc_params.num_mfcc_feats) |
| self.__mel_filter_bank = self.create_mel_filter_bank() |
| self._np_mel_bank = np.zeros([self.mfcc_params.num_fbank_bins, int(self.mfcc_params.n_fft / 2) + 1]) |
| |
| for i in range(self.mfcc_params.num_fbank_bins): |
| k = 0 |
| for j in range(int(self._filter_bank_filter_first[i]), int(self._filter_bank_filter_last[i]) + 1): |
| self._np_mel_bank[i, j] = self.__mel_filter_bank[i][k] |
| k += 1 |
| |
| def mel_scale(self, freq, use_htk_method): |
| """ |
| Gets the mel scale for a particular sample frequency. |
| |
| Args: |
| freq: The sampling frequency. |
| use_htk_method: Boolean to set whether to use HTK method or not. |
| |
| Returns: |
| the mel scale |
| """ |
| if use_htk_method: |
| return 1127.0 * np.log(1.0 + freq / 700.0) |
| else: |
| mel = freq / self.FREQ_STEP |
| |
| if freq >= self.MIN_LOG_HZ: |
| mel = self.MIN_LOG_MEL + np.log(freq / self.MIN_LOG_HZ) / self.LOG_STEP |
| return mel |
| |
| def inv_mel_scale(self, mel_freq, use_htk_method): |
| """ |
| Gets the sample frequency for a particular mel. |
| |
| Args: |
| mel_freq: The mel frequency. |
| use_htk_method: Boolean to set whether to use HTK method or not. |
| |
| Returns: |
| the sample frequency |
| """ |
| if use_htk_method: |
| return 700.0 * (np.exp(mel_freq / 1127.0) - 1.0) |
| else: |
| freq = self.FREQ_STEP * mel_freq |
| |
| if mel_freq >= self.MIN_LOG_MEL: |
| freq = self.MIN_LOG_HZ * np.exp(self.LOG_STEP * (mel_freq - self.MIN_LOG_MEL)) |
| return freq |
| |
| def spectrum_calc(self, audio_data): |
| return np.abs(np.fft.rfft(np.hanning(self.mfcc_params.frame_len + 1)[0:self.mfcc_params.frame_len] * audio_data, |
| self.mfcc_params.n_fft)) |
| |
| def log_mel(self, mel_energy): |
| mel_energy += 1e-10 # Avoid division by zero |
| return np.log(mel_energy) |
| |
| def mfcc_compute(self, audio_data): |
| """ |
| Extracts the MFCC for a single frame. |
| |
| Args: |
| audio_data: The audio data to process. |
| |
| Returns: |
| the MFCC features |
| """ |
| if len(audio_data) != self.mfcc_params.frame_len: |
| raise ValueError( |
| f"audio_data buffer size {len(audio_data)} does not match frame length {self.mfcc_params.frame_len}") |
| |
| audio_data = np.array(audio_data) |
| spec = self.spectrum_calc(audio_data) |
| mel_energy = np.dot(self._np_mel_bank.astype(np.float32), |
| np.transpose(spec).astype(np.float32)) |
| log_mel_energy = self.log_mel(mel_energy) |
| mfcc_feats = np.dot(self._dct_matrix, log_mel_energy) |
| return mfcc_feats |
| |
| def create_dct_matrix(self, num_fbank_bins, num_mfcc_feats): |
| """ |
| Creates the Discrete Cosine Transform matrix to be used in the compute function. |
| |
| Args: |
| num_fbank_bins: The number of filter bank bins |
| num_mfcc_feats: the number of MFCC features |
| |
| Returns: |
| the DCT matrix |
| """ |
| |
| dct_m = np.zeros(num_fbank_bins * num_mfcc_feats) |
| for k in range(num_mfcc_feats): |
| for n in range(num_fbank_bins): |
| dct_m[(k * num_fbank_bins) + n] = (np.sqrt(2 / num_fbank_bins)) * np.cos( |
| (np.pi / num_fbank_bins) * (n + 0.5) * k) |
| dct_m = np.reshape(dct_m, [self.mfcc_params.num_mfcc_feats, self.mfcc_params.num_fbank_bins]) |
| return dct_m |
| |
| def mel_norm(self, weight, right_mel, left_mel): |
| """ |
| Placeholder function over-ridden in child class |
| """ |
| return weight |
| |
| def create_mel_filter_bank(self): |
| """ |
| Creates the Mel filter bank. |
| |
| Returns: |
| the mel filter bank |
| """ |
| # FFT calculations are greatly accelerated for frame lengths which are powers of 2 |
| # Frames are padded and FFT bin width/length calculated accordingly |
| num_fft_bins = int(self._frame_len_padded / 2) |
| fft_bin_width = self.mfcc_params.sampling_freq / self._frame_len_padded |
| |
| mel_low_freq = self.mel_scale(self.mfcc_params.mel_lo_freq, self.mfcc_params.use_htk_method) |
| mel_high_freq = self.mel_scale(self.mfcc_params.mel_hi_freq, self.mfcc_params.use_htk_method) |
| mel_freq_delta = (mel_high_freq - mel_low_freq) / (self.mfcc_params.num_fbank_bins + 1) |
| |
| this_bin = np.zeros(num_fft_bins) |
| mel_fbank = [0] * self.mfcc_params.num_fbank_bins |
| for bin_num in range(self.mfcc_params.num_fbank_bins): |
| left_mel = mel_low_freq + bin_num * mel_freq_delta |
| center_mel = mel_low_freq + (bin_num + 1) * mel_freq_delta |
| right_mel = mel_low_freq + (bin_num + 2) * mel_freq_delta |
| first_index = last_index = -1 |
| |
| for i in range(num_fft_bins): |
| freq = (fft_bin_width * i) |
| mel = self.mel_scale(freq, self.mfcc_params.use_htk_method) |
| this_bin[i] = 0.0 |
| |
| if (mel > left_mel) and (mel < right_mel): |
| if mel <= center_mel: |
| weight = (mel - left_mel) / (center_mel - left_mel) |
| else: |
| weight = (right_mel - mel) / (right_mel - center_mel) |
| |
| this_bin[i] = self.mel_norm(weight, right_mel, left_mel) |
| |
| if first_index == -1: |
| first_index = i |
| last_index = i |
| |
| self._filter_bank_filter_first[bin_num] = first_index |
| self._filter_bank_filter_last[bin_num] = last_index |
| mel_fbank[bin_num] = np.zeros(last_index - first_index + 1) |
| j = 0 |
| |
| for i in range(first_index, last_index + 1): |
| mel_fbank[bin_num][j] = this_bin[i] |
| j += 1 |
| |
| return mel_fbank |
| |
| |
| class AudioPreprocessor: |
| |
| def __init__(self, mfcc, model_input_size, stride): |
| self.model_input_size = model_input_size |
| self.stride = stride |
| self._mfcc_calc = mfcc |
| |
| def _normalize(self, values): |
| """ |
| Normalize values to mean 0 and std 1 |
| """ |
| ret_val = (values - np.mean(values)) / np.std(values) |
| return ret_val |
| |
| def _get_features(self, features, mfcc_instance, audio_data): |
| idx = 0 |
| while len(features) < self.model_input_size * mfcc_instance.mfcc_params.num_mfcc_feats: |
| current_frame_feats = mfcc_instance.mfcc_compute(audio_data[idx:idx + int(mfcc_instance.mfcc_params.frame_len)]) |
| features.extend(current_frame_feats) |
| idx += self.stride |
| |
| def mfcc_delta_calc(self, features): |
| """ |
| Placeholder function over-ridden in child class |
| """ |
| return features |
| |
| def extract_features(self, audio_data): |
| """ |
| Extracts the MFCC features. Also calculates each features first and second order derivatives |
| if the mfcc_delta_calc() function has been implemented by a child class. |
| The matrix returned should be sized appropriately for input to the model, based |
| on the model info specified in the MFCC instance. |
| |
| Args: |
| audio_data: the audio data to be used for this calculation |
| Returns: |
| the derived MFCC feature vector, sized appropriately for inference |
| """ |
| |
| num_samples_per_inference = ((self.model_input_size - 1) |
| * self.stride) + self._mfcc_calc.mfcc_params.frame_len |
| |
| if len(audio_data) < num_samples_per_inference: |
| raise ValueError("audio_data size for feature extraction is smaller than " |
| "the expected number of samples needed for inference") |
| |
| features = [] |
| self._get_features(features, self._mfcc_calc, np.asarray(audio_data)) |
| features = np.reshape(np.array(features), (self.model_input_size, self._mfcc_calc.mfcc_params.num_mfcc_feats)) |
| features = self.mfcc_delta_calc(features) |
| return np.float32(features) |