How to use Punctuation Models in FunASR

[chenmengzheAAA]

There are two types of punctuation models, offline and online.

1. The offline model, refer to punc offline for more details.

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

inference_pipeline = pipeline(
    task=Tasks.punctuation,
    model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch'
)

rec_result = inference_pipeline(text_in='example/punc_example.txt')
print(rec_result)

The "text_in" supports three formats: raw inputs, text files, url files.

2. The online model, refer to punc online for more details.

Different from the offline model, the online model is used at each time of speech absence of voice activity detection(VAD) and
should be feed the cache extracted from the history text.

In the following code , we mark puctuation points on the pseudo streaming text where "|" represents the split points of VAD.

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

inference_pipeline = pipeline(
    task=Tasks.punctuation,
    model='damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727'
)

inputs = "跨境河流是养育沿岸|人民的生命之源长期以来为帮助下游地区防灾减灾中方技术人员|在上游地区极为恶劣的自然条件下克服巨大困难甚至冒着生命危险|向印方提供汛期水文资料处理紧急事件中方重视印方在跨境河流问题上的关切|愿意进一步完善双方联合工作机制|凡是|中方能做的我们|都会去做而且会做得更好我请印度朋友们放心中国在上游的|任何开发利用都会经过科学|规划和论证兼顾上下游的利益"
vads = inputs.split("|")
rec_result_all="outputs:"
param_dict = {"cache": []}
for vad in vads:
    rec_result = inference_pipeline(text_in=vad, param_dict=param_dict)
    rec_result_all += rec_result['text']

print(rec_result_all)