"""
|
Author: Speech Lab, Alibaba Group, China
|
TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization
|
https://arxiv.org/abs/2303.05397
|
"""
|
|
from modelscope.pipelines import pipeline
|
from modelscope.utils.constant import Tasks
|
|
# initialize the pipeline for inference
|
# when using the raw waveform files to inference, please use the config file `sond.yaml`
|
# and set mode to `sond_demo`
|
inference_diar_pipline = pipeline(
|
mode="sond_demo",
|
num_workers=0,
|
task=Tasks.speaker_diarization,
|
diar_model_config="sond.yaml",
|
model='damo/speech_diarization_sond-en-us-callhome-8k-n16k4-pytorch',
|
sv_model="damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch",
|
sv_model_revision="master",
|
)
|
|
# use audio_list as the input, where the first one is the record to be detected
|
# and the following files are enrollments for different speakers
|
audio_list = [
|
"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/record.wav",
|
"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_A.wav",
|
"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_B.wav",
|
"https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/spk_B1.wav"
|
]
|
|
results = inference_diar_pipline(audio_in=audio_list)
|
print(results)
|
