from malaya_speech.supervised import stt
from malaya_speech.stt import _ctc_availability, _huggingface_availability
from malaya_speech.utils import describe_availability
from herpetologist import check_type
_availability = {
'conformer-transducer': {
'Size (MB)': 120,
'Quantized Size (MB)': 32.3,
'Language': ['malay'],
},
'conformer-transducer-mixed': {
'Size (MB)': 120,
'Quantized Size (MB)': 32.3,
'Language': ['malay', 'singlish'],
},
'conformer-transducer-singlish': {
'Size (MB)': 120,
'Quantized Size (MB)': 32.3,
'Language': ['singlish'],
},
}
[docs]def available_transducer():
"""
List available Encoder-Transducer Aligner models.
"""
return describe_availability(_availability)
[docs]def available_ctc():
"""
List available Encoder-CTC Aligner models.
"""
return describe_availability(_ctc_availability)
[docs]def available_huggingface():
"""
List available HuggingFace Malaya-Speech Aligner models.
"""
return describe_availability(_huggingface_availability)
[docs]@check_type
def deep_transducer(
model: str = 'conformer-transducer', quantized: bool = False, **kwargs
):
"""
Load Encoder-Transducer Aligner model.
Parameters
----------
model : str, optional (default='conformer-transducer')
Check available models at `malaya_speech.force_alignment.available_aligner()`.
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.TransducerAligner class
"""
model = model.lower()
if model not in _availability:
raise ValueError(
'model not supported, please check supported models from `malaya_speech.force_alignment.available_aligner()`.'
)
return stt.transducer_load(
model=model,
module='force-alignment',
languages=_availability[model]['Language'],
quantized=quantized,
stt=False,
**kwargs
)
[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_Aligner 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],
stt=False,
**kwargs
)
[docs]@check_type
def huggingface(model: str = 'mesolitica/wav2vec2-xls-r-300m-mixed'):
"""
Load Finetuned models from HuggingFace.
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, stt=False)
