Add tokenize-hotwords option

sherpa-onnx/csrc/offline-recognizer-transducer-impl.h

@@ -157,7 +157,8 @@ class OfflineRecognizerTransducerImpl : public OfflineRecognizerImpl {
     std::vector<std::vector<int32_t>> current;
     std::vector<float> current_scores;
     if (!EncodeHotwords(is, config_.model_config.modeling_unit, symbol_table_,
-                        bpe_encoder_.get(), &current, &current_scores)) {
+                        config_.tokenize_hotwords, bpe_encoder_.get(), &current,
+                        &current_scores)) {
       SHERPA_ONNX_LOGE("Encode hotwords failed, skipping, hotwords are : %s",
                        hotwords.c_str());
     }
@@ -257,7 +258,8 @@ class OfflineRecognizerTransducerImpl : public OfflineRecognizerImpl {
     }
 
     if (!EncodeHotwords(is, config_.model_config.modeling_unit, symbol_table_,
-                        bpe_encoder_.get(), &hotwords_, &boost_scores_)) {
+                        config_.tokenize_hotwords, bpe_encoder_.get(),
+                        &hotwords_, &boost_scores_)) {
       SHERPA_ONNX_LOGE(
           "Failed to encode some hotwords, skip them already, see logs above "
           "for details.");
@@ -281,7 +283,8 @@ class OfflineRecognizerTransducerImpl : public OfflineRecognizerImpl {
     }
 
     if (!EncodeHotwords(is, config_.model_config.modeling_unit, symbol_table_,
-                        bpe_encoder_.get(), &hotwords_, &boost_scores_)) {
+                        config_.tokenize_hotwords, bpe_encoder_.get(),
+                        &hotwords_, &boost_scores_)) {
       SHERPA_ONNX_LOGE(
           "Failed to encode some hotwords, skip them already, see logs above "
           "for details.");

sherpa-onnx/csrc/offline-recognizer.cc

@@ -45,6 +45,11 @@ void OfflineRecognizerConfig::Register(ParseOptions *po) {
                "The bonus score for each token in context word/phrase. "
                "Used only when decoding_method is modified_beam_search");
 
+  po->Register(
+      "tokenize-hotwords", &tokenize_hotwords,
+      "Whether to tokenize hotwords, default true, if false the input hotwords "
+      "should be tokenized into tokens");
+
   po->Register(
       "rule-fsts", &rule_fsts,
       "If not empty, it specifies fsts for inverse text normalization. "
@@ -125,6 +130,7 @@ std::string OfflineRecognizerConfig::ToString() const {
   os << "max_active_paths=" << max_active_paths << ", ";
   os << "hotwords_file=\"" << hotwords_file << "\", ";
   os << "hotwords_score=" << hotwords_score << ", ";
+  os << "tokenize_hotwords=" << (tokenize_hotwords ? "True" : "False") << ", ";
   os << "blank_penalty=" << blank_penalty << ", ";
   os << "rule_fsts=\"" << rule_fsts << "\", ";
   os << "rule_fars=\"" << rule_fars << "\")";

sherpa-onnx/csrc/offline-recognizer.h

@@ -37,6 +37,9 @@ struct OfflineRecognizerConfig {
 
   std::string hotwords_file;
   float hotwords_score = 1.5;
+  /// Whether to tokenize the input hotwords, normally should be true
+  /// if false, you have to tokenize hotwords by yourself.
+  bool tokenize_hotwords = true;
 
   float blank_penalty = 0.0;
 
@@ -56,7 +59,7 @@ struct OfflineRecognizerConfig {
       const OfflineCtcFstDecoderConfig &ctc_fst_decoder_config,
       const std::string &decoding_method, int32_t max_active_paths,
       const std::string &hotwords_file, float hotwords_score,
-      float blank_penalty, const std::string &rule_fsts,
+      bool tokenize_hotwords, float blank_penalty, const std::string &rule_fsts,
       const std::string &rule_fars)
       : feat_config(feat_config),
         model_config(model_config),
@@ -66,6 +69,7 @@ struct OfflineRecognizerConfig {
         max_active_paths(max_active_paths),
         hotwords_file(hotwords_file),
         hotwords_score(hotwords_score),
+        tokenize_hotwords(tokenize_hotwords),
         blank_penalty(blank_penalty),
         rule_fsts(rule_fsts),
         rule_fars(rule_fars) {}
@@ -94,9 +98,10 @@ class OfflineRecognizer {
   /** Create a stream for decoding.
    *
    *  @param The hotwords for this string, it might contain several hotwords,
-   *         the hotwords are separated by "/". In each of the hotwords, there
-   *         are cjkchars or bpes, the bpe/cjkchar are separated by space (" ").
-   *         For example, hotwords I LOVE YOU and HELLO WORLD, looks like:
+   *         the hotwords are separated by "/". For eaxmple, I LOVE YOU/HELLO
+   *         WORLD. if tokenize_hotwords is false, the hotwords should be
+   *         tokenized, so hotwords I LOVE YOU and HELLO WORLD, should look
+   *         like:
    *
    *         "▁I ▁LOVE ▁YOU/▁HE LL O ▁WORLD"
    */

sherpa-onnx/csrc/online-recognizer-transducer-impl.h

@@ -186,7 +186,8 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
     std::vector<std::vector<int32_t>> current;
     std::vector<float> current_scores;
     if (!EncodeHotwords(is, config_.model_config.modeling_unit, sym_,
-                        bpe_encoder_.get(), &current, &current_scores)) {
+                        config_.tokenize_hotwords, bpe_encoder_.get(), &current,
+                        &current_scores)) {
       SHERPA_ONNX_LOGE("Encode hotwords failed, skipping, hotwords are : %s",
                        hotwords.c_str());
     }
@@ -401,7 +402,8 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
     }
 
     if (!EncodeHotwords(is, config_.model_config.modeling_unit, sym_,
-                        bpe_encoder_.get(), &hotwords_, &boost_scores_)) {
+                        config_.tokenize_hotwords, bpe_encoder_.get(),
+                        &hotwords_, &boost_scores_)) {
       SHERPA_ONNX_LOGE(
           "Failed to encode some hotwords, skip them already, see logs above "
           "for details.");
@@ -425,7 +427,8 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
     }
 
     if (!EncodeHotwords(is, config_.model_config.modeling_unit, sym_,
-                        bpe_encoder_.get(), &hotwords_, &boost_scores_)) {
+                        config_.tokenize_hotwords, bpe_encoder_.get(),
+                        &hotwords_, &boost_scores_)) {
       SHERPA_ONNX_LOGE(
           "Failed to encode some hotwords, skip them already, see logs above "
           "for details.");

sherpa-onnx/csrc/online-recognizer.cc

@@ -96,6 +96,10 @@ void OnlineRecognizerConfig::Register(ParseOptions *po) {
       "The file containing hotwords, one words/phrases per line, For example: "
       "HELLO WORLD"
       "你好世界");
+  po->Register(
+      "tokenize-hotwords", &tokenize_hotwords,
+      "Whether to tokenize hotwords, default true, if false the input hotwords "
+      "should be tokenized into tokens");
   po->Register("decoding-method", &decoding_method,
                "decoding method,"
                "now support greedy_search and modified_beam_search.");
@@ -183,6 +187,7 @@ std::string OnlineRecognizerConfig::ToString() const {
   os << "max_active_paths=" << max_active_paths << ", ";
   os << "hotwords_score=" << hotwords_score << ", ";
   os << "hotwords_file=\"" << hotwords_file << "\", ";
+  os << "tokenize_hotwords=" << (tokenize_hotwords ? "True" : "False") << ", ";
   os << "decoding_method=\"" << decoding_method << "\", ";
   os << "blank_penalty=" << blank_penalty << ", ";
   os << "temperature_scale=" << temperature_scale << ", ";

sherpa-onnx/csrc/online-recognizer.h

@@ -95,6 +95,9 @@ struct OnlineRecognizerConfig {
   /// used only for modified_beam_search
   std::string hotwords_file;
   float hotwords_score = 1.5;
+  /// Whether to tokenize the input hotwords, normally should be true
+  /// if false, you have to tokenize hotwords by yourself.
+  bool tokenize_hotwords = true;
 
   float blank_penalty = 0.0;
 
@@ -115,8 +118,9 @@ struct OnlineRecognizerConfig {
       const OnlineCtcFstDecoderConfig &ctc_fst_decoder_config,
       bool enable_endpoint, const std::string &decoding_method,
       int32_t max_active_paths, const std::string &hotwords_file,
-      float hotwords_score, float blank_penalty, float temperature_scale,
-      const std::string &rule_fsts, const std::string &rule_fars)
+      float hotwords_score, bool tokenize_hotwords, float blank_penalty,
+      float temperature_scale, const std::string &rule_fsts,
+      const std::string &rule_fars)
       : feat_config(feat_config),
         model_config(model_config),
         lm_config(lm_config),
@@ -127,6 +131,7 @@ struct OnlineRecognizerConfig {
         max_active_paths(max_active_paths),
         hotwords_file(hotwords_file),
         hotwords_score(hotwords_score),
+        tokenize_hotwords(tokenize_hotwords),
         blank_penalty(blank_penalty),
         temperature_scale(temperature_scale),
         rule_fsts(rule_fsts),
@@ -156,9 +161,10 @@ class OnlineRecognizer {
   /** Create a stream for decoding.
    *
    *  @param The hotwords for this string, it might contain several hotwords,
-   *         the hotwords are separated by "/". In each of the hotwords, there
-   *         are cjkchars or bpes, the bpe/cjkchar are separated by space (" ").
-   *         For example, hotwords I LOVE YOU and HELLO WORLD, looks like:
+   *         the hotwords are separated by "/". For eaxmple, I LOVE YOU/HELLO
+   *         WORLD. if tokenize_hotwords is false, the hotwords should be
+   *         tokenized, so hotwords I LOVE YOU and HELLO WORLD, should look
+   *         like:
    *
    *         "▁I ▁LOVE ▁YOU/▁HE LL O ▁WORLD"
    */

sherpa-onnx/csrc/text2token-test.cc

@@ -43,12 +43,23 @@ TEST(TEXT2TOKEN, TEST_cjkchar) {
   std::vector<std::vector<int32_t>> ids;
   std::vector<float> scores;
 
-  auto r = EncodeHotwords(iss, "cjkchar", sym_table, nullptr, &ids, &scores);
+  auto r =
+      EncodeHotwords(iss, "cjkchar", sym_table, true, nullptr, &ids, &scores);
 
   std::vector<std::vector<int32_t>> expected_ids(
       {{379, 380, 72, 874, 93, 1251, 489}, {262, 147, 3423, 2476, 21, 147}});
   EXPECT_EQ(ids, expected_ids);
+  EXPECT_EQ(scores.size(), 0);
+
+  // tokenize_hotwords = false
+  text = "世 界 人 民 大 团 结\n中 国 V S 美 国\n\n";  // Test blank lines also
 
+  iss.clear();
+  iss.str(text);
+
+  r = EncodeHotwords(iss, "cjkchar", sym_table, false, nullptr, &ids, &scores);
+
+  EXPECT_EQ(ids, expected_ids);
   EXPECT_EQ(scores.size(), 0);
 }
 
@@ -79,8 +90,8 @@ TEST(TEXT2TOKEN, TEST_bpe) {
   std::vector<std::vector<int32_t>> ids;
   std::vector<float> scores;
 
-  auto r =
-      EncodeHotwords(iss, "bpe", sym_table, bpe_processor.get(), &ids, &scores);
+  auto r = EncodeHotwords(iss, "bpe", sym_table, true, bpe_processor.get(),
+                          &ids, &scores);
 
   std::vector<std::vector<int32_t>> expected_ids(
       {{22, 58, 24, 425}, {19, 370, 47}});
@@ -117,8 +128,8 @@ TEST(TEXT2TOKEN, TEST_cjkchar_bpe) {
   std::vector<std::vector<int32_t>> ids;
   std::vector<float> scores;
 
-  auto r = EncodeHotwords(iss, "cjkchar+bpe", sym_table, bpe_processor.get(),
-                          &ids, &scores);
+  auto r = EncodeHotwords(iss, "cjkchar+bpe", sym_table, true,
+                          bpe_processor.get(), &ids, &scores);
 
   std::vector<std::vector<int32_t>> expected_ids(
       {{1368, 1392, 557, 680, 275, 178, 475},
@@ -156,8 +167,8 @@ TEST(TEXT2TOKEN, TEST_bbpe) {
   std::vector<std::vector<int32_t>> ids;
   std::vector<float> scores;
 
-  auto r =
-      EncodeHotwords(iss, "bpe", sym_table, bpe_processor.get(), &ids, &scores);
+  auto r = EncodeHotwords(iss, "bpe", sym_table, true, bpe_processor.get(),
+                          &ids, &scores);
 
   std::vector<std::vector<int32_t>> expected_ids(
       {{259, 1118, 234, 188, 132}, {259, 1585, 236, 161, 148, 236, 160, 191}});

sherpa-onnx/csrc/utils.cc

@@ -70,6 +70,9 @@ static bool EncodeBase(const std::vector<std::string> &lines,
         }
       }
     }
+    if (tmp_ids.empty()) {
+      continue;
+    }
     ids->push_back(std::move(tmp_ids));
     tmp_ids = {};
     tmp_scores.push_back(score);
@@ -101,14 +104,22 @@ static bool EncodeBase(const std::vector<std::string> &lines,
 }
 
 bool EncodeHotwords(std::istream &is, const std::string &modeling_unit,
-                    const SymbolTable &symbol_table,
+                    const SymbolTable &symbol_table, bool tokenize_hotwords,
                     const ssentencepiece::Ssentencepiece *bpe_encoder,
                     std::vector<std::vector<int32_t>> *hotwords,
                     std::vector<float> *boost_scores) {
   std::vector<std::string> lines;
   std::string line;
   std::string word;
 
+  if (!tokenize_hotwords) {
+    while (std::getline(is, line)) {
+      lines.push_back(line);
+    }
+    return EncodeBase(lines, symbol_table, hotwords, nullptr, boost_scores,
+                      nullptr);
+  }
+
   while (std::getline(is, line)) {
     std::string score;
     std::string phrase;

sherpa-onnx/csrc/utils.h

@@ -27,7 +27,7 @@ namespace sherpa_onnx {
  *          otherwise returns false.
  */
 bool EncodeHotwords(std::istream &is, const std::string &modeling_unit,
-                    const SymbolTable &symbol_table,
+                    const SymbolTable &symbol_table, bool tokenize_hotwords,
                     const ssentencepiece::Ssentencepiece *bpe_encoder_,
                     std::vector<std::vector<int32_t>> *hotwords_id,
                     std::vector<float> *boost_scores);

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

@@ -17,14 +17,15 @@ static void PybindOfflineRecognizerConfig(py::module *m) {
       .def(py::init<const FeatureExtractorConfig &, const OfflineModelConfig &,
                     const OfflineLMConfig &, const OfflineCtcFstDecoderConfig &,
                     const std::string &, int32_t, const std::string &, float,
-                    float, const std::string &, const std::string &>(),
+                    bool, float, const std::string &, const std::string &>(),
            py::arg("feat_config"), py::arg("model_config"),
            py::arg("lm_config") = OfflineLMConfig(),
            py::arg("ctc_fst_decoder_config") = OfflineCtcFstDecoderConfig(),
            py::arg("decoding_method") = "greedy_search",
            py::arg("max_active_paths") = 4, py::arg("hotwords_file") = "",
-           py::arg("hotwords_score") = 1.5, py::arg("blank_penalty") = 0.0,
-           py::arg("rule_fsts") = "", py::arg("rule_fars") = "")
+           py::arg("hotwords_score") = 1.5, py::arg("tokenize_hotwords") = true,
+           py::arg("blank_penalty") = 0.0, py::arg("rule_fsts") = "",
+           py::arg("rule_fars") = "")
       .def_readwrite("feat_config", &PyClass::feat_config)
       .def_readwrite("model_config", &PyClass::model_config)
       .def_readwrite("lm_config", &PyClass::lm_config)
@@ -33,6 +34,7 @@ static void PybindOfflineRecognizerConfig(py::module *m) {
       .def_readwrite("max_active_paths", &PyClass::max_active_paths)
       .def_readwrite("hotwords_file", &PyClass::hotwords_file)
       .def_readwrite("hotwords_score", &PyClass::hotwords_score)
+      .def_readwrite("tokenize_hotwords", &PyClass::tokenize_hotwords)
       .def_readwrite("blank_penalty", &PyClass::blank_penalty)
       .def_readwrite("rule_fsts", &PyClass::rule_fsts)
       .def_readwrite("rule_fars", &PyClass::rule_fars)

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

@@ -54,20 +54,21 @@ static void PybindOnlineRecognizerResult(py::module *m) {
 static void PybindOnlineRecognizerConfig(py::module *m) {
   using PyClass = OnlineRecognizerConfig;
   py::class_<PyClass>(*m, "OnlineRecognizerConfig")
-      .def(py::init<const FeatureExtractorConfig &, const OnlineModelConfig &,
-                    const OnlineLMConfig &, const EndpointConfig &,
-                    const OnlineCtcFstDecoderConfig &, bool,
-                    const std::string &, int32_t, const std::string &, float,
-                    float, float, const std::string &, const std::string &>(),
-           py::arg("feat_config"), py::arg("model_config"),
-           py::arg("lm_config") = OnlineLMConfig(),
-           py::arg("endpoint_config") = EndpointConfig(),
-           py::arg("ctc_fst_decoder_config") = OnlineCtcFstDecoderConfig(),
-           py::arg("enable_endpoint"), py::arg("decoding_method"),
-           py::arg("max_active_paths") = 4, py::arg("hotwords_file") = "",
-           py::arg("hotwords_score") = 0, py::arg("blank_penalty") = 0.0,
-           py::arg("temperature_scale") = 2.0, py::arg("rule_fsts") = "",
-           py::arg("rule_fars") = "")
+      .def(
+          py::init<const FeatureExtractorConfig &, const OnlineModelConfig &,
+                   const OnlineLMConfig &, const EndpointConfig &,
+                   const OnlineCtcFstDecoderConfig &, bool, const std::string &,
+                   int32_t, const std::string &, float, bool, float, float,
+                   const std::string &, const std::string &>(),
+          py::arg("feat_config"), py::arg("model_config"),
+          py::arg("lm_config") = OnlineLMConfig(),
+          py::arg("endpoint_config") = EndpointConfig(),
+          py::arg("ctc_fst_decoder_config") = OnlineCtcFstDecoderConfig(),
+          py::arg("enable_endpoint"), py::arg("decoding_method"),
+          py::arg("max_active_paths") = 4, py::arg("hotwords_file") = "",
+          py::arg("hotwords_score") = 0, py::arg("tokenize_hotwords") = true,
+          py::arg("blank_penalty") = 0.0, py::arg("temperature_scale") = 2.0,
+          py::arg("rule_fsts") = "", py::arg("rule_fars") = "")
       .def_readwrite("feat_config", &PyClass::feat_config)
       .def_readwrite("model_config", &PyClass::model_config)
       .def_readwrite("lm_config", &PyClass::lm_config)
@@ -78,6 +79,7 @@ static void PybindOnlineRecognizerConfig(py::module *m) {
       .def_readwrite("max_active_paths", &PyClass::max_active_paths)
       .def_readwrite("hotwords_file", &PyClass::hotwords_file)
       .def_readwrite("hotwords_score", &PyClass::hotwords_score)
+      .def_readwrite("tokenize_hotwords", &PyClass::tokenize_hotwords)
       .def_readwrite("blank_penalty", &PyClass::blank_penalty)
       .def_readwrite("temperature_scale", &PyClass::temperature_scale)
       .def_readwrite("rule_fsts", &PyClass::rule_fsts)