152 lines
5.1 KiB
Python
152 lines
5.1 KiB
Python
# Copyright (c) 2023 Xiaomi Corporation
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from pathlib import Path
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from typing import List
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from _sherpa_onnx import (
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EndpointConfig,
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FeatureExtractorConfig,
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OnlineRecognizer as _Recognizer,
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OnlineRecognizerConfig,
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OnlineStream,
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OnlineTransducerModelConfig,
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)
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def _assert_file_exists(f: str):
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assert Path(f).is_file(), f"{f} does not exist"
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class OnlineRecognizer(object):
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"""A class for streaming speech recognition.
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Please refer to the following files for usages
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- https://github.com/k2-fsa/sherpa-onnx/blob/master/sherpa-onnx/python/tests/test_online_recognizer.py
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- https://github.com/k2-fsa/sherpa-onnx/blob/master/python-api-examples/online-decode-files.py
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"""
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def __init__(
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self,
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tokens: str,
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encoder: str,
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decoder: str,
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joiner: str,
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num_threads: int = 4,
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sample_rate: float = 16000,
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feature_dim: int = 80,
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enable_endpoint_detection: bool = False,
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rule1_min_trailing_silence: float = 2.4,
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rule2_min_trailing_silence: float = 1.2,
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rule3_min_utterance_length: float = 20.0,
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decoding_method: str = "greedy_search",
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max_active_paths: int = 4,
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provider: str = "cpu",
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):
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"""
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Please refer to
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`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html>`_
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to download pre-trained models for different languages, e.g., Chinese,
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English, etc.
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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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encoder:
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Path to ``encoder.onnx``.
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decoder:
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Path to ``decoder.onnx``.
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joiner:
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Path to ``joiner.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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enable_endpoint_detection:
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True to enable endpoint detection. False to disable endpoint
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detection.
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rule1_min_trailing_silence:
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Used only when enable_endpoint_detection is True. If the duration
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of trailing silence in seconds is larger than this value, we assume
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an endpoint is detected.
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rule2_min_trailing_silence:
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Used only when enable_endpoint_detection is True. If we have decoded
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something that is nonsilence and if the duration of trailing silence
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in seconds is larger than this value, we assume an endpoint is
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detected.
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rule3_min_utterance_length:
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Used only when enable_endpoint_detection is True. If the utterance
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length in seconds is larger than this value, we assume an endpoint
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is detected.
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decoding_method:
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Valid values are greedy_search, modified_beam_search.
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max_active_paths:
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Use only when decoding_method is modified_beam_search. It specifies
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the maximum number of active paths during beam search.
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provider:
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onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
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"""
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_assert_file_exists(tokens)
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_assert_file_exists(encoder)
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_assert_file_exists(decoder)
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_assert_file_exists(joiner)
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assert num_threads > 0, num_threads
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model_config = OnlineTransducerModelConfig(
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encoder_filename=encoder,
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decoder_filename=decoder,
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joiner_filename=joiner,
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tokens=tokens,
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num_threads=num_threads,
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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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endpoint_config = EndpointConfig(
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rule1_min_trailing_silence=rule1_min_trailing_silence,
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rule2_min_trailing_silence=rule2_min_trailing_silence,
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rule3_min_utterance_length=rule3_min_utterance_length,
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)
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recognizer_config = OnlineRecognizerConfig(
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feat_config=feat_config,
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model_config=model_config,
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endpoint_config=endpoint_config,
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enable_endpoint=enable_endpoint_detection,
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decoding_method=decoding_method,
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max_active_paths=max_active_paths,
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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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def create_stream(self):
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return self.recognizer.create_stream()
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def decode_stream(self, s: OnlineStream):
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self.recognizer.decode_stream(s)
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def decode_streams(self, ss: List[OnlineStream]):
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self.recognizer.decode_streams(ss)
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def is_ready(self, s: OnlineStream) -> bool:
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return self.recognizer.is_ready(s)
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def get_result(self, s: OnlineStream) -> str:
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return self.recognizer.get_result(s).text.strip()
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def is_endpoint(self, s: OnlineStream) -> bool:
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return self.recognizer.is_endpoint(s)
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def reset(self, s: OnlineStream) -> bool:
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return self.recognizer.reset(s)
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