online_recognizer.py
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# Copyright (c) 2023 Xiaomi Corporation
from pathlib import Path
from typing import List, Optional
from _sherpa_onnx import (
EndpointConfig,
FeatureExtractorConfig,
OnlineModelConfig,
OnlineRecognizer as _Recognizer,
OnlineRecognizerConfig,
OnlineStream,
OnlineTransducerModelConfig,
)
def _assert_file_exists(f: str):
assert Path(f).is_file(), f"{f} does not exist"
class OnlineRecognizer(object):
"""A class for streaming speech recognition.
Please refer to the following files for usages
- https://github.com/k2-fsa/sherpa-onnx/blob/master/sherpa-onnx/python/tests/test_online_recognizer.py
- https://github.com/k2-fsa/sherpa-onnx/blob/master/python-api-examples/online-decode-files.py
"""
@classmethod
def from_transducer(
cls,
tokens: str,
encoder: str,
decoder: str,
joiner: str,
num_threads: int = 4,
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",
max_active_paths: int = 4,
context_score: float = 1.5,
provider: str = "cpu",
model_type: str = "",
):
"""
Please refer to
`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html>`_
to download pre-trained models for different languages, e.g., Chinese,
English, etc.
Args:
tokens:
Path to ``tokens.txt``. Each line in ``tokens.txt`` contains two
columns::
symbol integer_id
encoder:
Path to ``encoder.onnx``.
decoder:
Path to ``decoder.onnx``.
joiner:
Path to ``joiner.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:
Valid values are greedy_search, modified_beam_search.
max_active_paths:
Use only when decoding_method is modified_beam_search. It specifies
the maximum number of active paths during beam search.
provider:
onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
model_type:
Online transducer model type. Valid values are: conformer, lstm,
zipformer, zipformer2. All other values lead to loading the model twice.
"""
self = cls.__new__(cls)
_assert_file_exists(tokens)
_assert_file_exists(encoder)
_assert_file_exists(decoder)
_assert_file_exists(joiner)
assert num_threads > 0, num_threads
transducer_config = OnlineTransducerModelConfig(
encoder=encoder,
decoder=decoder,
joiner=joiner,
)
model_config = OnlineModelConfig(
transducer=transducer_config,
tokens=tokens,
num_threads=num_threads,
provider=provider,
model_type=model_type,
)
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,
max_active_paths=max_active_paths,
context_score=context_score,
)
self.recognizer = _Recognizer(recognizer_config)
self.config = recognizer_config
return self
def create_stream(self, contexts_list: Optional[List[List[int]]] = None):
if contexts_list is None:
return self.recognizer.create_stream()
else:
return self.recognizer.create_stream(contexts_list)
def decode_stream(self, s: OnlineStream):
self.recognizer.decode_stream(s)
def decode_streams(self, ss: List[OnlineStream]):
self.recognizer.decode_streams(ss)
def is_ready(self, s: OnlineStream) -> bool:
return self.recognizer.is_ready(s)
def get_result(self, s: OnlineStream) -> str:
return self.recognizer.get_result(s).text.strip()
def tokens(self, s: OnlineStream) -> List[str]:
return self.recognizer.get_result(s).tokens
def timestamps(self, s: OnlineStream) -> List[float]:
return self.recognizer.get_result(s).timestamps
def is_endpoint(self, s: OnlineStream) -> bool:
return self.recognizer.is_endpoint(s)
def reset(self, s: OnlineStream) -> bool:
return self.recognizer.reset(s)