how to use vad, asr and punc model by pipeline

[LauraGPT]

how to split wav by vad model

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
import logging
logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)

import soundfile

waveform, sample_rate = soundfile.read("/home/zhifu.gzf/.cache/modelscope/hub/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav")
# fs = 16000

# vad pipeline
inference_pipeline_vad = pipeline(
    task=Tasks.voice_activity_detection,
    model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
    model_revision=None,
)
# asr pipeline
inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')
# punc pipeline
inference_pipeline_punc = pipeline(
    task=Tasks.punctuation,
    model='damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727',
    model_revision=None,
)

segments_result = inference_pipeline_vad(audio_in=waveform)
param_punc_dict = {"cache": []}
for i, segments in enumerate(segments_result["text"]):
    beg_idx = segments[0] * sample_rate/1000
    end_idx = segments[1] * sample_rate/1000
    waveform_slice = waveform[int(beg_idx):int(end_idx)]
    result_segments = inference_pipeline(audio_in=waveform_slice)
    if result_segments != []:
        result_segments_withpunc = inference_pipeline_punc(text_in=result_segments['text'], param_dict=param_punc_dict)
        print(result_segments_withpunc['text'])
    else:
        print(result_segments)
    

[AnitaSherry]

how to split wav by vad model

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
import logging
logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)

import soundfile

waveform, sample_rate = soundfile.read("/home/zhifu.gzf/.cache/modelscope/hub/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav")
# fs = 16000

inference_pipeline_vad = pipeline(
    task=Tasks.voice_activity_detection,
    model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
    model_revision=None,
)
inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')

segments_result = inference_pipeline_vad(audio_in=waveform)
for i, segments in enumerate(segments_result["text"]):
    beg_idx = segments[0] * sample_rate/1000
    end_idx = segments[1] * sample_rate/1000
    waveform_slice = waveform[int(beg_idx):int(end_idx)]
    result_segments = inference_pipeline(audio_in=waveform_slice)
    print(result_segments)
    

for i, segments in enumerate(segments_result["text"]):
这一行里，segments_result["text"]是字符串，应该加一个eval()

[LauraGPT]

how to split wav by vad model

from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
import logging
logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)

import soundfile

waveform, sample_rate = soundfile.read("/home/zhifu.gzf/.cache/modelscope/hub/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav")
# fs = 16000

inference_pipeline_vad = pipeline(
    task=Tasks.voice_activity_detection,
    model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
    model_revision=None,
)
inference_pipeline = pipeline(
    task=Tasks.auto_speech_recognition,
    model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch')

segments_result = inference_pipeline_vad(audio_in=waveform)
for i, segments in enumerate(segments_result["text"]):
    beg_idx = segments[0] * sample_rate/1000
    end_idx = segments[1] * sample_rate/1000
    waveform_slice = waveform[int(beg_idx):int(end_idx)]
    result_segments = inference_pipeline(audio_in=waveform_slice)
    print(result_segments)
    

for i, segments in enumerate(segments_result["text"]): 这一行里，segments_result["text"]是字符串，应该加一个eval()

I have changed the code, Please git pull and reinstall

[wwfcnu]

inference_pipeline(audio_in=waveform_slice)支持batch输入吗