How to switch decoding mode for UniASR model

[hnluo]

There are three decoding mode for UniASR model(fast、normal、offline)，for more model detailes, please refer to UniASR

If you want to decode in fast mode

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825',
    param_dict={"decoding_model": "fast"})

rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
print(rec_result)

If you want to decode in normal mode

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825',
    param_dict={"decoding_model": "normal"})

rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
print(rec_result)

If you want to decode in offline mode

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825',
    param_dict={"decoding_model": "offline"})

rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
print(rec_result)

[lixikun]

Great job! but I test the model's rtf is slower than the paramform model, Do we have the plan to update to paramform model?

[suyufan]

I ran the above code and got an error
KeyError: 'asr-inference is not in the pipelines registry group auto-speech-recognition. Please make sure the correct version of ModelScope library is used.'

Environment：
Ubuntu
Python 3.9.19
funasr 1.0.22
modelscope 1.9.4
tensorflow 2.4.1
torch 2.2.2
cuda 11.7

Can you help me? give me your environments information.