keyword_spotter.py
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# Copyright (c) 2023 Xiaomi Corporation
from pathlib import Path
from typing import List, Optional
from _sherpa_onnx import (
FeatureExtractorConfig,
KeywordSpotterConfig,
OnlineModelConfig,
OnlineTransducerModelConfig,
OnlineStream,
ProviderConfig,
)
from _sherpa_onnx import KeywordSpotter as _KeywordSpotter
def _assert_file_exists(f: str):
assert Path(f).is_file(), f"{f} does not exist"
class KeywordSpotter(object):
"""A class for keyword spotting.
Please refer to the following files for usages
- https://github.com/k2-fsa/sherpa-onnx/blob/master/python-api-examples/keyword-spotter.py
- https://github.com/k2-fsa/sherpa-onnx/blob/master/python-api-examples/keyword-spotter-from-microphone.py
"""
def __init__(
self,
tokens: str,
encoder: str,
decoder: str,
joiner: str,
keywords_file: str,
num_threads: int = 2,
sample_rate: float = 16000,
feature_dim: int = 80,
max_active_paths: int = 4,
keywords_score: float = 1.0,
keywords_threshold: float = 0.25,
num_trailing_blanks: int = 1,
provider: str = "cpu",
device: int = 0,
):
"""
Please refer to
`<https://k2-fsa.github.io/sherpa/onnx/kws/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``.
keywords_file:
The file containing keywords, one word/phrase per line, and for each
phrase the bpe/cjkchar/pinyin are separated by a space.
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.
max_active_paths:
Use only when decoding_method is modified_beam_search. It specifies
the maximum number of active paths during beam search.
keywords_score:
The boosting score of each token for keywords. The larger the easier to
survive beam search.
keywords_threshold:
The trigger threshold (i.e. probability) of the keyword. The larger the
harder to trigger.
num_trailing_blanks:
The number of trailing blanks a keyword should be followed. Setting
to a larger value (e.g. 8) when your keywords has overlapping tokens
between each other.
provider:
onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
device:
onnxruntime cuda device index.
"""
_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,
)
provider_config = ProviderConfig(
provider=provider,
device=device,
)
model_config = OnlineModelConfig(
transducer=transducer_config,
tokens=tokens,
num_threads=num_threads,
provider_config=provider_config,
)
feat_config = FeatureExtractorConfig(
sampling_rate=sample_rate,
feature_dim=feature_dim,
)
keywords_spotter_config = KeywordSpotterConfig(
feat_config=feat_config,
model_config=model_config,
max_active_paths=max_active_paths,
num_trailing_blanks=num_trailing_blanks,
keywords_score=keywords_score,
keywords_threshold=keywords_threshold,
keywords_file=keywords_file,
)
self.keyword_spotter = _KeywordSpotter(keywords_spotter_config)
def reset_stream(self, s: OnlineStream):
self.keyword_spotter.reset(s)
def create_stream(self, keywords: Optional[str] = None):
if keywords is None:
return self.keyword_spotter.create_stream()
else:
return self.keyword_spotter.create_stream(keywords)
def decode_stream(self, s: OnlineStream):
self.keyword_spotter.decode_stream(s)
def decode_streams(self, ss: List[OnlineStream]):
self.keyword_spotter.decode_streams(ss)
def is_ready(self, s: OnlineStream) -> bool:
return self.keyword_spotter.is_ready(s)
def get_result(self, s: OnlineStream) -> str:
return self.keyword_spotter.get_result(s).keyword.strip()
def tokens(self, s: OnlineStream) -> List[str]:
return self.keyword_spotter.get_result(s).tokens
def timestamps(self, s: OnlineStream) -> List[float]:
return self.keyword_spotter.get_result(s).timestamps