| # Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| # SPDX-License-Identifier: MIT |
| |
| import numpy as np |
| import os |
| import sys |
| |
| script_dir = os.path.dirname(__file__) |
| sys.path.insert(1, os.path.join(script_dir, '..', 'common')) |
| |
| from mfcc import MFCC, AudioPreprocessor |
| |
| |
| class Wav2LetterMFCC(MFCC): |
| """Extends base MFCC class to provide Wav2Letter-specific MFCC requirements.""" |
| |
| def __init__(self, mfcc_params): |
| super().__init__(mfcc_params) |
| |
| 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)) ** 2 |
| |
| def log_mel(self, mel_energy): |
| mel_energy += 1e-10 |
| log_mel_energy = 10.0 * np.log10(mel_energy) |
| top_db = 80.0 |
| return np.maximum(log_mel_energy, log_mel_energy.max() - top_db) |
| |
| 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): |
| if k == 0: |
| dct_m[(k * num_fbank_bins) + n] = 2 * np.sqrt(1 / (4 * num_fbank_bins)) * np.cos( |
| (np.pi / num_fbank_bins) * (n + 0.5) * k) |
| else: |
| dct_m[(k * num_fbank_bins) + n] = 2 * np.sqrt(1 / (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): |
| """Over-riding parent class with ASR specific weight normalisation.""" |
| enorm = 2.0 / (self.inv_mel_scale(right_mel, False) - self.inv_mel_scale(left_mel, False)) |
| return weight * enorm |
| |
| |
| class W2LAudioPreprocessor(AudioPreprocessor): |
| |
| def __init__(self, mfcc, model_input_size, stride): |
| self.model_input_size = model_input_size |
| self.stride = stride |
| |
| super().__init__(self, model_input_size, stride) |
| # Savitzky - Golay differential filters |
| self.savgol_order1_coeffs = np.array([6.66666667e-02, 5.00000000e-02, 3.33333333e-02, |
| 1.66666667e-02, -3.46944695e-18, -1.66666667e-02, |
| -3.33333333e-02, -5.00000000e-02, -6.66666667e-02]) |
| |
| self.savgol_order2_coeffs = np.array([0.06060606, 0.01515152, -0.01731602, |
| -0.03679654, -0.04329004, -0.03679654, |
| -0.01731602, 0.01515152, 0.06060606]) |
| self._mfcc_calc = mfcc |
| |
| def mfcc_delta_calc(self, features): |
| """Over-riding parent class with ASR specific MFCC derivative features""" |
| mfcc_delta_np = np.zeros_like(features) |
| mfcc_delta2_np = np.zeros_like(features) |
| |
| for i in range(features.shape[1]): |
| idelta = np.convolve(features[:, i], self.savgol_order1_coeffs, 'same') |
| mfcc_delta_np[:, i] = idelta |
| ideltadelta = np.convolve(features[:, i], self.savgol_order2_coeffs, 'same') |
| mfcc_delta2_np[:, i] = ideltadelta |
| |
| features = np.concatenate((self._normalize(features), self._normalize(mfcc_delta_np), |
| self._normalize(mfcc_delta2_np)), axis=1) |
| |
| return features |