Go API for speaker diarization (#1403)

go-api-examples/non-streaming-speaker-diarization/go.mod

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+module non-streaming-speaker-diarization
+
+go 1.12

go-api-examples/non-streaming-speaker-diarization/main.go

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+package main
+
+import (
+	sherpa "github.com/k2-fsa/sherpa-onnx-go/sherpa_onnx"
+	"log"
+)
+
+/*
+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
+*/
+
+func initSpeakerDiarization() *sherpa.OfflineSpeakerDiarization {
+	config := sherpa.OfflineSpeakerDiarizationConfig{}
+
+	config.Segmentation.Pyannote.Model = "./sherpa-onnx-pyannote-segmentation-3-0/model.onnx"
+	config.Embedding.Model = "./3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx"
+
+	// The test wave file contains 4 speakers, so we use 4 here
+	config.Clustering.NumClusters = 4
+
+	// if you don't know the actual numbers in the wave file,
+	// then please don't set NumClusters; you need to use
+	//
+	// config.Clustering.Threshold = 0.5
+	//
+
+	// A larger Threshold leads to fewer clusters
+	// A smaller Threshold leads to more clusters
+
+	sd := sherpa.NewOfflineSpeakerDiarization(&config)
+	return sd
+}
+
+func main() {
+	wave_filename := "./0-four-speakers-zh.wav"
+	wave := sherpa.ReadWave(wave_filename)
+	if wave == nil {
+		log.Printf("Failed to read %v", wave_filename)
+		return
+	}
+
+	sd := initSpeakerDiarization()
+	if sd == nil {
+		log.Printf("Please check your config")
+		return
+	}
+
+	defer sherpa.DeleteOfflineSpeakerDiarization(sd)
+
+	if wave.SampleRate != sd.SampleRate() {
+		log.Printf("Expected sample rate: %v, given: %d\n", sd.SampleRate(), wave.SampleRate)
+		return
+	}
+
+	log.Println("Started")
+	segments := sd.Process(wave.Samples)
+	n := len(segments)
+
+	for i := 0; i < n; i++ {
+		log.Printf("%.3f -- %.3f speaker_%02d\n", segments[i].Start, segments[i].End, segments[i].Speaker)
+	}
+}

go-api-examples/non-streaming-speaker-diarization/run.sh

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+#!/usr/bin/env bash
+
+
+if [ ! -f ./sherpa-onnx-pyannote-segmentation-3-0/model.onnx ]; then
+  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
+fi
+
+if [ ! -f ./3dspeaker_speech_eres2net_base_sv_zh-cn_3dspeaker_16k.onnx ]; then
+  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
+fi
+
+if [ ! -f ./0-four-speakers-zh.wav ]; then
+  curl -SL -O https://github.com/k2-fsa/sherpa-onnx/releases/download/speaker-segmentation-models/0-four-speakers-zh.wav
+fi
+
+go mod tidy
+go build
+./non-streaming-speaker-diarization