wenet runtime binding
wenet python wrapper for runtime
note： Because wenet support binding runtime，so archive this repo
if you‘re interesting in ctc decoder and mwer loss etc please see https://github.com/Mddct/ctcdecoder
pip install wenet
import wenet
model = wenet.from_pretrained("wenetspeech")
# for single infer
wav = 'example.wav'
model.recognize(wav, methods = "ctc_prefix_beam_search")
# for batch infer
wav_list = [example.wav, example.wav]
batch_size = 10
model.recognize_batch(wav_list, batch_size=10, methods = "attention_rescoring")
# with wfst lm
options = wenet.decoder.CtcPrefixBeamSearchOptions(blank = 0, first_beam_size=10, second_beam_size=10)
params = wenet.decoder.Params(ctc_options=options)
model = wenet.from_pretrained("wenetspeech", fst_path="TLG.fst", params=params)
# If options is used, the default decoding method in Options is used
model.recognize(wav)
model.recognize_batch(wav）
# for ctc probs
ctc_probs = model.recognize(wav, methods = "ctc_prefix_beam_search", ctc_prbos=True)
some useful ref:
https://github.com/daanzu/wenet_stt_python
https://cffi.readthedocs.io/en/latest/
http://www.swig.org/
There is no need to export decoder and accept waveform and ' reset etc to user
1 initial decoder in recognize and streaming_recognize
2 python : streaming recognize return type should be a
generator
Go: streaming recognize return type should be a channel
3 when we want to change the config of current model to do next decoding, model.config[...] = ... should work
Therefore, internal streaming and non streaming depend only on the chunk size
[{"sentence":"甚至出现"}]��=0
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