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Unity使用sherpa-onnx实现关键词检测

news 2026/3/26 18:29:57

使用模型 sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01

效果图

具体代码

using uMicrophoneWebGL; using UnityEngine; [RequireComponent(typeof(MicrophoneWebGL))] public class KeywordSpottingSample : MonoBehaviour { MicrophoneWebGL microphone; public KeywordSpotting keywordSpotting; // Start is called before the first frame update void Start() { keywordSpotting.Init(); microphone = GetComponent<MicrophoneWebGL>(); microphone.dataEvent.AddListener(OnAudioData); } public void OnAudioData(float[] data) { if (keywordSpotting != null) { keywordSpotting.AcceptData(data); } } float timer = 0f; float interval = 0.2f; string keyword; private void Update() { if (keywordSpotting != null && keywordSpotting.initDone) { timer += Time.deltaTime; if (timer >= interval) { keyword = keywordSpotting.Recognize(); if (!string.IsNullOrEmpty(keyword)) { Debug.Log("keyword:" + keyword); } timer = 0f; } } } }

using System.IO; using SherpaOnnx; using UnityEngine; /// <summary> /// 关键字识别 /// </summary> public class KeywordSpotting : MonoBehaviour { KeywordSpotter keywordSpotter; string pathRoot; string modelPath; OnlineStream onlineStream; int sampleRate = 16000; public bool initDone = false; public void Init() { pathRoot = Util.GetPath() + "/models"; //需要将此文件夹拷贝到models modelPath = pathRoot + "/sherpa-onnx-kws-zipformer-wenetspeech-3.3M-2024-01-01"; KeywordSpotterConfig config = new KeywordSpotterConfig(); config.FeatConfig.SampleRate = 16000; config.FeatConfig.FeatureDim = 80; config.ModelConfig.Transducer.Encoder = Path.Combine(modelPath, "encoder-epoch-12-avg-2-chunk-16-left-64.onnx"); config.ModelConfig.Transducer.Decoder = Path.Combine(modelPath, "decoder-epoch-12-avg-2-chunk-16-left-64.onnx"); config.ModelConfig.Transducer.Joiner = Path.Combine(modelPath, "joiner-epoch-12-avg-2-chunk-16-left-64.onnx"); config.ModelConfig.Tokens = Path.Combine(modelPath, "tokens.txt"); config.ModelConfig.Provider = "cpu"; config.ModelConfig.NumThreads = 1; config.ModelConfig.Debug = 0; config.KeywordsFile = Path.Combine(modelPath, "keywords.txt"); keywordSpotter = new KeywordSpotter(config); onlineStream = keywordSpotter.CreateStream(); initDone = true; } public void AcceptData(float[] data) { onlineStream.AcceptWaveform(sampleRate, data); } KeywordResult result; public string Recognize() { while (keywordSpotter.IsReady(onlineStream)) { keywordSpotter.Decode(onlineStream); result = keywordSpotter.GetResult(onlineStream); if (result.Keyword != string.Empty) { Debug.Log("关键字: " + result.Keyword); // Remember to call Reset() right after detecting a keyword keywordSpotter.Reset(onlineStream); return result.Keyword; } } return string.Empty; } }