97 lines
2.6 KiB
Python
97 lines
2.6 KiB
Python
from pathlib import Path
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from typing import List
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from _sherpa_onnx import (
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OnlineStream,
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OnlineTransducerModelConfig,
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FeatureExtractorConfig,
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OnlineRecognizerConfig,
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)
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from _sherpa_onnx import OnlineRecognizer as _Recognizer
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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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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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):
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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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"""
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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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num_threads=num_threads,
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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 = OnlineRecognizerConfig(
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feat_config=feat_config,
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model_config=model_config,
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tokens=tokens,
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)
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self.recognizer = _Recognizer(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
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