Add fbank and mfcc unittest.

paddleaudio/tests/features/test_kaldi.py

@@ -0,0 +1,81 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import unittest
+
+import numpy as np
+import paddle
+import torch
+import torchaudio
+
+import paddleaudio
+from .base import FeatTest
+
+
+class TestKaldi(FeatTest):
+    def initParmas(self):
+        self.window_size = 1024
+        self.dtype = 'float32'
+
+    def test_window(self):
+        t_hann_window = torch.hann_window(
+            self.window_size, periodic=False, dtype=eval(f'torch.{self.dtype}'))
+        t_hamm_window = torch.hamming_window(
+            self.window_size,
+            periodic=False,
+            alpha=0.54,
+            beta=0.46,
+            dtype=eval(f'torch.{self.dtype}'))
+        t_povey_window = torch.hann_window(
+            self.window_size, periodic=False,
+            dtype=eval(f'torch.{self.dtype}')).pow(0.85)
+
+        p_hann_window = paddleaudio.functional.window.get_window(
+            'hann',
+            self.window_size,
+            fftbins=False,
+            dtype=eval(f'paddle.{self.dtype}'))
+        p_hamm_window = paddleaudio.functional.window.get_window(
+            'hamming',
+            self.window_size,
+            fftbins=False,
+            dtype=eval(f'paddle.{self.dtype}'))
+        p_povey_window = paddleaudio.functional.window.get_window(
+            'hann',
+            self.window_size,
+            fftbins=False,
+            dtype=eval(f'paddle.{self.dtype}')).pow(0.85)
+
+        np.testing.assert_array_almost_equal(t_hann_window, p_hann_window)
+        np.testing.assert_array_almost_equal(t_hamm_window, p_hamm_window)
+        np.testing.assert_array_almost_equal(t_povey_window, p_povey_window)
+
+    def test_fbank(self):
+        ta_features = torchaudio.compliance.kaldi.fbank(
+            torch.from_numpy(self.waveform.astype(self.dtype)))
+        pa_features = paddleaudio.compliance.kaldi.fbank(
+            paddle.to_tensor(self.waveform.astype(self.dtype)))
+        np.testing.assert_array_almost_equal(
+            ta_features, pa_features, decimal=4)
+
+    def test_mfcc(self):
+        ta_features = torchaudio.compliance.kaldi.mfcc(
+            torch.from_numpy(self.waveform.astype(self.dtype)))
+        pa_features = paddleaudio.compliance.kaldi.mfcc(
+            paddle.to_tensor(self.waveform.astype(self.dtype)))
+        np.testing.assert_array_almost_equal(
+            ta_features, pa_features, decimal=4)
+
+
+if __name__ == '__main__':
+    unittest.main()

paddleaudio/tests/features/test_librosa.py

@@ -27,9 +27,11 @@ class TestLibrosa(FeatTest):
         self.n_fft = 512
         self.hop_length = 128
         self.n_mels = 40
+        self.n_mfcc = 20
         self.fmin = 0.0
         self.window_str = 'hann'
         self.pad_mode = 'reflect'
+        self.top_db = 80.0
 
     def test_stft(self):
         if len(self.waveform.shape) == 2:  # (C, T)
@@ -222,6 +224,58 @@ class TestLibrosa(FeatTest):
         np.testing.assert_array_almost_equal(
             feature_librosa, feature_layer, decimal=4)
 
+    def test_mfcc(self):
+        if len(self.waveform.shape) == 2:  # (C, T)
+            self.waveform = self.waveform.squeeze(
+                0)  # 1D input for librosa.feature.melspectrogram
+
+        # librosa:
+        feature_librosa = librosa.feature.mfcc(
+            y=self.waveform,
+            sr=self.sr,
+            S=None,
+            n_mfcc=self.n_mfcc,
+            dct_type=2,
+            norm='ortho',
+            lifter=0,
+            n_fft=self.n_fft,
+            hop_length=self.hop_length,
+            n_mels=self.n_mels,
+            fmin=self.fmin)
+
+        # paddleaudio.compliance.librosa:
+        feature_compliance = paddleaudio.compliance.librosa.mfcc(
+            x=self.waveform,
+            sr=self.sr,
+            n_mfcc=self.n_mfcc,
+            dct_type=2,
+            norm='ortho',
+            lifter=0,
+            window_size=self.n_fft,
+            hop_length=self.hop_length,
+            n_mels=self.n_mels,
+            fmin=self.fmin,
+            top_db=self.top_db)
+
+        # paddleaudio.features.layer
+        x = paddle.to_tensor(
+            self.waveform, dtype=paddle.float64).unsqueeze(0)  # Add batch dim.
+        feature_extractor = paddleaudio.features.MFCC(
+            sr=self.sr,
+            n_mfcc=self.n_mfcc,
+            n_fft=self.n_fft,
+            hop_length=self.hop_length,
+            n_mels=self.n_mels,
+            f_min=self.fmin,
+            top_db=self.top_db,
+            dtype=x.dtype)
+        feature_layer = feature_extractor(x).squeeze(0).numpy()
+
+        np.testing.assert_array_almost_equal(
+            feature_librosa, feature_compliance, decimal=4)
+        np.testing.assert_array_almost_equal(
+            feature_librosa, feature_layer, decimal=4)
+
 
 if __name__ == '__main__':
     unittest.main()