Add C++ runtime for SenseVoice models (#1148)

This commit is contained in:
Fangjun Kuang
2024-07-18 22:54:18 +08:00
committed by GitHub
parent 3bae5c3fe5
commit 25f0a10468
34 changed files with 1160 additions and 39 deletions

View File

@@ -10,6 +10,7 @@ from _sherpa_onnx import (
OfflineModelConfig,
OfflineNemoEncDecCtcModelConfig,
OfflineParaformerModelConfig,
OfflineSenseVoiceModelConfig,
)
from _sherpa_onnx import OfflineRecognizer as _Recognizer
from _sherpa_onnx import (
@@ -173,6 +174,88 @@ class OfflineRecognizer(object):
self.config = recognizer_config
return self
@classmethod
def from_sense_voice(
cls,
model: str,
tokens: str,
num_threads: int = 1,
sample_rate: int = 16000,
feature_dim: int = 80,
decoding_method: str = "greedy_search",
debug: bool = False,
provider: str = "cpu",
language: str = "",
use_itn: bool = False,
rule_fsts: str = "",
rule_fars: str = "",
):
"""
Please refer to
`<https://github.com/k2-fsa/sherpa-onnx/releases/tag/asr-models>`_
to download pre-trained models.
Args:
tokens:
Path to ``tokens.txt``. Each line in ``tokens.txt`` contains two
columns::
symbol integer_id
model:
Path to ``model.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.
decoding_method:
Valid values are greedy_search.
debug:
True to show debug messages.
provider:
onnxruntime execution providers. Valid values are: cpu, cuda, coreml.
language:
If not empty, then valid values are: auto, zh, en, ja, ko, yue
use_itn:
True to enable inverse text normalization; False to disable it.
rule_fsts:
If not empty, it specifies fsts for inverse text normalization.
If there are multiple fsts, they are separated by a comma.
rule_fars:
If not empty, it specifies fst archives for inverse text normalization.
If there are multiple archives, they are separated by a comma.
"""
self = cls.__new__(cls)
model_config = OfflineModelConfig(
sense_voice=OfflineSenseVoiceModelConfig(
model=model,
language=language,
use_itn=use_itn,
),
tokens=tokens,
num_threads=num_threads,
debug=debug,
provider=provider,
)
feat_config = FeatureExtractorConfig(
sampling_rate=sample_rate,
feature_dim=feature_dim,
)
recognizer_config = OfflineRecognizerConfig(
feat_config=feat_config,
model_config=model_config,
decoding_method=decoding_method,
rule_fsts=rule_fsts,
rule_fars=rule_fars,
)
self.recognizer = _Recognizer(recognizer_config)
self.config = recognizer_config
return self
@classmethod
def from_paraformer(
cls,