From e33bb15d269bb3e2e41f7a3540d9b92703bb5c50 Mon Sep 17 00:00:00 2001
From: aky15 <ankeyu.aky@11.17.44.249>
Date: 星期三, 15 三月 2023 10:51:52 +0800
Subject: [PATCH] Merge branch 'main' into dev_aky
---
egs_modelscope/speaker_verification/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch/infer.py | 39 +++++++++++++++++++++++++++++++++++++++
1 files changed, 39 insertions(+), 0 deletions(-)
diff --git a/egs_modelscope/speaker_verification/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch/infer.py b/egs_modelscope/speaker_verification/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch/infer.py
new file mode 100644
index 0000000..1fd9dc6
--- /dev/null
+++ b/egs_modelscope/speaker_verification/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch/infer.py
@@ -0,0 +1,39 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+import numpy as np
+
+if __name__ == '__main__':
+ inference_sv_pipline = pipeline(
+ task=Tasks.speaker_verification,
+ model='damo/speech_xvector_sv-en-us-callhome-8k-spk6135-pytorch'
+ )
+
+ # extract speaker embedding
+ # for url use "spk_embedding" as key
+ rec_result = inference_sv_pipline(
+ audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/sv_example_enroll.wav')
+ enroll = rec_result["spk_embedding"]
+
+ # for local file use "spk_embedding" as key
+ rec_result = inference_sv_pipline(audio_in='example/sv_example_same.wav')
+ same = rec_result["spk_embedding"]
+
+ import soundfile
+ wav = soundfile.read('example/sv_example_enroll.wav')[0]
+ # for raw inputs use "spk_embedding" as key
+ spk_embedding = inference_sv_pipline(audio_in=wav)["spk_embedding"]
+
+ rec_result = inference_sv_pipline(
+ audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_data/sv_example_different.wav')
+ different = rec_result["spk_embedding"]
+
+ # calculate cosine similarity for same speaker
+ sv_threshold = 0.80
+ 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
+ print("Similarity:", same_cos)
+
+ # calculate cosine similarity for different speaker
+ 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
+ print("Similarity:", diff_cos)
--
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