1 files changed, 91 insertions, 0 deletions
diff --git a/python/pyarmnn/examples/speech_recognition/wav2letter_mfcc.py b/python/pyarmnn/examples/speech_recognition/wav2letter_mfcc.py
new file mode 100644
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+++ b/python/pyarmnn/examples/speech_recognition/wav2letter_mfcc.py
@@ -0,0 +1,91 @@
+# 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