from malaya_speech.supervised import stt
from herpetologist import check_type
from malaya_speech.utils import describe_availability
import json
_transducer_availability = {
'tiny-conformer': {
'Size (MB)': 24.4,
'Quantized Size (MB)': 9.14,
'WER': 0.2128108,
'CER': 0.08136871,
'WER-LM': 0.1996828,
'CER-LM': 0.0770037,
'Language': ['malay'],
},
'small-conformer': {
'Size (MB)': 49.2,
'Quantized Size (MB)': 18.1,
'WER': 0.19853302,
'CER': 0.07449528,
'WER-LM': 0.18536073,
'CER-LM': 0.07114307,
'Language': ['malay'],
},
'conformer': {
'Size (MB)': 125,
'Quantized Size (MB)': 37.1,
'WER': 0.1636023,
'CER': 0.0587443,
'WER-LM': 0.1561821,
'CER-LM': 0.0571897,
'Language': ['malay'],
},
'large-conformer': {
'Size (MB)': 404,
'Quantized Size (MB)': 107,
'WER': 0.1566839,
'CER': 0.0619715,
'WER-LM': 0.1486221,
'CER-LM': 0.0590102,
'Language': ['malay'],
},
'conformer-stack-2mixed': {
'Size (MB)': 130,
'Quantized Size (MB)': 38.5,
'WER': 0.1036084,
'CER': 0.050069,
'WER-LM': 0.1029111,
'CER-LM': 0.0502013,
'Language': ['malay', 'singlish'],
},
'conformer-stack-3mixed': {
'Size (MB)': 130,
'Quantized Size (MB)': 38.5,
'WER': 0.2347684,
'CER': 0.133944,
'WER-LM': 0.229241,
'CER-LM': 0.1307018,
'Language': ['malay', 'singlish', 'mandarin'],
},
'small-conformer-singlish': {
'Size (MB)': 49.2,
'Quantized Size (MB)': 18.1,
'WER': 0.0878310,
'CER': 0.0456859,
'WER-LM': 0.08733263,
'CER-LM': 0.04531657,
'Language': ['singlish'],
},
'conformer-singlish': {
'Size (MB)': 125,
'Quantized Size (MB)': 37.1,
'WER': 0.07779246,
'CER': 0.0403616,
'WER-LM': 0.07718602,
'CER-LM': 0.03986952,
'Language': ['singlish'],
},
'large-conformer-singlish': {
'Size (MB)': 404,
'Quantized Size (MB)': 107,
'WER': 0.07014733,
'CER': 0.03587201,
'WER-LM': 0.06981206,
'CER-LM': 0.03572307,
'Language': ['singlish'],
},
'xs-squeezeformer': {
'Size (MB)': 51.9,
'Quantized Size (MB)': 23.4,
'WER': 0.19809173,
'CER': 0.07903460,
'WER-LM': 0.19884239,
'CER-LM': 0.07812183,
'Language': ['malay'],
},
'sm-squeezeformer': {
'Size (MB)': 147,
'Quantized Size (MB)': 47.4,
'WER': 0.176126847,
'CER': 0.0680792,
'WER-LM': 0.1687297,
'CER-LM': 0.061468,
'Language': ['malay'],
},
'm-squeezeformer': {
'Size (MB)': 261,
'Quantized Size (MB)': 78.5,
'WER': 0.16700751,
'CER': 0.05972837,
'WER-LM': 0.15618489,
'CER-LM': 0.05363883,
'Language': ['malay'],
},
}
_ctc_availability = {
'hubert-conformer-tiny': {
'Size (MB)': 36.6,
'Quantized Size (MB)': 10.3,
'WER': 0.3359682,
'CER': 0.0882573,
'WER-LM': 0.1992265,
'CER-LM': 0.0635223,
'Language': ['malay'],
},
'hubert-conformer': {
'Size (MB)': 115,
'Quantized Size (MB)': 31.1,
'WER': 0.238714,
'CER': 0.0608998,
'WER-LM': 0.1414791,
'CER-LM': 0.0450751,
'Language': ['malay'],
},
'hubert-conformer-large': {
'Size (MB)': 392,
'Quantized Size (MB)': 100,
'WER': 0.2203140,
'CER': 0.0549270,
'WER-LM': 0.1280064,
'CER-LM': 0.03853289,
'Language': ['malay'],
},
'hubert-conformer-large-3mixed': {
'Size (MB)': 392,
'Quantized Size (MB)': 100,
'WER': 0.2411256,
'CER': 0.0787939,
'WER-LM': 0.13276059,
'CER-LM': 0.05748197,
'Language': ['malay', 'singlish', 'mandarin'],
},
'best-rq-conformer-tiny': {
'Size (MB)': 36.6,
'Quantized Size (MB)': 10.3,
'WER': 0.3192907,
'CER': 0.078988,
'WER-LM': 0.179582,
'CER-LM': 0.055521,
'Language': ['malay'],
},
'best-rq-conformer': {
'Size (MB)': 115,
'Quantized Size (MB)': 31.1,
'WER': 0.2536784,
'CER': 0.0658045,
'WER-LM': 0.1542058,
'CER-LM': 0.0482278,
'Language': ['malay'],
},
'best-rq-conformer-large': {
'Size (MB)': 392,
'Quantized Size (MB)': 100,
'WER': 0.2346511,
'CER': 0.0601605,
'WER-LM': 0.1300819,
'CER-LM': 0.044521,
'Language': ['malay'],
},
}
# https://huggingface.co/mesolitica/wav2vec2-xls-r-300m-mixed
_huggingface_availability = {
'mesolitica/wav2vec2-xls-r-300m-mixed': {
'Size (MB)': 1180,
'WER': 0.1322198,
'CER': 0.0481054,
'WER-LM': 0.0988016,
'CER-LM': 0.0411965,
'Language': ['malay', 'singlish', 'mandarin'],
},
}
google_accuracy = {
'malay': {
'WER': 0.164775,
'CER': 0.0597320,
},
'singlish': {
'WER': 0.4941349,
'CER': 0.3026296,
}
}
[docs]def available_ctc():
"""
List available Encoder-CTC ASR models.
"""
return describe_availability(_ctc_availability)
[docs]def available_transducer():
"""
List available Encoder-Transducer ASR models.
"""
return describe_availability(_transducer_availability)
[docs]def available_huggingface():
"""
List available HuggingFace Malaya-Speech ASR models.
"""
return describe_availability(_huggingface_availability)
[docs]@check_type
def deep_ctc(
model: str = 'hubert-conformer', quantized: bool = False, **kwargs
):
"""
Load Encoder-CTC ASR model.
Parameters
----------
model : str, optional (default='hubert-conformer')
Check available models at `malaya_speech.stt.available_ctc()`.
quantized : bool, optional (default=False)
if True, will load 8-bit quantized model.
Quantized model not necessary faster, totally depends on the machine.
Returns
-------
result : malaya_speech.model.wav2vec.Wav2Vec2_CTC class
"""
model = model.lower()
if model not in _ctc_availability:
raise ValueError(
'model not supported, please check supported models from `malaya_speech.stt.available_ctc()`.'
)
return stt.wav2vec2_ctc_load(
model=model,
module='speech-to-text-ctc-v2',
quantized=quantized,
mode=_ctc_availability[model],
**kwargs
)
[docs]@check_type
def deep_transducer(
model: str = 'conformer', quantized: bool = False, **kwargs
):
"""
Load Encoder-Transducer ASR model.
Parameters
----------
model : str, optional (default='conformer')
Check available models at `malaya_speech.stt.available_transducer()`.
quantized : bool, optional (default=False)
if True, will load 8-bit quantized model.
Quantized model not necessary faster, totally depends on the machine.
Returns
-------
result : malaya_speech.model.transducer.Transducer class
"""
model = model.lower()
if model not in _transducer_availability:
raise ValueError(
'model not supported, please check supported models from `malaya_speech.stt.available_transducer()`.'
)
return stt.transducer_load(
model=model,
module='speech-to-text-transducer',
languages=_transducer_availability[model]['Language'],
quantized=quantized,
**kwargs
)
[docs]@check_type
def huggingface(model: str = 'mesolitica/wav2vec2-xls-r-300m-mixed', **kwargs):
"""
Load Finetuned models from HuggingFace. Required Tensorflow >= 2.0.
Parameters
----------
model : str, optional (default='mesolitica/wav2vec2-xls-r-300m-mixed')
Check available models at `malaya_speech.stt.available_huggingface()`.
Returns
-------
result : malaya_speech.model.huggingface.CTC class
"""
model = model.lower()
if model not in _huggingface_availability:
raise ValueError(
'model not supported, please check supported models from `malaya_speech.stt.available_huggingface()`.'
)
return stt.huggingface_load(model=model, **kwargs)
