More fixes

python-api-examples/non_streaming_server.py

@@ -327,7 +327,7 @@ def add_modified_beam_search_args(parser: argparse.ArgumentParser):
     )
 
 
-def add_contexts_args(parser: argparse.ArgumentParser):
+def add_hotwords_args(parser: argparse.ArgumentParser):
     parser.add_argument(
         "--bpe-model",
         type=str,
@@ -337,25 +337,36 @@ def add_contexts_args(parser: argparse.ArgumentParser):
         Used only when --decoding-method=modified_beam_search
         """,
     )
+    parser.add_argument(
+        "--tokens_type",
+        type=str,
+        default="cjkchar",
+        help="""
+        The type of tokens (i.e the modeling unit).
+        Valid values are bpe, cjkchar+bpe, cjkchar.
+        """,
+    )
 
     parser.add_argument(
-        "--modeling-unit",
+        "--hotwords-file",
         type=str,
-        default="char",
+        default="",
         help="""
-        The type of modeling unit.
-        Valid values are bpe, bpe+char, char.
-        Note: the char here means characters in CJK languages.
+        The file containing hotwords, one words/phrases per line, and for each
+        phrase the bpe/cjkchar are separated by a space. For example:
+
+        HELLO WORLD
+        你 好 世 界
         """,
     )
 
     parser.add_argument(
-        "--context-score",
+        "--hotwords-score",
         type=float,
         default=1.5,
         help="""
-        The context score of each token for biasing word/phrase. Used only if
-        --contexts is given.
+        The hotword score of each token for biasing word/phrase. Used only if
+        --hotwords-file is given.
         """,
     )
 
@@ -376,7 +387,7 @@ def check_args(args):
         assert Path(args.decoder).is_file(), args.decoder
         assert Path(args.joiner).is_file(), args.joiner
 
-    if args.contexts != "":
+    if args.hotwords_file != "":
         assert args.decoding_method == "modified_beam_search", args.decoding_method
 
 
@@ -388,7 +399,7 @@ def get_args():
     add_model_args(parser)
     add_feature_config_args(parser)
     add_decoding_args(parser)
-    add_contexts_args(parser)
+    add_hotwords_args(parser)
 
     parser.add_argument(
         "--port",
@@ -808,24 +819,6 @@ def assert_file_exists(filename: str):
     )
 
 
-def encode_contexts(args, contexts: List[str]) -> List[List[int]]:
-    sp = None
-    if "bpe" in args.modeling_unit:
-        assert_file_exists(args.bpe_model)
-        sp = spm.SentencePieceProcessor()
-        sp.load(args.bpe_model)
-    tokens = {}
-    with open(args.tokens, "r", encoding="utf-8") as f:
-        for line in f:
-            toks = line.strip().split()
-            assert len(toks) == 2, len(toks)
-            assert toks[0] not in tokens, f"Duplicate token: {toks} "
-            tokens[toks[0]] = int(toks[1])
-    return sherpa_onnx.encode_contexts(
-        modeling_unit=args.modeling_unit, contexts=contexts, sp=sp, tokens_table=tokens
-    )
-
-
 def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
     if args.encoder:
         assert len(args.paraformer) == 0, args.paraformer
@@ -848,7 +841,10 @@ def create_recognizer(args) -> sherpa_onnx.OfflineRecognizer:
             feature_dim=args.feat_dim,
             decoding_method=args.decoding_method,
             max_active_paths=args.max_active_paths,
-            context_score=args.context_score,
+            tokens_type=args.tokens_type,
+            bpe_model=args.bpe_model,
+            hotwords_file=args.hotwords_file,
+            hotwords_score=args.hotwords_score,
         )
     elif args.paraformer:
         assert len(args.nemo_ctc) == 0, args.nemo_ctc

python-api-examples/offline-decode-files.py

@@ -134,8 +134,8 @@ def get_args():
         type=float,
         default=1.5,
         help="""
-        The context score of each token for biasing word/phrase. Used only if
-        --contexts is given.
+        The hotword score of each token for biasing word/phrase. Used only if
+        --hotwords-file is given.
         """,
     )
 
@@ -327,7 +327,7 @@ def main():
             tokens_type=args.tokens_type,
             bpe_model=args.bpe_model,
             hotwords_file=args.hotwords_file,
-            hotwords_score=args.context_score,
+            hotwords_score=args.hotwords_score,
             debug=args.debug,
         )
     elif args.paraformer:

python-api-examples/online-decode-files.py

@@ -134,36 +134,35 @@ def get_args():
     )
 
     parser.add_argument(
-        "--modeling-unit",
+        "--tokens_type",
         type=str,
-        default="char",
+        default="cjkchar",
         help="""
-        The type of modeling unit, it will be used to tokenize contexts biasing phrases.
-        Valid values are bpe, bpe+char, char.
-        Note: the char here means characters in CJK languages.
-        Used only when --decoding-method=modified_beam_search
+        The type of tokens (i.e the modeling unit).
+        Valid values are bpe, cjkchar+bpe, cjkchar.
         """,
     )
 
     parser.add_argument(
-        "--contexts",
+        "--hotwords-file",
         type=str,
         default="",
         help="""
-        The context list, it is a string containing some words/phrases separated
-        with /, for example, 'HELLO WORLD/I LOVE YOU/GO AWAY".
-        Used only when --decoding-method=modified_beam_search
+        The file containing hotwords, one words/phrases per line, and for each
+        phrase the bpe/cjkchar are separated by a space. For example:
+
+        HELLO WORLD
+        你 好 世 界
         """,
     )
 
     parser.add_argument(
-        "--context-score",
+        "--hotwords-score",
         type=float,
         default=1.5,
         help="""
-        The context score of each token for biasing word/phrase. Used only if
-        --contexts is given.
-        Used only when --decoding-method=modified_beam_search
+        The hotword score of each token for biasing word/phrase. Used only if
+        --hotwords-file is given.
         """,
     )
 
@@ -214,27 +213,6 @@ def read_wave(wave_filename: str) -> Tuple[np.ndarray, int]:
         return samples_float32, f.getframerate()
 
 
-def encode_contexts(args, contexts: List[str]) -> List[List[int]]:
-    sp = None
-    if "bpe" in args.modeling_unit:
-        assert_file_exists(args.bpe_model)
-        sp = spm.SentencePieceProcessor()
-        sp.load(args.bpe_model)
-    tokens = {}
-    with open(args.tokens, "r", encoding="utf-8") as f:
-        for line in f:
-            toks = line.strip().split()
-            assert len(toks) == 2, len(toks)
-            assert toks[0] not in tokens, f"Duplicate token: {toks} "
-            tokens[toks[0]] = int(toks[1])
-    return sherpa_onnx.encode_contexts(
-        modeling_unit=args.modeling_unit,
-        contexts=contexts,
-        sp=sp,
-        tokens_table=tokens,
-    )
-
-
 def main():
     args = get_args()
     assert_file_exists(args.tokens)
@@ -258,7 +236,10 @@ def main():
             feature_dim=80,
             decoding_method=args.decoding_method,
             max_active_paths=args.max_active_paths,
-            hotwords_score=args.context_score,
+            tokens_type=args.tokens_type,
+            bpe_model=args.bpe_model,
+            hotwords_file=args.hotwords_file,
+            hotwords_score=args.hotwords_score,
         )
     elif args.paraformer_encoder:
         recognizer = sherpa_onnx.OnlineRecognizer.from_paraformer(
@@ -277,12 +258,6 @@ def main():
     print("Started!")
     start_time = time.time()
 
-    contexts_list = []
-    contexts = [x.strip().upper() for x in args.contexts.split("/") if x.strip()]
-    if contexts:
-        print(f"Contexts list: {contexts}")
-        contexts_list = encode_contexts(args, contexts)
-
     streams = []
     total_duration = 0
     for wave_filename in args.sound_files:
@@ -291,10 +266,7 @@ def main():
         duration = len(samples) / sample_rate
         total_duration += duration
 
-        if contexts_list:
-            s = recognizer.create_stream(contexts_list=contexts_list)
-        else:
-            s = recognizer.create_stream()
+        s = recognizer.create_stream()
 
         s.accept_waveform(sample_rate, samples)
 

python-api-examples/streaming_server.py

@@ -187,6 +187,51 @@ def add_decoding_args(parser: argparse.ArgumentParser):
     add_modified_beam_search_args(parser)
 
 
+def add_hotwords_args(parser: argparse.ArgumentParser):
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="",
+        help="""
+        Path to bpe.model,
+        Used only when --decoding-method=modified_beam_search
+        """,
+    )
+    parser.add_argument(
+        "--tokens_type",
+        type=str,
+        default="cjkchar",
+        help="""
+        The type of tokens (i.e the modeling unit).
+        Valid values are bpe, cjkchar+bpe, cjkchar.
+        """,
+    )
+
+    parser.add_argument(
+        "--hotwords-file",
+        type=str,
+        default="",
+        help="""
+        The file containing hotwords, one words/phrases per line, and for each
+        phrase the bpe/cjkchar are separated by a space. For example:
+
+        HELLO WORLD
+        你 好 世 界
+        """,
+    )
+
+    parser.add_argument(
+        "--hotwords-score",
+        type=float,
+        default=1.5,
+        help="""
+        The hotword score of each token for biasing word/phrase. Used only if
+        --hotwords-file is given.
+        """,
+    )
+
+
+
 def add_modified_beam_search_args(parser: argparse.ArgumentParser):
     parser.add_argument(
         "--num-active-paths",
@@ -239,6 +284,7 @@ def get_args():
     add_model_args(parser)
     add_decoding_args(parser)
     add_endpointing_args(parser)
+    add_hotwords_args(parser)
 
     parser.add_argument(
         "--port",
@@ -343,6 +389,10 @@ def create_recognizer(args) -> sherpa_onnx.OnlineRecognizer:
             feature_dim=args.feat_dim,
             decoding_method=args.decoding_method,
             max_active_paths=args.num_active_paths,
+            tokens_type=args.tokens_type,
+            bpe_model=args.bpe_model,
+            hotwords_score=args.hotwords_score,
+            hotwords_file=args.hotwords_file,
             enable_endpoint_detection=args.use_endpoint != 0,
             rule1_min_trailing_silence=args.rule1_min_trailing_silence,
             rule2_min_trailing_silence=args.rule2_min_trailing_silence,

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

@@ -93,17 +93,16 @@ class OfflineRecognizerTransducerImpl : public OfflineRecognizerImpl {
       const std::string &hotwords) const override {
     auto hws = std::regex_replace(hotwords, std::regex("/"), "\n");
     std::istringstream is(hws);
-    int32_t default_hws_num = hotwords_.size();
-    std::vector<std::vector<int32_t>> tmp;
+    std::vector<std::vector<int32_t>> current;
     if (!EncodeHotwords(is, config_.model_config.tokens_type, symbol_table_,
-                        bpe_processor_, &tmp)) {
+                        bpe_processor_, &current)) {
       SHERPA_ONNX_LOGE("Encode hotwords failed, skipping, hotwords are : %s",
                        hotwords.c_str());
     }
-    hotwords_.insert(hotwords_.end(), tmp.begin(), tmp.end());
+    current.insert(current.end(), hotwords_.begin(), hotwords_.end());
+
     auto context_graph =
-        std::make_shared<ContextGraph>(hotwords_, config_.hotwords_score);
-    hotwords_.resize(default_hws_num);
+        std::make_shared<ContextGraph>(current, config_.hotwords_score);
     return std::make_unique<OfflineStream>(config_.feat_config, context_graph);
   }
 

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

@@ -29,7 +29,7 @@ class OnlineRecognizerImpl {
   virtual std::unique_ptr<OnlineStream> CreateStream() const = 0;
 
   virtual std::unique_ptr<OnlineStream> CreateStream(
-      const std::vector<std::vector<int32_t>> &contexts) const {
+      const std::string &hotwords) const {
     SHERPA_ONNX_LOGE("Only transducer models support contextual biasing.");
     exit(-1);
   }

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

@@ -117,15 +117,18 @@ class OnlineRecognizerTransducerImpl : public OnlineRecognizerImpl {
   }
 
   std::unique_ptr<OnlineStream> CreateStream(
-      const std::vector<std::vector<int32_t>> &contexts) const override {
-    // We create context_graph at this level, because we might have default
-    // context_graph(will be added later if needed) that belongs to the whole
-    // model rather than each stream.
-    std::vector<std::vector<int32_t>> hotwords;
-    hotwords.insert(hotwords.end(), hotwords_.begin(), hotwords_.end());
-    hotwords.insert(hotwords.end(), contexts.begin(), contexts.end());
+      const std::string &hotwords) const override {
+    auto hws = std::regex_replace(hotwords, std::regex("/"), "\n");
+    std::istringstream is(hws);
+    std::vector<std::vector<int32_t>> current;
+    if (!EncodeHotwords(is, config_.model_config.tokens_type, sym_,
+                        bpe_processor_, &current)) {
+      SHERPA_ONNX_LOGE("Encode hotwords failed, skipping, hotwords are : %s",
+                       hotwords.c_str());
+    }
+    current.insert(current.end(), hotwords_.begin(), hotwords_.end());
     auto context_graph =
-        std::make_shared<ContextGraph>(hotwords, config_.hotwords_score);
+        std::make_shared<ContextGraph>(current, config_.hotwords_score);
     auto stream =
         std::make_unique<OnlineStream>(config_.feat_config, context_graph);
     InitOnlineStream(stream.get());

sherpa-onnx/csrc/online-recognizer.cc

@@ -113,8 +113,8 @@ std::unique_ptr<OnlineStream> OnlineRecognizer::CreateStream() const {
 }
 
 std::unique_ptr<OnlineStream> OnlineRecognizer::CreateStream(
-    const std::vector<std::vector<int32_t>> &context_list) const {
-  return impl_->CreateStream(context_list);
+    const std::string &hotwords) const {
+  return impl_->CreateStream(hotwords);
 }
 
 bool OnlineRecognizer::IsReady(OnlineStream *s) const {

sherpa-onnx/csrc/online-recognizer.h

@@ -124,8 +124,7 @@ class OnlineRecognizer {
   std::unique_ptr<OnlineStream> CreateStream() const;
 
   // Create a stream with context phrases
-  std::unique_ptr<OnlineStream> CreateStream(
-      const std::vector<std::vector<int32_t>> &context_list) const;
+  std::unique_ptr<OnlineStream> CreateStream(const std::string &hotwords) const;
 
   /**
    * Return true if the given stream has enough frames for decoding.