package main import ( "fmt" "github.com/gen2brain/malgo" sherpa "github.com/k2-fsa/sherpa-onnx-go/sherpa_onnx" flag "github.com/spf13/pflag" "log" "strings" ) func initRecognizer() *sherpa.OnlineRecognizer { config := sherpa.OnlineRecognizerConfig{} config.FeatConfig = sherpa.FeatureConfig{SampleRate: 16000, FeatureDim: 80} flag.StringVar(&config.ModelConfig.Transducer.Encoder, "encoder", "", "Path to the transducer encoder model") flag.StringVar(&config.ModelConfig.Transducer.Decoder, "decoder", "", "Path to the transducer decoder model") flag.StringVar(&config.ModelConfig.Transducer.Joiner, "joiner", "", "Path to the transducer joiner model") flag.StringVar(&config.ModelConfig.Paraformer.Encoder, "paraformer-encoder", "", "Path to the paraformer encoder model") flag.StringVar(&config.ModelConfig.Paraformer.Decoder, "paraformer-decoder", "", "Path to the paraformer decoder model") flag.StringVar(&config.ModelConfig.Tokens, "tokens", "", "Path to the tokens file") flag.IntVar(&config.ModelConfig.NumThreads, "num-threads", 1, "Number of threads for computing") flag.IntVar(&config.ModelConfig.Debug, "debug", 0, "Whether to show debug message") flag.StringVar(&config.ModelConfig.ModelType, "model-type", "", "Optional. Used for loading the model in a faster way") flag.StringVar(&config.ModelConfig.Provider, "provider", "cpu", "Provider to use") flag.StringVar(&config.DecodingMethod, "decoding-method", "greedy_search", "Decoding method. Possible values: greedy_search, modified_beam_search") flag.IntVar(&config.MaxActivePaths, "max-active-paths", 4, "Used only when --decoding-method is modified_beam_search") flag.IntVar(&config.EnableEndpoint, "enable-endpoint", 1, "Whether to enable endpoint") flag.Float32Var(&config.Rule1MinTrailingSilence, "rule1-min-trailing-silence", 2.4, "Threshold for rule1") flag.Float32Var(&config.Rule2MinTrailingSilence, "rule2-min-trailing-silence", 1.2, "Threshold for rule2") flag.Float32Var(&config.Rule3MinUtteranceLength, "rule3-min-utterance-length", 20, "Threshold for rule3") flag.Parse() log.Println("Initializing recognizer (may take several seconds)") recognizer := sherpa.NewOnlineRecognizer(&config) log.Println("Recognizer created!") return recognizer } func main() { ctx, err := malgo.InitContext(nil, malgo.ContextConfig{}, func(message string) { fmt.Printf("LOG <%v>", message) }) chk(err) defer func() { _ = ctx.Uninit() ctx.Free() }() deviceConfig := malgo.DefaultDeviceConfig(malgo.Duplex) deviceConfig.Capture.Format = malgo.FormatS16 deviceConfig.Capture.Channels = 1 deviceConfig.Playback.Format = malgo.FormatS16 deviceConfig.Playback.Channels = 1 deviceConfig.SampleRate = 16000 deviceConfig.Alsa.NoMMap = 1 recognizer := initRecognizer() defer sherpa.DeleteOnlineRecognizer(recognizer) stream := sherpa.NewOnlineStream(recognizer) defer sherpa.DeleteOnlineStream(stream) var last_text string segment_idx := 0 onRecvFrames := func(_, pSample []byte, framecount uint32) { samples := samplesInt16ToFloat(pSample) stream.AcceptWaveform(16000, samples) // Please use a separate goroutine for decoding in your app for recognizer.IsReady(stream) { recognizer.Decode(stream) } text := recognizer.GetResult(stream).Text if len(text) != 0 && last_text != text { last_text = strings.ToLower(text) fmt.Printf("\r%d: %s", segment_idx, last_text) } if recognizer.IsEndpoint(stream) { if len(text) != 0 { segment_idx++ fmt.Println() } recognizer.Reset(stream) } } captureCallbacks := malgo.DeviceCallbacks{ Data: onRecvFrames, } device, err := malgo.InitDevice(ctx.Context, deviceConfig, captureCallbacks) chk(err) err = device.Start() chk(err) fmt.Println("Started. Please speak. Press ctrl + C to exit") fmt.Scanln() device.Uninit() } func chk(err error) { if err != nil { panic(err) } } func samplesInt16ToFloat(inSamples []byte) []float32 { numSamples := len(inSamples) / 2 outSamples := make([]float32, numSamples) for i := 0; i != numSamples; i++ { // Decode two bytes into an int16 using bit manipulation s16 := int16(inSamples[2*i]) | int16(inSamples[2*i+1])<<8 outSamples[i] = float32(s16) / 32768 } return outSamples }