Python API for speaker diarization.

.github/scripts/test-python.sh

@@ -8,6 +8,21 @@ log() {
   echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
 }
 
+log "test offline speaker diarization"
+
+curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+tar xvf sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+rm sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+
+curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-recongition-models/3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx
+
+curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/0-four-speakers-zh.wav
+
+python3 ./python-api-examples/offline-speaker-diarization.py
+
+rm -rf *.wav *.onnx ./sherpa-onnx-pyannote-segmentation-3-0
+
+
 log "test_clustering"
 pushd /tmp/
 mkdir test-cluster

.github/workflows/windows-x64.yaml

@@ -93,7 +93,7 @@ jobs:
         shell: bash
         run: |
           du -h -d1 .
-          export PATH=$PWD/build/bin:$PATH
+          export PATH=$PWD/build/bin/Release:$PATH
           export EXE=sherpa-onnx-offline-speaker-diarization.exe
 
           .github/scripts/test-speaker-diarization.sh

.github/workflows/windows-x86.yaml

@@ -93,7 +93,7 @@ jobs:
         shell: bash
         run: |
           du -h -d1 .
-          export PATH=$PWD/build/bin:$PATH
+          export PATH=$PWD/build/bin/Release:$PATH
           export EXE=sherpa-onnx-offline-speaker-diarization.exe
 
           .github/scripts/test-speaker-diarization.sh

python-api-examples/offline-speaker-diarization.py

@@ -0,0 +1,118 @@
+#!/usr/bin/env python3
+# Copyright (c)  2024  Xiaomi Corporation
+
+"""
+This file shows how to use sherpa-onnx Python API for
+offline/non-streaming speaker diarization.
+
+Usage:
+
+Step 1: Download a speaker segmentation model
+
+Please visit https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-segmentation-models
+for a list of available models. The following is an example
+
+  wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+  tar xvf sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+  rm sherpa-onnx-pyannote-segmentation-3-0.tar.bz2
+
+Step 2: Download a speaker embedding extractor model
+
+Please visit https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-recongition-models
+for a list of available models. The following is an example
+
+  wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-recongition-models/3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx
+
+Step 3. Download test wave files
+
+Please visit https://github.com/k2-fsa/sherpa-onnx/releases/tag/speaker-segmentation-models
+for a list of available test wave files. The following is an example
+
+  wget https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/0-four-speakers-zh.wav
+
+Step 4. Run it
+
+    python3 ./python-api-examples/offline-speaker-diarization.py
+
+"""
+from pathlib import Path
+
+import sherpa_onnx
+import soundfile as sf
+
+
+def init_speaker_diarization(num_speakers: int = -1, cluster_threshold: float = 0.5):
+    """
+    Args:
+      num_speakers:
+        If you know the actual number of speakers in the wave file, then please
+        specify it. Otherwise, leave it to -1
+      cluster_threshold:
+        If num_speakers is -1, then this threshold is used for clustering.
+        A smaller cluster_threshold leads to more clusters, i.e., more speakers.
+        A larger cluster_threshold leads to fewer clusters, i.e., fewer speakers.
+    """
+    segmentation_model = "./sherpa-onnx-pyannote-segmentation-3-0/model.onnx"
+    embedding_extractor_model = (
+        "./3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx"
+    )
+
+    config = sherpa_onnx.OfflineSpeakerDiarizationConfig(
+        segmentation=sherpa_onnx.OfflineSpeakerSegmentationModelConfig(
+            pyannote=sherpa_onnx.OfflineSpeakerSegmentationPyannoteModelConfig(
+                model=segmentation_model
+            ),
+        ),
+        embedding=sherpa_onnx.SpeakerEmbeddingExtractorConfig(
+            model=embedding_extractor_model
+        ),
+        clustering=sherpa_onnx.FastClusteringConfig(
+            num_clusters=num_speakers, threshold=cluster_threshold
+        ),
+        min_duration_on=0.3,
+        min_duration_off=0.5,
+    )
+    if not config.validate():
+        raise RuntimeError(
+            "Please check your config and make sure all required files exist"
+        )
+
+    return sherpa_onnx.OfflineSpeakerDiarization(config)
+
+
+def progress_callback(num_processed_chunk: int, num_total_chunks: int) -> int:
+    progress = num_processed_chunk / num_total_chunks * 100
+    print(f"Progress: {progress:.3f}%")
+    return 0
+
+
+def main():
+    wave_filename = "./0-four-speakers-zh.wav"
+    if not Path(wave_filename).is_file():
+        raise RuntimeError(f"{wave_filename} does not exist")
+
+    audio, sample_rate = sf.read(wave_filename, dtype="float32", always_2d=True)
+    audio = audio[:, 0]  # only use the first channel
+
+    # Since we know there are 4 speakers in the above test wave file, we use
+    # num_speakers 4 here
+    sd = init_speaker_diarization(num_speakers=4)
+    if sample_rate != sd.sample_rate:
+        raise RuntimeError(
+            f"Expected samples rate: {sd.sample_rate}, given: {sample_rate}"
+        )
+
+    show_porgress = True
+
+    if show_porgress:
+        result = sd.process(audio, callback=progress_callback).sort_by_start_time()
+    else:
+        result = sd.process(audio).sort_by_start_time()
+
+    for r in result:
+        print(f"{r.start:.3f} -- {r.end:.3f} speaker_{r.speaker:02}")
+        #  print(r) # this one is simpler
+
+
+if __name__ == "__main__":
+    main()

sherpa-onnx/csrc/offline-speaker-diarization-pyannote-impl.h

@@ -103,7 +103,7 @@ class OfflineSpeakerDiarizationPyannoteImpl
     auto chunk_speaker_samples_list_pair = GetChunkSpeakerSampleIndexes(labels);
     Matrix2D embeddings =
         ComputeEmbeddings(audio, n, chunk_speaker_samples_list_pair.second,
-                          callback, callback_arg);
+                          std::move(callback), callback_arg);
 
     std::vector<int32_t> cluster_labels = clustering_.Cluster(
         &embeddings(0, 0), embeddings.rows(), embeddings.cols());

sherpa-onnx/csrc/offline-speaker-diarization-result.h

@@ -28,6 +28,8 @@ class OfflineSpeakerDiarizationSegment {
   const std::string &Text() const { return text_; }
   float Duration() const { return end_ - start_; }
 
+  void SetText(const std::string &text) { text_ = text; }
+
   std::string ToString() const;
 
  private:

sherpa-onnx/csrc/offline-speaker-diarization.h

@@ -34,10 +34,13 @@ struct OfflineSpeakerDiarizationConfig {
   OfflineSpeakerDiarizationConfig(
       const OfflineSpeakerSegmentationModelConfig &segmentation,
       const SpeakerEmbeddingExtractorConfig &embedding,
-      const FastClusteringConfig &clustering)
+      const FastClusteringConfig &clustering, float min_duration_on,
+      float min_duration_off)
       : segmentation(segmentation),
         embedding(embedding),
-        clustering(clustering) {}
+        clustering(clustering),
+        min_duration_on(min_duration_on),
+        min_duration_off(min_duration_off) {}
 
   void Register(ParseOptions *po);
   bool Validate() const;

sherpa-onnx/python/csrc/CMakeLists.txt

@@ -62,6 +62,8 @@ endif()
 if(SHERPA_ONNX_ENABLE_SPEAKER_DIARIZATION)
   list(APPEND srcs
     fast-clustering.cc
+    offline-speaker-diarization-result.cc
+    offline-speaker-diarization.cc
   )
 endif()
 

sherpa-onnx/python/csrc/offline-speaker-diarization-result.cc

@@ -0,0 +1,32 @@
+// sherpa-onnx/python/csrc/offline-speaker-diarization-result.cc
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#include "sherpa-onnx/python/csrc/offline-speaker-diarization-result.h"
+
+#include "sherpa-onnx/csrc/offline-speaker-diarization-result.h"
+
+namespace sherpa_onnx {
+
+static void PybindOfflineSpeakerDiarizationSegment(py::module *m) {
+  using PyClass = OfflineSpeakerDiarizationSegment;
+  py::class_<PyClass>(*m, "OfflineSpeakerDiarizationSegment")
+      .def_property_readonly("start", &PyClass::Start)
+      .def_property_readonly("end", &PyClass::End)
+      .def_property_readonly("duration", &PyClass::Duration)
+      .def_property_readonly("speaker", &PyClass::Speaker)
+      .def_property("text", &PyClass::Text, &PyClass::SetText)
+      .def("__str__", &PyClass::ToString);
+}
+
+void PybindOfflineSpeakerDiarizationResult(py::module *m) {
+  PybindOfflineSpeakerDiarizationSegment(m);
+  using PyClass = OfflineSpeakerDiarizationResult;
+  py::class_<PyClass>(*m, "OfflineSpeakerDiarizationResult")
+      .def_property_readonly("num_speakers", &PyClass::NumSpeakers)
+      .def_property_readonly("num_segments", &PyClass::NumSegments)
+      .def("sort_by_start_time", &PyClass::SortByStartTime)
+      .def("sort_by_speaker", &PyClass::SortBySpeaker);
+}
+
+}  // namespace sherpa_onnx

sherpa-onnx/python/csrc/offline-speaker-diarization-result.h

@@ -0,0 +1,16 @@
+// sherpa-onnx/python/csrc/offline-speaker-diarization-result.h
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_OFFLINE_SPEAKER_DIARIZATION_RESULT_H_
+#define SHERPA_ONNX_PYTHON_CSRC_OFFLINE_SPEAKER_DIARIZATION_RESULT_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineSpeakerDiarizationResult(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_OFFLINE_SPEAKER_DIARIZATION_RESULT_H_

sherpa-onnx/python/csrc/offline-speaker-diarization.cc

@@ -0,0 +1,92 @@
+// sherpa-onnx/python/csrc/offline-speaker-diarization.cc
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#include "sherpa-onnx/python/csrc/offline-speaker-diarization.h"
+
+#include <string>
+#include <vector>
+
+#include "sherpa-onnx/csrc/offline-speaker-diarization.h"
+#include "sherpa-onnx/csrc/offline-speaker-segmentation-model-config.h"
+#include "sherpa-onnx/csrc/offline-speaker-segmentation-pyannote-model-config.h"
+
+namespace sherpa_onnx {
+
+static void PybindOfflineSpeakerSegmentationPyannoteModelConfig(py::module *m) {
+  using PyClass = OfflineSpeakerSegmentationPyannoteModelConfig;
+  py::class_<PyClass>(*m, "OfflineSpeakerSegmentationPyannoteModelConfig")
+      .def(py::init<>())
+      .def(py::init<const std::string &>(), py::arg("model"))
+      .def_readwrite("model", &PyClass::model)
+      .def("__str__", &PyClass::ToString)
+      .def("validate", &PyClass::Validate);
+}
+
+static void PybindOfflineSpeakerSegmentationModelConfig(py::module *m) {
+  PybindOfflineSpeakerSegmentationPyannoteModelConfig(m);
+
+  using PyClass = OfflineSpeakerSegmentationModelConfig;
+  py::class_<PyClass>(*m, "OfflineSpeakerSegmentationModelConfig")
+      .def(py::init<>())
+      .def(py::init<const OfflineSpeakerSegmentationPyannoteModelConfig &,
+                    int32_t, bool, const std::string &>(),
+           py::arg("pyannote"), py::arg("num_threads") = 1,
+           py::arg("debug") = false, py::arg("provider") = "cpu")
+      .def_readwrite("pyannote", &PyClass::pyannote)
+      .def_readwrite("num_threads", &PyClass::num_threads)
+      .def_readwrite("debug", &PyClass::debug)
+      .def_readwrite("provider", &PyClass::provider)
+      .def("__str__", &PyClass::ToString)
+      .def("validate", &PyClass::Validate);
+}
+
+static void PybindOfflineSpeakerDiarizationConfig(py::module *m) {
+  PybindOfflineSpeakerSegmentationModelConfig(m);
+
+  using PyClass = OfflineSpeakerDiarizationConfig;
+  py::class_<PyClass>(*m, "OfflineSpeakerDiarizationConfig")
+      .def(py::init<const OfflineSpeakerSegmentationModelConfig &,
+                    const SpeakerEmbeddingExtractorConfig &,
+                    const FastClusteringConfig &, float, float>(),
+           py::arg("segmentation"), py::arg("embedding"), py::arg("clustering"),
+           py::arg("min_duration_on") = 0.3, py::arg("min_duration_off") = 0.5)
+      .def_readwrite("segmentation", &PyClass::segmentation)
+      .def_readwrite("embedding", &PyClass::embedding)
+      .def_readwrite("clustering", &PyClass::clustering)
+      .def_readwrite("min_duration_on", &PyClass::min_duration_on)
+      .def_readwrite("min_duration_off", &PyClass::min_duration_off)
+      .def("__str__", &PyClass::ToString)
+      .def("validate", &PyClass::Validate);
+}
+
+void PybindOfflineSpeakerDiarization(py::module *m) {
+  PybindOfflineSpeakerDiarizationConfig(m);
+
+  using PyClass = OfflineSpeakerDiarization;
+  py::class_<PyClass>(*m, "OfflineSpeakerDiarization")
+      .def(py::init<const OfflineSpeakerDiarizationConfig &>(),
+           py::arg("config"))
+      .def_property_readonly("sample_rate", &PyClass::SampleRate)
+      .def(
+          "process",
+          [](const PyClass &self, const std::vector<float> samples,
+             std::function<int32_t(int32_t, int32_t)> callback) {
+            if (!callback) {
+              return self.Process(samples.data(), samples.size());
+            }
+
+            std::function<int32_t(int32_t, int32_t, void *)> callback_wrapper =
+                [callback](int32_t processed_chunks, int32_t num_chunks,
+                           void *) -> int32_t {
+              callback(processed_chunks, num_chunks);
+              return 0;
+            };
+
+            return self.Process(samples.data(), samples.size(),
+                                callback_wrapper);
+          },
+          py::arg("samples"), py::arg("callback") = py::none());
+}
+
+}  // namespace sherpa_onnx

sherpa-onnx/python/csrc/offline-speaker-diarization.h

@@ -0,0 +1,16 @@
+// sherpa-onnx/python/csrc/offline-speaker-diarization.h
+//
+// Copyright (c)  2024  Xiaomi Corporation
+
+#ifndef SHERPA_ONNX_PYTHON_CSRC_OFFLINE_SPEAKER_DIARIZATION_H_
+#define SHERPA_ONNX_PYTHON_CSRC_OFFLINE_SPEAKER_DIARIZATION_H_
+
+#include "sherpa-onnx/python/csrc/sherpa-onnx.h"
+
+namespace sherpa_onnx {
+
+void PybindOfflineSpeakerDiarization(py::module *m);
+
+}
+
+#endif  // SHERPA_ONNX_PYTHON_CSRC_OFFLINE_SPEAKER_DIARIZATION_H_

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

@@ -37,6 +37,8 @@
 
 #if SHERPA_ONNX_ENABLE_SPEAKER_DIARIZATION == 1
 #include "sherpa-onnx/python/csrc/fast-clustering.h"
+#include "sherpa-onnx/python/csrc/offline-speaker-diarization-result.h"
+#include "sherpa-onnx/python/csrc/offline-speaker-diarization.h"
 #endif
 
 namespace sherpa_onnx {
@@ -74,14 +76,16 @@ PYBIND11_MODULE(_sherpa_onnx, m) {
   PybindOfflineTts(&m);
 #endif
 
-#if SHERPA_ONNX_ENABLE_SPEAKER_DIARIZATION == 1
-  PybindFastClustering(&m);
-#endif
-
   PybindSpeakerEmbeddingExtractor(&m);
   PybindSpeakerEmbeddingManager(&m);
   PybindSpokenLanguageIdentification(&m);
 
+#if SHERPA_ONNX_ENABLE_SPEAKER_DIARIZATION == 1
+  PybindFastClustering(&m);
+  PybindOfflineSpeakerDiarizationResult(&m);
+  PybindOfflineSpeakerDiarization(&m);
+#endif
+
   PybindAlsa(&m);
 }