From 92c4b128a15fa1a1d775839a0e5951b67f6ec3e1 Mon Sep 17 00:00:00 2001
From: speech_asr <wangjiaming.wjm@alibaba-inc.com>
Date: 星期五, 10 三月 2023 18:50:05 +0800
Subject: [PATCH] update unittest
---
tests/test_sv_inference_pipeline.py | 47 +++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 47 insertions(+), 0 deletions(-)
diff --git a/tests/test_sv_inference_pipeline.py b/tests/test_sv_inference_pipeline.py
new file mode 100644
index 0000000..265f839
--- /dev/null
+++ b/tests/test_sv_inference_pipeline.py
@@ -0,0 +1,47 @@
+import unittest
+
+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'
+ )
+ # 鎻愬彇涓嶅悓鍙ュ瓙鐨勮璇濅汉宓屽叆鐮�
+ 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"]
+
+ # 瀵圭浉鍚岀殑璇磋瘽浜鸿绠椾綑寮︾浉浼煎害
+ 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))
+
+ # 瀵逛笉鍚岀殑璇磋瘽浜鸿绠椾綑寮︾浉浼煎害
+ 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()
--
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