Add C++ runtime for SenseVoice models (#1148)
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@@ -10,6 +10,7 @@ from _sherpa_onnx import (
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OfflineModelConfig,
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OfflineNemoEncDecCtcModelConfig,
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OfflineParaformerModelConfig,
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OfflineSenseVoiceModelConfig,
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)
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from _sherpa_onnx import OfflineRecognizer as _Recognizer
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from _sherpa_onnx import (
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@@ -173,6 +174,88 @@ class OfflineRecognizer(object):
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self.config = recognizer_config
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return self
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@classmethod
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def from_sense_voice(
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cls,
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model: str,
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tokens: str,
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num_threads: int = 1,
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sample_rate: int = 16000,
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feature_dim: int = 80,
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decoding_method: str = "greedy_search",
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debug: bool = False,
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provider: str = "cpu",
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language: str = "",
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use_itn: bool = False,
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rule_fsts: str = "",
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rule_fars: str = "",
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):
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"""
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Please refer to
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`<https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models>`_
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to download pre-trained models.
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Args:
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tokens:
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Path to ``tokens.txt``. Each line in ``tokens.txt`` contains two
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columns::
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symbol integer_id
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model:
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Path to ``model.onnx``.
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num_threads:
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Number of threads for neural network computation.
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sample_rate:
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Sample rate of the training data used to train the model.
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feature_dim:
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Dimension of the feature used to train the model.
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decoding_method:
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Valid values are greedy_search.
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debug:
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True to show debug messages.
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provider:
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onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
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language:
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If not empty, then valid values are: auto, zh, en, ja, ko, yue
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use_itn:
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True to enable inverse text normalization; False to disable it.
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rule_fsts:
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If not empty, it specifies fsts for inverse text normalization.
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If there are multiple fsts, they are separated by a comma.
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rule_fars:
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If not empty, it specifies fst archives for inverse text normalization.
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If there are multiple archives, they are separated by a comma.
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"""
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self = cls.__new__(cls)
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model_config = OfflineModelConfig(
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sense_voice=OfflineSenseVoiceModelConfig(
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model=model,
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language=language,
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use_itn=use_itn,
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),
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tokens=tokens,
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num_threads=num_threads,
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debug=debug,
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provider=provider,
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)
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feat_config = FeatureExtractorConfig(
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sampling_rate=sample_rate,
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feature_dim=feature_dim,
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)
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recognizer_config = OfflineRecognizerConfig(
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feat_config=feat_config,
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model_config=model_config,
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decoding_method=decoding_method,
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rule_fsts=rule_fsts,
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rule_fars=rule_fars,
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)
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self.recognizer = _Recognizer(recognizer_config)
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self.config = recognizer_config
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return self
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@classmethod
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def from_paraformer(
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cls,
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