Add hotwords support to Swift/Go/Python/C#/Kotlin APIs

CMakeLists.txt

@@ -1,7 +1,7 @@
 cmake_minimum_required(VERSION 3.13 FATAL_ERROR)
 project(sherpa-ncnn)
 
-set(SHERPA_NCNN_VERSION "2.0.7")
+set(SHERPA_NCNN_VERSION "2.1.0")
 
 # Disable warning about
 #

android/SherpaNcnn/app/src/main/java/com/k2fsa/sherpa/ncnn/SherpaNcnn.kt

@@ -33,6 +33,8 @@ data class RecognizerConfig(
     var rule1MinTrailingSilence: Float = 2.4f,
     var rule2MinTrailingSilence: Float = 1.0f,
     var rule3MinUtteranceLength: Float = 30.0f,
+    var hotwordsFile: String = "",
+    var hotwordsScore: Float = 1.5f,
 )
 
 class SherpaNcnn(

c-api-examples/decode-file-c-api.c

@@ -45,6 +45,8 @@ int32_t main(int32_t argc, char *argv[]) {
     return -1;
   }
   SherpaNcnnRecognizerConfig config;
+  memset(&config, 0, sizeof(config));
+
   config.model_config.tokens = argv[1];
   config.model_config.encoder_param = argv[2];
   config.model_config.encoder_bin = argv[3];
@@ -57,6 +59,7 @@ int32_t main(int32_t argc, char *argv[]) {
   if (argc >= 10 && atoi(argv[9]) > 0) {
     num_threads = atoi(argv[9]);
   }
+
   config.model_config.num_threads = num_threads;
   config.model_config.use_vulkan_compute = 0;
 
@@ -65,6 +68,7 @@ int32_t main(int32_t argc, char *argv[]) {
   if (argc >= 11) {
     config.decoder_config.decoding_method = argv[10];
   }
+
   config.decoder_config.num_active_paths = 4;
   config.enable_endpoint = 0;
   config.rule1_min_trailing_silence = 2.4;
@@ -73,16 +77,14 @@ int32_t main(int32_t argc, char *argv[]) {
 
   config.feat_config.sampling_rate = 16000;
   config.feat_config.feature_dim = 80;
-  if(argc >= 12) {
+  if (argc >= 12) {
     config.hotwords_file = argv[11];
-  } else {
-    config.hotwords_file = "";
   }
-  if(argc == 13) {
+
+  if (argc == 13) {
     config.hotwords_score = atof(argv[12]);
-  } else {
-    config.hotwords_score = 1.5;
   }
+
   SherpaNcnnRecognizer *recognizer = CreateRecognizer(&config);
 
   const char *wav_filename = argv[8];

scripts/dotnet/sherpa-ncnn.cs

@@ -63,6 +63,11 @@ public struct OnlineRecognizerConfig
         public float Rule1MinTrailingSilence;
         public float Rule2MinTrailingSilence;
         public float Rule3MinUtteranceLength;
+
+        [MarshalAs(UnmanagedType.LPStr)]
+        public string HotwordsFile;
+
+        public float HotwordsScore;
     }
 
     // please see

scripts/go/sherpa_ncnn.go

@@ -84,6 +84,9 @@ type RecognizerConfig struct {
 	Rule1MinTrailingSilence float32
 	Rule2MinTrailingSilence float32
 	Rule3MinUtteranceLength float32
+
+	HotwordsFile  string
+	HotwordsScore float32
 }
 
 // It contains the recognition result for a online stream.
@@ -148,6 +151,11 @@ func NewRecognizer(config *RecognizerConfig) *Recognizer {
 	c.rule2_min_trailing_silence = C.float(config.Rule2MinTrailingSilence)
 	c.rule3_min_utterance_length = C.float(config.Rule3MinUtteranceLength)
 
+	c.hotwords_file = C.CString(config.HotwordsFile)
+	defer C.free(unsafe.Pointer(c.hotwords_file))
+
+	c.hotwords_score = C.float(config.HotwordsScore)
+
 	recognizer := &Recognizer{}
 	recognizer.impl = C.CreateRecognizer(&c)
 

sherpa-ncnn/c-api/c-api.cc

@@ -39,6 +39,8 @@ struct SherpaNcnnDisplay {
   std::unique_ptr<sherpa_ncnn::Display> impl;
 };
 
+#define SHERPA_NCNN_OR(x, y) (x ? x : y)
+
 SherpaNcnnRecognizer *CreateRecognizer(
     const SherpaNcnnRecognizerConfig *in_config) {
   // model_config
@@ -56,7 +58,7 @@ SherpaNcnnRecognizer *CreateRecognizer(
   config.model_config.use_vulkan_compute =
       in_config->model_config.use_vulkan_compute;
 
-  int32_t num_threads = in_config->model_config.num_threads;
+  int32_t num_threads = SHERPA_NCNN_OR(in_config->model_config.num_threads, 1);
 
   config.model_config.encoder_opt.num_threads = num_threads;
   config.model_config.decoder_opt.num_threads = num_threads;
@@ -66,8 +68,9 @@ SherpaNcnnRecognizer *CreateRecognizer(
   config.decoder_config.method = in_config->decoder_config.decoding_method;
   config.decoder_config.num_active_paths =
       in_config->decoder_config.num_active_paths;
-  config.hotwords_file = in_config->hotwords_file;
-  config.hotwords_score = in_config->hotwords_score;
+
+  config.hotwords_file = SHERPA_NCNN_OR(in_config->hotwords_file, "");
+  config.hotwords_score = SHERPA_NCNN_OR(in_config->hotwords_score, 1.5);
 
   config.enable_endpoint = in_config->enable_endpoint;
 
@@ -80,11 +83,17 @@ SherpaNcnnRecognizer *CreateRecognizer(
   config.endpoint_config.rule3.min_utterance_length =
       in_config->rule3_min_utterance_length;
 
-  config.feat_config.sampling_rate = in_config->feat_config.sampling_rate;
-  config.feat_config.feature_dim = in_config->feat_config.feature_dim;
+  config.feat_config.sampling_rate =
+      SHERPA_NCNN_OR(in_config->feat_config.sampling_rate, 16000);
+
+  config.feat_config.feature_dim =
+      SHERPA_NCNN_OR(in_config->feat_config.feature_dim, 80);
 
   auto recognizer = std::make_unique<sherpa_ncnn::Recognizer>(config);
+
   if (!recognizer->GetModel()) {
+    NCNN_LOGE("Failed to create the recognizer! Please check your config: %s",
+              config.ToString().c_str());
     return nullptr;
   }
 

sherpa-ncnn/c-api/c-api.h

@@ -133,10 +133,10 @@ SHERPA_NCNN_API typedef struct SherpaNcnnRecognizerConfig {
   /// this value.
   /// Used only when enable_endpoint is not 0.
   float rule3_min_utterance_length;
-  
-  /// hotwords file, each line is a hotword which is segmented into char by space
-  /// if language is something like CJK, segment manually,
-  /// if language is something like English, segment by bpe model.
+
+  /// hotwords file, each line is a hotword which is segmented into char by
+  /// space if language is something like CJK, segment manually, if language is
+  /// something like English, segment by bpe model.
   const char *hotwords_file;
 
   /// scale of hotwords, used only when hotwords_file is not empty

sherpa-ncnn/csrc/decoder.h

@@ -59,7 +59,9 @@ struct DecoderResult {
   // used only for modified_beam_search
   Hypotheses hyps;
 };
+
 class Stream;
+
 class Decoder {
  public:
   virtual ~Decoder() = default;
@@ -88,7 +90,11 @@ class Decoder {
    * and there are no paddings.
    */
   virtual void Decode(ncnn::Mat encoder_out, DecoderResult *result) = 0;
-  virtual void Decode(ncnn::Mat encoder_out, Stream *s, DecoderResult *result){};
+
+  virtual void Decode(ncnn::Mat encoder_out, Stream *s, DecoderResult *result) {
+    NCNN_LOGE("Please override it!");
+    exit(-1);
+  }
 };
 
 }  // namespace sherpa_ncnn

sherpa-ncnn/csrc/hypothesis.h

@@ -24,6 +24,7 @@
 #include <unordered_map>
 #include <utility>
 #include <vector>
+
 #include "sherpa-ncnn/csrc/context-graph.h"
 
 namespace sherpa_ncnn {
@@ -43,7 +44,7 @@ struct Hypothesis {
 
   Hypothesis() = default;
   Hypothesis(const std::vector<int32_t> &ys, double log_prob,
-            const ContextState *context_state = nullptr)
+             const ContextState *context_state = nullptr)
       : ys(ys), log_prob(log_prob), context_state(context_state) {}
 
   // If two Hypotheses have the same `Key`, then they contain

sherpa-ncnn/csrc/modified-beam-search-decoder.cc

@@ -195,7 +195,6 @@ void ModifiedBeamSearchDecoder::Decode(ncnn::Mat encoder_out,
   result->num_trailing_blanks = hyp.num_trailing_blanks;
 }
 
-
 void ModifiedBeamSearchDecoder::Decode(ncnn::Mat encoder_out, Stream *s,
                                        DecoderResult *result) {
   int32_t context_size = model_->ContextSize();
@@ -205,7 +204,6 @@ void ModifiedBeamSearchDecoder::Decode(ncnn::Mat encoder_out, Stream *s,
     std::vector<Hypothesis> prev = cur.GetTopK(num_active_paths_, true);
     cur.Clear();
 
-
     ncnn::Mat decoder_input = BuildDecoderInput(prev);
     ncnn::Mat decoder_out;
     if (t == 0 && prev.size() == 1 && prev[0].ys.size() == context_size &&
@@ -218,14 +216,13 @@ void ModifiedBeamSearchDecoder::Decode(ncnn::Mat encoder_out, Stream *s,
 
     // decoder_out.w == decoder_dim
     // decoder_out.h == num_active_paths
-	ncnn::Mat encoder_out_t(encoder_out.w, 1, encoder_out.row(t));
+    ncnn::Mat encoder_out_t(encoder_out.w, 1, encoder_out.row(t));
 
     ncnn::Mat joiner_out = model_->RunJoiner(encoder_out_t, decoder_out);
     // joiner_out.w == vocab_size
     // joiner_out.h == num_active_paths
     LogSoftmax(&joiner_out);
 
-
     float *p_joiner_out = joiner_out;
 
     for (int32_t i = 0; i != joiner_out.h; ++i) {
@@ -255,8 +252,8 @@ void ModifiedBeamSearchDecoder::Decode(ncnn::Mat encoder_out, Stream *s,
         new_hyp.num_trailing_blanks = 0;
         new_hyp.timestamps.push_back(t + frame_offset);
         if (s != nullptr && s->GetContextGraph() != nullptr) {
-          auto context_res = s->GetContextGraph()->ForwardOneStep(
-              context_state, new_token);
+          auto context_res =
+              s->GetContextGraph()->ForwardOneStep(context_state, new_token);
           context_score = context_res.first;
           new_hyp.context_state = context_res.second;
         }