561 lines
18 KiB
Python
561 lines
18 KiB
Python
# Copyright (c) 2023 by manyeyes
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# Copyright (c) 2023 Xiaomi Corporation
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from pathlib import Path
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from typing import List, Optional
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from _sherpa_onnx import (
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FeatureExtractorConfig,
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OfflineCtcFstDecoderConfig,
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OfflineModelConfig,
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OfflineNemoEncDecCtcModelConfig,
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OfflineParaformerModelConfig,
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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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OfflineRecognizerConfig,
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OfflineStream,
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OfflineTdnnModelConfig,
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OfflineTransducerModelConfig,
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OfflineWenetCtcModelConfig,
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OfflineWhisperModelConfig,
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OfflineZipformerCtcModelConfig,
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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 OfflineRecognizer(object):
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"""A class for offline 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_offline_recognizer.py
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- https://github.com/k2-fsa/sherpa-onnx/blob/master/python-api-examples/offline-decode-files.py
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"""
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@classmethod
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def from_transducer(
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cls,
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encoder: str,
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decoder: str,
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joiner: 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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max_active_paths: int = 4,
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hotwords_file: str = "",
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hotwords_score: float = 1.5,
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blank_penalty: float = 0.0,
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modeling_unit: str = "cjkchar",
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bpe_vocab: str = "",
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debug: bool = False,
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provider: str = "cpu",
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model_type: str = "transducer",
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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/offline-transducer/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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decoding_method:
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Valid values: greedy_search, modified_beam_search.
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max_active_paths:
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Maximum number of active paths to keep. Used only when
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decoding_method is modified_beam_search.
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hotwords_file:
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The file containing hotwords, one words/phrases per line, and for each
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phrase the bpe/cjkchar are separated by a space.
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hotwords_score:
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The hotword score of each token for biasing word/phrase. Used only if
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hotwords_file is given with modified_beam_search as decoding method.
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blank_penalty:
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The penalty applied on blank symbol during decoding.
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modeling_unit:
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The modeling unit of the model, commonly used units are bpe, cjkchar,
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cjkchar+bpe, etc. Currently, it is needed only when hotwords are
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provided, we need it to encode the hotwords into token sequence.
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and the modeling unit is bpe or cjkchar+bpe.
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bpe_vocab:
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The vocabulary generated by google's sentencepiece program.
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It is a file has two columns, one is the token, the other is
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the log probability, you can get it from the directory where
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your bpe model is generated. Only used when hotwords provided
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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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"""
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self = cls.__new__(cls)
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model_config = OfflineModelConfig(
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transducer=OfflineTransducerModelConfig(
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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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),
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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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modeling_unit=modeling_unit,
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bpe_vocab=bpe_vocab,
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model_type=model_type,
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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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if len(hotwords_file) > 0 and decoding_method != "modified_beam_search":
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raise ValueError(
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"Please use --decoding-method=modified_beam_search when using "
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f"--hotwords-file. Currently given: {decoding_method}"
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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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max_active_paths=max_active_paths,
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hotwords_file=hotwords_file,
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hotwords_score=hotwords_score,
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blank_penalty=blank_penalty,
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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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paraformer: 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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):
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"""
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Please refer to
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`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/index.html>`_
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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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paraformer:
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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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"""
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self = cls.__new__(cls)
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model_config = OfflineModelConfig(
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paraformer=OfflineParaformerModelConfig(model=paraformer),
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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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model_type="paraformer",
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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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)
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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_telespeech_ctc(
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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 = 40,
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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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):
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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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model:
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Path to ``model.onnx``.
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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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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. It is
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ignored and is hard-coded in C++ to 40.
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feature_dim:
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Dimension of the feature used to train the model. It is ignored
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and is hard-coded in C++ to 40.
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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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"""
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self = cls.__new__(cls)
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model_config = OfflineModelConfig(
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telespeech_ctc=model,
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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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model_type="nemo_ctc",
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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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)
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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_nemo_ctc(
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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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):
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"""
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Please refer to
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`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/nemo/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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model:
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Path to ``model.onnx``.
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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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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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"""
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self = cls.__new__(cls)
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model_config = OfflineModelConfig(
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nemo_ctc=OfflineNemoEncDecCtcModelConfig(model=model),
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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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model_type="nemo_ctc",
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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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)
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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_whisper(
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cls,
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encoder: str,
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decoder: str,
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tokens: str,
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language: str = "en",
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task: str = "transcribe",
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num_threads: int = 1,
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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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tail_paddings: int = -1,
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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/whisper/index.html>`_
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to download pre-trained models for different kinds of whisper models,
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e.g., tiny, tiny.en, base, base.en, etc.
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Args:
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encoder_model:
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Path to the encoder model, e.g., tiny-encoder.onnx,
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tiny-encoder.int8.onnx, tiny-encoder.ort, etc.
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decoder_model:
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Path to the encoder model, e.g., tiny-encoder.onnx,
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tiny-encoder.int8.onnx, tiny-encoder.ort, etc.
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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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language:
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The spoken language in the audio file. Example values: en, de, zh,
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jp, fr. See https://github.com/openai/whisper/blob/main/whisper/tokenizer.py#L10
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for all possible values. Note that for non-multilingual models, the
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only valid value is 'en'.
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task:
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Valid values are: transcribe, translate. Note that for
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non-multilingual models, the only valid value is 'transcribe'.
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num_threads:
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Number of threads for neural network computation.
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decoding_method:
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Valid values: 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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"""
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self = cls.__new__(cls)
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model_config = OfflineModelConfig(
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whisper=OfflineWhisperModelConfig(
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encoder=encoder,
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decoder=decoder,
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language=language,
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task=task,
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tail_paddings=tail_paddings,
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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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model_type="whisper",
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)
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feat_config = FeatureExtractorConfig(
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sampling_rate=16000,
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feature_dim=80,
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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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)
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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_tdnn_ctc(
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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 = 8000,
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feature_dim: int = 23,
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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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):
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"""
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Please refer to
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`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/yesno/index.html>`_
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to download pre-trained models.
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Args:
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model:
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Path to ``model.onnx``.
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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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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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"""
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self = cls.__new__(cls)
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model_config = OfflineModelConfig(
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tdnn=OfflineTdnnModelConfig(model=model),
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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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model_type="tdnn",
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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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)
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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_wenet_ctc(
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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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):
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"""
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Please refer to
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`<https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/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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model:
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Path to ``model.onnx``.
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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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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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"""
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self = cls.__new__(cls)
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model_config = OfflineModelConfig(
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wenet_ctc=OfflineWenetCtcModelConfig(model=model),
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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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model_type="wenet_ctc",
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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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)
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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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def create_stream(self, hotwords: Optional[str] = None):
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if hotwords is None:
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return self.recognizer.create_stream()
|
|
else:
|
|
return self.recognizer.create_stream(hotwords)
|
|
|
|
def decode_stream(self, s: OfflineStream):
|
|
self.recognizer.decode_stream(s)
|
|
|
|
def decode_streams(self, ss: List[OfflineStream]):
|
|
self.recognizer.decode_streams(ss)
|