FunASR/tests/test_sv_inference_pipeline.py
speech_asr 7989e476da update
2023-03-16 20:25:28 +08:00

49 lines
1.9 KiB
Python

import unittest
import numpy as np
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
logger = get_logger()
class TestXVectorInferencePipelines(unittest.TestCase):
def test_funasr_path(self):
import funasr
import os
logger.info("run_dir:{0} ; funasr_path: {1}".format(os.getcwd(), funasr.__file__))
def test_inference_pipeline(self):
inference_sv_pipline = pipeline(
task=Tasks.speaker_verification,
model='damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch'
)
# 提取不同句子的说话人嵌入码
rec_result = inference_sv_pipline(
audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_enroll.wav')
enroll = rec_result["spk_embedding"]
rec_result = inference_sv_pipline(
audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_same.wav')
same = rec_result["spk_embedding"]
rec_result = inference_sv_pipline(
audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/sv_example_different.wav')
different = rec_result["spk_embedding"]
# 对相同的说话人计算余弦相似度
sv_threshold = 0.9465
same_cos = np.sum(enroll * same) / (np.linalg.norm(enroll) * np.linalg.norm(same))
same_cos = max(same_cos - sv_threshold, 0.0) / (1.0 - sv_threshold) * 100.0
logger.info("Similarity: {}".format(same_cos))
# 对不同的说话人计算余弦相似度
diff_cos = np.sum(enroll * different) / (np.linalg.norm(enroll) * np.linalg.norm(different))
diff_cos = max(diff_cos - sv_threshold, 0.0) / (1.0 - sv_threshold) * 100.0
logger.info("Similarity: {}".format(diff_cos))
if __name__ == '__main__':
unittest.main()