Add C++ support for streaming NeMo CTC models. (#857)

sherpa-onnx/python/sherpa_onnx/online_recognizer.py

@@ -12,9 +12,11 @@ from _sherpa_onnx import (
 from _sherpa_onnx import OnlineRecognizer as _Recognizer
 from _sherpa_onnx import (
     OnlineRecognizerConfig,
+    OnlineRecognizerResult,
     OnlineStream,
     OnlineTransducerModelConfig,
     OnlineWenetCtcModelConfig,
+    OnlineNeMoCtcModelConfig,
     OnlineZipformer2CtcModelConfig,
     OnlineCtcFstDecoderConfig,
 )
@@ -59,6 +61,7 @@ class OnlineRecognizer(object):
         lm: str = "",
         lm_scale: float = 0.1,
         temperature_scale: float = 2.0,
+        debug: bool = False,
     ):
         """
         Please refer to
@@ -154,6 +157,7 @@ class OnlineRecognizer(object):
             num_threads=num_threads,
             provider=provider,
             model_type=model_type,
+            debug=debug,
         )
 
         feat_config = FeatureExtractorConfig(
@@ -220,6 +224,7 @@ class OnlineRecognizer(object):
         rule3_min_utterance_length: float = 20.0,
         decoding_method: str = "greedy_search",
         provider: str = "cpu",
+        debug: bool = False,
     ):
         """
         Please refer to
@@ -283,6 +288,7 @@ class OnlineRecognizer(object):
             num_threads=num_threads,
             provider=provider,
             model_type="paraformer",
+            debug=debug,
         )
 
         feat_config = FeatureExtractorConfig(
@@ -324,6 +330,7 @@ class OnlineRecognizer(object):
         ctc_graph: str = "",
         ctc_max_active: int = 3000,
         provider: str = "cpu",
+        debug: bool = False,
     ):
         """
         Please refer to
@@ -386,6 +393,7 @@ class OnlineRecognizer(object):
             tokens=tokens,
             num_threads=num_threads,
             provider=provider,
+            debug=debug,
         )
 
         feat_config = FeatureExtractorConfig(
@@ -417,6 +425,106 @@ class OnlineRecognizer(object):
         self.config = recognizer_config
         return self
 
+    @classmethod
+    def from_nemo_ctc(
+        cls,
+        tokens: str,
+        model: str,
+        num_threads: int = 2,
+        sample_rate: float = 16000,
+        feature_dim: int = 80,
+        enable_endpoint_detection: bool = False,
+        rule1_min_trailing_silence: float = 2.4,
+        rule2_min_trailing_silence: float = 1.2,
+        rule3_min_utterance_length: float = 20.0,
+        decoding_method: str = "greedy_search",
+        provider: str = "cpu",
+        debug: bool = False,
+    ):
+        """
+        Please refer to
+        `<https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models>`_
+        to download pre-trained models.
+
+        Args:
+          tokens:
+            Path to ``tokens.txt``. Each line in ``tokens.txt`` contains two
+            columns::
+
+                symbol integer_id
+
+          model:
+            Path to ``model.onnx``.
+          num_threads:
+            Number of threads for neural network computation.
+          sample_rate:
+            Sample rate of the training data used to train the model.
+          feature_dim:
+            Dimension of the feature used to train the model.
+          enable_endpoint_detection:
+            True to enable endpoint detection. False to disable endpoint
+            detection.
+          rule1_min_trailing_silence:
+            Used only when enable_endpoint_detection is True. If the duration
+            of trailing silence in seconds is larger than this value, we assume
+            an endpoint is detected.
+          rule2_min_trailing_silence:
+            Used only when enable_endpoint_detection is True. If we have decoded
+            something that is nonsilence and if the duration of trailing silence
+            in seconds is larger than this value, we assume an endpoint is
+            detected.
+          rule3_min_utterance_length:
+            Used only when enable_endpoint_detection is True. If the utterance
+            length in seconds is larger than this value, we assume an endpoint
+            is detected.
+          decoding_method:
+            The only valid value is greedy_search.
+          provider:
+            onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
+          debug:
+            True to show meta data in the model.
+        """
+        self = cls.__new__(cls)
+        _assert_file_exists(tokens)
+        _assert_file_exists(model)
+
+        assert num_threads > 0, num_threads
+
+        nemo_ctc_config = OnlineNeMoCtcModelConfig(
+            model=model,
+        )
+
+        model_config = OnlineModelConfig(
+            nemo_ctc=nemo_ctc_config,
+            tokens=tokens,
+            num_threads=num_threads,
+            provider=provider,
+            debug=debug,
+        )
+
+        feat_config = FeatureExtractorConfig(
+            sampling_rate=sample_rate,
+            feature_dim=feature_dim,
+        )
+
+        endpoint_config = EndpointConfig(
+            rule1_min_trailing_silence=rule1_min_trailing_silence,
+            rule2_min_trailing_silence=rule2_min_trailing_silence,
+            rule3_min_utterance_length=rule3_min_utterance_length,
+        )
+
+        recognizer_config = OnlineRecognizerConfig(
+            feat_config=feat_config,
+            model_config=model_config,
+            endpoint_config=endpoint_config,
+            enable_endpoint=enable_endpoint_detection,
+            decoding_method=decoding_method,
+        )
+
+        self.recognizer = _Recognizer(recognizer_config)
+        self.config = recognizer_config
+        return self
+
     @classmethod
     def from_wenet_ctc(
         cls,
@@ -433,6 +541,7 @@ class OnlineRecognizer(object):
         rule3_min_utterance_length: float = 20.0,
         decoding_method: str = "greedy_search",
         provider: str = "cpu",
+        debug: bool = False,
     ):
         """
         Please refer to
@@ -497,6 +606,7 @@ class OnlineRecognizer(object):
             tokens=tokens,
             num_threads=num_threads,
             provider=provider,
+            debug=debug,
         )
 
         feat_config = FeatureExtractorConfig(
@@ -537,6 +647,9 @@ class OnlineRecognizer(object):
     def is_ready(self, s: OnlineStream) -> bool:
         return self.recognizer.is_ready(s)
 
+    def get_result_all(self, s: OnlineStream) -> OnlineRecognizerResult:
+        return self.recognizer.get_result(s)
+
     def get_result(self, s: OnlineStream) -> str:
         return self.recognizer.get_result(s).text.strip()