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-rw-r--r--python/pyarmnn/examples/speech_recognition/tests/test_mfcc.py286
1 files changed, 286 insertions, 0 deletions
diff --git a/python/pyarmnn/examples/speech_recognition/tests/test_mfcc.py b/python/pyarmnn/examples/speech_recognition/tests/test_mfcc.py
new file mode 100644
index 0000000000..d692ab51c8
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+++ b/python/pyarmnn/examples/speech_recognition/tests/test_mfcc.py
@@ -0,0 +1,286 @@
+# 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
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-19 09:40:29 +0000