Ebranchformer (#1951)

* adding ebranchformer encoder

* extend surfaced FeatureExtractorConfig

- so ebranchformer feature extraction can be configured from Python
- the GlobCmvn is not needed, as it is a module in the OnnxEncoder

* clean the code

* Integrating remarks from Fangjun

sherpa-onnx/python/csrc/features.cc

@@ -11,15 +11,21 @@ namespace sherpa_onnx {
 static void PybindFeatureExtractorConfig(py::module *m) {
   using PyClass = FeatureExtractorConfig;
   py::class_<PyClass>(*m, "FeatureExtractorConfig")
-      .def(py::init<int32_t, int32_t, float, float, float>(),
-           py::arg("sampling_rate") = 16000, py::arg("feature_dim") = 80,
-           py::arg("low_freq") = 20.0f, py::arg("high_freq") = -400.0f,
-           py::arg("dither") = 0.0f)
+      .def(py::init<int32_t, int32_t, float, float, float, bool, bool>(),
+           py::arg("sampling_rate") = 16000,
+           py::arg("feature_dim") = 80,
+           py::arg("low_freq") = 20.0f,
+           py::arg("high_freq") = -400.0f,
+           py::arg("dither") = 0.0f,
+           py::arg("normalize_samples") = true,
+           py::arg("snip_edges") = false)
       .def_readwrite("sampling_rate", &PyClass::sampling_rate)
       .def_readwrite("feature_dim", &PyClass::feature_dim)
       .def_readwrite("low_freq", &PyClass::low_freq)
       .def_readwrite("high_freq", &PyClass::high_freq)
       .def_readwrite("dither", &PyClass::dither)
+      .def_readwrite("normalize_samples", &PyClass::normalize_samples)
+      .def_readwrite("snip_edges", &PyClass::snip_edges)
       .def("__str__", &PyClass::ToString);
 }
 

sherpa-onnx/python/csrc/online-stream.cc

@@ -22,6 +22,23 @@ Args:
     to the range [-1, 1].
 )";
 
+
+constexpr const char *kGetFramesUsage = R"(
+Get n frames starting from the given frame index.
+(hint: intended for debugging, for comparing FBANK features across pipelines)
+
+Args:
+  frame_index:
+    The starting frame index
+  n:
+    Number of frames to get.
+Return:
+  Return a 2-D tensor of shape (n, feature_dim).
+  which is flattened into a 1-D vector (flattened in row major).
+  Unflatten in python with:
+    `features = np.reshape(arr, (n, feature_dim))`
+)";
+
 void PybindOnlineStream(py::module *m) {
   using PyClass = OnlineStream;
   py::class_<PyClass>(*m, "OnlineStream")
@@ -34,6 +51,9 @@ void PybindOnlineStream(py::module *m) {
           py::arg("sample_rate"), py::arg("waveform"), kAcceptWaveformUsage,
           py::call_guard<py::gil_scoped_release>())
       .def("input_finished", &PyClass::InputFinished,
+           py::call_guard<py::gil_scoped_release>())
+      .def("get_frames", &PyClass::GetFrames,
+           py::arg("frame_index"), py::arg("n"), kGetFramesUsage,
            py::call_guard<py::gil_scoped_release>());
 }
 

sherpa-onnx/python/sherpa_onnx/online_recognizer.py

@@ -50,6 +50,8 @@ class OnlineRecognizer(object):
         low_freq: float = 20.0,
         high_freq: float = -400.0,
         dither: float = 0.0,
+        normalize_samples: bool = True,
+        snip_edges: bool = False,
         enable_endpoint_detection: bool = False,
         rule1_min_trailing_silence: float = 2.4,
         rule2_min_trailing_silence: float = 1.2,
@@ -118,6 +120,15 @@ class OnlineRecognizer(object):
             By default the audio samples are in range [-1,+1],
             so dithering constant 0.00003 is a good value,
             equivalent to the default 1.0 from kaldi
+          normalize_samples:
+            True for +/- 1.0 range of audio samples (default, zipformer feats),
+            False for +/- 32k samples (ebranchformer features).
+          snip_edges:
+            handling of end of audio signal in kaldi feature extraction.
+            If true, end effects will be handled by outputting only frames that
+            completely fit in the file, and the number of frames depends on the
+            frame-length.  If false, the number of frames depends only on the
+            frame-shift, and we reflect the data at the ends.
           enable_endpoint_detection:
             True to enable endpoint detection. False to disable endpoint
             detection.
@@ -248,6 +259,8 @@ class OnlineRecognizer(object):
 
         feat_config = FeatureExtractorConfig(
             sampling_rate=sample_rate,
+            normalize_samples=normalize_samples,
+            snip_edges=snip_edges,
             feature_dim=feature_dim,
             low_freq=low_freq,
             high_freq=high_freq,