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
|
|
if __name__ == '__main__':
|
output_dir = None
|
inference_pipline = pipeline(
|
task=Tasks.voice_activity_detection,
|
model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
|
model_revision='v1.2.0',
|
output_dir=output_dir,
|
batch_size=1,
|
mode='online',
|
)
|
speech, sample_rate = soundfile.read("./vad_example_16k.wav")
|
speech_length = speech.shape[0]
|
|
sample_offset = 0
|
|
step = 160 * 10
|
param_dict = {'in_cache': dict(), 'max_end_sil': 800}
|
for sample_offset in range(0, speech_length, min(step, speech_length - sample_offset)):
|
if sample_offset + step >= speech_length - 1:
|
step = speech_length - sample_offset
|
is_final = True
|
else:
|
is_final = False
|
param_dict['is_final'] = is_final
|
segments_result = inference_pipline(audio_in=speech[sample_offset: sample_offset + step],
|
param_dict=param_dict)
|
print(segments_result)
|
