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# 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
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