Add OnlinePlp Python APIs.

kaldifeat/python/kaldifeat/__init__.py

@@ -10,5 +10,5 @@ from _kaldifeat import (
 
 from .fbank import Fbank, OnlineFbank
 from .mfcc import Mfcc, OnlineMfcc
-from .plp import Plp
+from .plp import OnlinePlp, Plp
 from .spectrogram import Spectrogram

kaldifeat/python/kaldifeat/plp.py

@@ -4,9 +4,20 @@
 import _kaldifeat
 
 from .offline_feature import OfflineFeature
+from .online_feature import OnlineFeature
 
 
 class Plp(OfflineFeature):
     def __init__(self, opts: _kaldifeat.PlpOptions):
         super().__init__(opts)
         self.computer = _kaldifeat.Plp(opts)
+
+
+class OnlinePlp(OnlineFeature):
+    def __init__(self, opts: _kaldifeat.PlpOptions):
+        super().__init__(opts)
+        self.computer = _kaldifeat.OnlinePlp(opts)
+
+    def __setstate__(self, state):
+        self.opts = _kaldifeat.PlpOptions.from_dict(state)
+        self.computer = _kaldifeat.OnlinePlp(self.opts)

kaldifeat/python/tests/test_plp.py

@@ -13,24 +13,82 @@ import kaldifeat
 cur_dir = Path(__file__).resolve().parent
 
 
+def test_online_plp(
+    opts: kaldifeat.PlpOptions,
+    wave: torch.Tensor,
+    cpu_features: torch.Tensor,
+):
+    """
+    Args:
+      opts:
+        The options to create the online plp extractor.
+      wave:
+        The input 1-D waveform.
+      cpu_features:
+        The groud truth features that are computed offline
+    """
+    online_plp = kaldifeat.OnlinePlp(opts)
+
+    num_processed_frames = 0
+    i = 0  # current sample index to feed
+    while not online_plp.is_last_frame(num_processed_frames - 1):
+        while num_processed_frames < online_plp.num_frames_ready:
+            # There are new frames to be processed
+            frame = online_plp.get_frame(num_processed_frames)
+            assert torch.allclose(
+                frame.squeeze(0), cpu_features[num_processed_frames], atol=1e-3
+            )
+            num_processed_frames += 1
+
+        # Simulate streaming . Send a random number of audio samples
+        # to the extractor
+        num_samples = torch.randint(300, 1000, (1,)).item()
+
+        samples = wave[i : (i + num_samples)]  # noqa
+        i += num_samples
+        if len(samples) == 0:
+            online_plp.input_finished()
+            continue
+
+        online_plp.accept_waveform(16000, samples)
+
+    assert num_processed_frames == online_plp.num_frames_ready
+    assert num_processed_frames == cpu_features.size(0)
+
+
 def test_plp_default():
     print("=====test_plp_default=====")
+    filename = cur_dir / "test_data/test.wav"
+    wave = read_wave(filename)
+    gt = read_ark_txt(cur_dir / "test_data/test-plp.txt")
+
+    cpu_features = None
     for device in get_devices():
         print("device", device)
         opts = kaldifeat.PlpOptions()
         opts.frame_opts.dither = 0
         opts.device = device
         plp = kaldifeat.Plp(opts)
-        filename = cur_dir / "test_data/test.wav"
-        wave = read_wave(filename).to(device)
 
-        features = plp(wave)
-        gt = read_ark_txt(cur_dir / "test_data/test-plp.txt")
+        features = plp(wave.to(device))
+        if device.type == "cpu":
+            cpu_features = features
+
         assert torch.allclose(features.cpu(), gt, rtol=1e-1)
 
+    opts = kaldifeat.PlpOptions()
+    opts.frame_opts.dither = 0
+
+    test_online_plp(opts, wave, cpu_features)
+
 
 def test_plp_no_snip_edges():
     print("=====test_plp_no_snip_edges=====")
+    filename = cur_dir / "test_data/test.wav"
+    wave = read_wave(filename)
+    gt = read_ark_txt(cur_dir / "test_data/test-plp-no-snip-edges.txt")
+
+    cpu_features = None
     for device in get_devices():
         print("device", device)
         opts = kaldifeat.PlpOptions()
@@ -39,16 +97,26 @@ def test_plp_no_snip_edges():
         opts.frame_opts.snip_edges = False
 
         plp = kaldifeat.Plp(opts)
-        filename = cur_dir / "test_data/test.wav"
-        wave = read_wave(filename).to(device)
 
-        features = plp(wave)
-        gt = read_ark_txt(cur_dir / "test_data/test-plp-no-snip-edges.txt")
+        features = plp(wave.to(device))
+        if device.type == "cpu":
+            cpu_features = features
         assert torch.allclose(features.cpu(), gt, atol=1e-1)
 
+    opts = kaldifeat.PlpOptions()
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+
+    test_online_plp(opts, wave, cpu_features)
+
 
 def test_plp_htk_10_ceps():
     print("=====test_plp_htk_10_ceps=====")
+    filename = cur_dir / "test_data/test.wav"
+    wave = read_wave(filename)
+    gt = read_ark_txt(cur_dir / "test_data/test-plp-htk-10-ceps.txt")
+
+    cpu_features = None
     for device in get_devices():
         print("device", device)
         opts = kaldifeat.PlpOptions()
@@ -58,13 +126,19 @@ def test_plp_htk_10_ceps():
         opts.frame_opts.dither = 0
 
         plp = kaldifeat.Plp(opts)
-        filename = cur_dir / "test_data/test.wav"
-        wave = read_wave(filename).to(device)
 
-        features = plp(wave)
-        gt = read_ark_txt(cur_dir / "test_data/test-plp-htk-10-ceps.txt")
+        features = plp(wave.to(device))
+        if device.type == "cpu":
+            cpu_features = features
         assert torch.allclose(features.cpu(), gt, atol=1e-1)
 
+    opts = kaldifeat.PlpOptions()
+    opts.htk_compat = True
+    opts.num_ceps = 10
+    opts.frame_opts.dither = 0
+
+    test_online_plp(opts, wave, cpu_features)
+
 
 def test_pickle():
     for device in get_devices():
@@ -79,6 +153,16 @@ def test_pickle():
 
         assert str(plp.opts) == str(plp2.opts)
 
+    opts = kaldifeat.PlpOptions()
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+
+    plp = kaldifeat.OnlinePlp(opts)
+    data = pickle.dumps(plp)
+    plp2 = pickle.loads(data)
+
+    assert str(plp.opts) == str(plp2.opts)
+
 
 if __name__ == "__main__":
     test_plp_default()