| # Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| # SPDX-License-Identifier: MIT |
| |
| import numpy as np |
| |
| from context import preprocess |
| |
| test_wav = [ |
| -3,0,1,-1,2,3,-2,2, |
| 1,-2,0,3,-1,8,3,2, |
| -1,-1,2,7,3,5,6,6, |
| 6,12,5,6,3,3,5,4, |
| 4,6,7,7,7,3,7,2, |
| 8,4,4,2,-4,-1,-1,-4, |
| 2,1,-1,-4,0,-7,-6,-2, |
| -5,1,-5,-1,-7,-3,-3,-7, |
| 0,-3,3,-5,0,1,-2,-2, |
| -3,-3,-7,-3,-2,-6,-5,-8, |
| -2,-8,4,-9,-4,-9,-5,-5, |
| -3,-9,-3,-9,-1,-7,-4,1, |
| -3,2,-8,-4,-4,-5,1,-3, |
| -1,0,-1,-2,-3,-2,-4,-1, |
| 1,-1,3,0,3,2,0,0, |
| 0,-3,1,1,0,8,3,4, |
| 1,5,6,4,7,3,3,0, |
| 3,6,7,6,4,5,9,9, |
| 5,5,8,1,6,9,6,6, |
| 7,1,8,1,5,0,5,5, |
| 0,3,2,7,2,-3,3,0, |
| 3,0,0,0,2,0,-1,-1, |
| -2,-3,-8,0,1,0,-3,-3, |
| -3,-2,-3,-3,-4,-6,-2,-8, |
| -9,-4,-1,-5,-3,-3,-4,-3, |
| -6,3,0,-1,-2,-9,-4,-2, |
| 2,-1,3,-5,-5,-2,0,-2, |
| 0,-1,-3,1,-2,9,4,5, |
| 2,2,1,0,-6,-2,0,0, |
| 0,-1,4,-4,3,-7,-1,5, |
| -6,-1,-5,4,3,9,-2,1, |
| 3,0,0,-2,1,2,1,1, |
| 0,3,2,-1,3,-3,7,0, |
| 0,3,2,2,-2,3,-2,2, |
| -3,4,-1,-1,-5,-1,-3,-2, |
| 1,-1,3,2,4,1,2,-2, |
| 0,2,7,0,8,-3,6,-3, |
| 6,1,2,-3,-1,-1,-1,1, |
| -2,2,1,2,0,-2,3,-2, |
| 3,-2,1,0,-3,-1,-2,-4, |
| -6,-5,-8,-1,-4,0,-3,-1, |
| -1,-1,0,-2,-3,-7,-1,0, |
| 1,5,0,5,1,1,-3,0, |
| -6,3,-8,4,-8,6,-6,1, |
| -6,-2,-5,-6,0,-5,4,-1, |
| 4,-2,1,2,1,0,-2,0, |
| 0,2,-2,2,-5,2,0,-2, |
| 1,-2,0,5,1,0,1,5, |
| 0,8,3,2,2,0,5,-2, |
| 3,1,0,1,0,-2,-1,-3, |
| 1,-1,3,0,3,0,-2,-1, |
| -4,-4,-4,-1,-4,-4,-3,-6, |
| -3,-7,-3,-1,-2,0,-5,-4, |
| -7,-3,-2,-2,1,2,2,8, |
| 5,4,2,4,3,5,0,3, |
| 3,6,4,2,2,-2,4,-2, |
| 3,3,2,1,1,4,-5,2, |
| -3,0,-1,1,-2,2,5,1, |
| 4,2,3,1,-1,1,0,6, |
| 0,-2,-1,1,-1,2,-5,-1, |
| -5,-1,-6,-3,-3,2,4,0, |
| -1,-5,3,-4,-1,-3,-4,1, |
| -4,1,-1,-1,0,-5,-4,-2, |
| -1,-1,-3,-7,-3,-3,4,4, |
| ] |
| |
| def test_mel_scale_function_with_htk_true(): |
| samp_freq = 16000 |
| frame_len_ms = 32 |
| frame_len_samples = samp_freq * frame_len_ms * 0.001 |
| num_mfcc_feats = 13 |
| num_fbank_bins = 128 |
| mel_lo_freq = 0 |
| mil_hi_freq = 8000 |
| use_htk = False |
| n_FFT = 512 |
| |
| mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats, |
| frame_len_samples, use_htk, n_FFT) |
| |
| mfcc_inst = preprocess.MFCC(mfcc_params) |
| |
| mel = mfcc_inst.mel_scale(16, True) |
| |
| assert np.isclose(mel, 25.470010570730597) |
| |
| |
| def test_mel_scale_function_with_htk_false(): |
| samp_freq = 16000 |
| frame_len_ms = 32 |
| frame_len_samples = samp_freq * frame_len_ms * 0.001 |
| num_mfcc_feats = 13 |
| num_fbank_bins = 128 |
| mel_lo_freq = 0 |
| mil_hi_freq = 8000 |
| use_htk = False |
| n_FFT = 512 |
| |
| mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats, |
| frame_len_samples, use_htk, n_FFT) |
| |
| mfcc_inst = preprocess.MFCC(mfcc_params) |
| |
| mel = mfcc_inst.mel_scale(16, False) |
| |
| assert np.isclose(mel, 0.24) |
| |
| |
| def test_inverse_mel_scale_function_with_htk_true(): |
| samp_freq = 16000 |
| frame_len_ms = 32 |
| frame_len_samples = samp_freq * frame_len_ms * 0.001 |
| num_mfcc_feats = 13 |
| num_fbank_bins = 128 |
| mel_lo_freq = 0 |
| mil_hi_freq = 8000 |
| use_htk = False |
| n_FFT = 512 |
| |
| mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats, |
| frame_len_samples, use_htk, n_FFT) |
| |
| mfcc_inst = preprocess.MFCC(mfcc_params) |
| |
| mel = mfcc_inst.inv_mel_scale(16, True) |
| |
| assert np.isclose(mel, 10.008767240008943) |
| |
| |
| def test_inverse_mel_scale_function_with_htk_false(): |
| samp_freq = 16000 |
| frame_len_ms = 32 |
| frame_len_samples = samp_freq * frame_len_ms * 0.001 |
| num_mfcc_feats = 13 |
| num_fbank_bins = 128 |
| mel_lo_freq = 0 |
| mil_hi_freq = 8000 |
| use_htk = False |
| n_FFT = 512 |
| |
| mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats, |
| frame_len_samples, use_htk, n_FFT) |
| |
| mfcc_inst = preprocess.MFCC(mfcc_params) |
| |
| mel = mfcc_inst.inv_mel_scale(16, False) |
| |
| assert np.isclose(mel, 1071.170287494467) |
| |
| |
| def test_create_mel_filter_bank(): |
| samp_freq = 16000 |
| frame_len_ms = 32 |
| frame_len_samples = samp_freq * frame_len_ms * 0.001 |
| num_mfcc_feats = 13 |
| num_fbank_bins = 128 |
| mel_lo_freq = 0 |
| mil_hi_freq = 8000 |
| use_htk = False |
| n_FFT = 512 |
| |
| mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats, |
| frame_len_samples, use_htk, n_FFT) |
| |
| mfcc_inst = preprocess.MFCC(mfcc_params) |
| |
| mel_filter_bank = mfcc_inst.create_mel_filter_bank() |
| |
| assert len(mel_filter_bank) == 128 |
| |
| assert str(mel_filter_bank[0]) == "[0.02837754]" |
| assert str(mel_filter_bank[1]) == "[0.01438901 0.01398853]" |
| assert str(mel_filter_bank[2]) == "[0.02877802]" |
| assert str(mel_filter_bank[3]) == "[0.04236608]" |
| assert str(mel_filter_bank[4]) == "[0.00040047 0.02797707]" |
| assert str(mel_filter_bank[5]) == "[0.01478948 0.01358806]" |
| assert str(mel_filter_bank[50]) == "[0.03298853]" |
| assert str(mel_filter_bank[100]) == "[0.00260166 0.00588759 0.00914814 0.00798015 0.00476919 0.00158245]" |
| |
| |
| def test_mfcc_compute(): |
| samp_freq = 16000 |
| frame_len_ms = 32 |
| frame_len_samples = samp_freq * frame_len_ms * 0.001 |
| num_mfcc_feats = 13 |
| num_fbank_bins = 128 |
| mel_lo_freq = 0 |
| mil_hi_freq = 8000 |
| use_htk = False |
| n_FFT = 512 |
| |
| audio_data = np.array(test_wav) / (2 ** 15) |
| |
| mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats, |
| frame_len_samples, use_htk, n_FFT) |
| mfcc_inst = preprocess.MFCC(mfcc_params) |
| mfcc_feats = mfcc_inst.mfcc_compute(audio_data) |
| |
| assert np.isclose((mfcc_feats[0]), -834.9656973095651) |
| assert np.isclose((mfcc_feats[1]), 21.026915475076322) |
| assert np.isclose((mfcc_feats[2]), 18.628541708201688) |
| assert np.isclose((mfcc_feats[3]), 7.341153529494758) |
| assert np.isclose((mfcc_feats[4]), 18.907974386153214) |
| assert np.isclose((mfcc_feats[5]), -5.360387487466194) |
| assert np.isclose((mfcc_feats[6]), 6.523572638527085) |
| assert np.isclose((mfcc_feats[7]), -11.270643644983316) |
| assert np.isclose((mfcc_feats[8]), 8.375177203773777) |
| assert np.isclose((mfcc_feats[9]), 12.06721844362991) |
| assert np.isclose((mfcc_feats[10]), 8.30815892468875) |
| assert np.isclose((mfcc_feats[11]), -13.499911910889917) |
| assert np.isclose((mfcc_feats[12]), -18.176121251436165) |
| |
| |
| def test_sliding_window_for_small_num_samples(): |
| samp_freq = 16000 |
| frame_len_ms = 32 |
| frame_len_samples = samp_freq * frame_len_ms * 0.001 |
| num_mfcc_feats = 13 |
| mode_input_size = 9 |
| stride = 160 |
| num_fbank_bins = 128 |
| mel_lo_freq = 0 |
| mil_hi_freq = 8000 |
| use_htk = False |
| n_FFT = 512 |
| |
| audio_data = np.array(test_wav) / (2 ** 15) |
| |
| full_audio_data = np.tile(audio_data, 9) |
| |
| mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats, |
| frame_len_samples, use_htk, n_FFT) |
| mfcc_inst = preprocess.MFCC(mfcc_params) |
| preprocessor = preprocess.Preprocessor(mfcc_inst, mode_input_size, stride) |
| |
| input_tensor = preprocessor.extract_features(full_audio_data) |
| |
| assert np.isclose(input_tensor[0][0], -3.4660944830426454) |
| assert np.isclose(input_tensor[0][1], 0.3587718932127629) |
| assert np.isclose(input_tensor[0][2], 0.3480551325669172) |
| assert np.isclose(input_tensor[0][3], 0.2976191917228921) |
| assert np.isclose(input_tensor[0][4], 0.3493037340849936) |
| assert np.isclose(input_tensor[0][5], 0.2408643285767937) |
| assert np.isclose(input_tensor[0][6], 0.2939659585037282) |
| assert np.isclose(input_tensor[0][7], 0.2144552669573928) |
| assert np.isclose(input_tensor[0][8], 0.302239565899944) |
| assert np.isclose(input_tensor[0][9], 0.3187368787077345) |
| assert np.isclose(input_tensor[0][10], 0.3019401051295793) |
| assert np.isclose(input_tensor[0][11], 0.20449412797602678) |
| |
| assert np.isclose(input_tensor[0][38], -0.18751440767749533) |
| |
| |
| def test_sliding_window_for_wav_2_letter_sized_input(): |
| samp_freq = 16000 |
| frame_len_ms = 32 |
| frame_len_samples = samp_freq * frame_len_ms * 0.001 |
| num_mfcc_feats = 13 |
| mode_input_size = 296 |
| stride = 160 |
| num_fbank_bins = 128 |
| mel_lo_freq = 0 |
| mil_hi_freq = 8000 |
| use_htk = False |
| n_FFT = 512 |
| |
| audio_data = np.zeros(47712, dtype=int) |
| |
| mfcc_params = preprocess.MFCCParams(samp_freq, num_fbank_bins, mel_lo_freq, mil_hi_freq, num_mfcc_feats, |
| frame_len_samples, use_htk, n_FFT) |
| |
| mfcc_inst = preprocess.MFCC(mfcc_params) |
| preprocessor = preprocess.Preprocessor(mfcc_inst, mode_input_size, stride) |
| |
| input_tensor = preprocessor.extract_features(audio_data) |
| |
| assert len(input_tensor[0]) == 39 |
| assert len(input_tensor) == 296 |