speaker-checker

utils/speaker_checker_utils.py

@@ -0,0 +1,114 @@
+from modelscope.pipelines import pipeline
+import numpy as np
+import os
+
+ERES2NETV2 = {
+    "task": 'speaker-verification',
+    "model_name": 'damo/speech_eres2netv2_sv_zh-cn_16k-common',
+    "model_revision": 'v1.0.1',
+    "save_embeddings": False
+}
+
+# 保存 embedding 的路径
+DEFALUT_SAVE_PATH = os.path.join(os.path.dirname(os.path.dirname(__name__)), "speaker_embedding")
+
+class SpeakerChecker:
+    def __init__(self, 
+                speaker_wav_path,
+                task='speaker-verification',
+                model_name='damo/speech_eres2netv2_sv_zh-cn_16k-common',
+                model_revision='v1.0.1',
+                device="cuda",
+                save_embeddings=False,):
+        self.pipeline = pipeline(
+                task=task,
+                model=model_name,
+                model_revision=model_revision,
+                device=device)
+        self.save_embeddings = save_embeddings
+
+        
+        self.update_embedding_with_wav(speaker_wav_path)
+
+    # save path 为 none 时     不将 speaker_wav_path 对应音频的 embedding 存在本地
+    # save_path 不为 none 时   将 speaker_wav_path 对应音频的 embedding 存在本地对应位置
+    def update_embedding_with_wav(self, speaker_wav_path, save_path=None):
+        self.speaker_1_emb = self.wav2embeddings(speaker_wav_path, save_path)
+    
+    def update_embedding_with_np(self, speaker_emb_path):
+        self.speaker_1_emb = np.load(speaker_emb_path)
+
+    def wav2embeddings(self, speaker_1_wav, save_path=None):
+        result = self.pipeline([speaker_1_wav], output_emb=True)
+        speaker_1_emb = result['embs'][0]
+        if save_path is not None:
+            np.save(save_path, speaker_1_emb)
+        return speaker_1_emb
+    
+    def checker(self, audio: str, threshold=0.333):
+        result = self.pipeline([audio], output_emb=True)
+        speaker2_emb = result["embs"][0]
+        similarity = np.dot(self.speaker_1_emb, speaker2_emb) / (np.linalg.norm(self.speaker_1_emb) * np.linalg.norm(speaker2_emb))
+        if similarity > threshold:
+            return True
+        else:
+            return False
+
+
+    # def _verifaction(self, speaker_1_wav, speaker_2_wav, threshold, save_path):
+    #     if not self.save_embeddings:
+    #         result = self.pipeline([speaker_1_wav, speaker_2_wav], thr=threshold)
+    #         return result["text"]
+    #     else:
+    #         result = self.pipeline([speaker_1_wav, speaker_2_wav], thr=threshold, output_emb=True)
+    #         speaker1_emb = result["embs"][0]
+    #         speaker2_emb = result["embs"][1]
+    #         np.save(os.path.join(save_path, "speaker_1.npy"), speaker1_emb)
+    #         return result['outputs']["text"]
+        
+    # def _verifaction_from_embedding(self, base_emb, speaker_2_wav, threshold):
+    #     base_emb = np.load(base_emb)
+    #     result = self.pipeline([speaker_2_wav], output_emb=True)
+    #     speaker2_emb = result["embs"][0]
+    #     similarity = np.dot(base_emb, speaker2_emb) / (np.linalg.norm(base_emb) * np.linalg.norm(speaker2_emb))
+    #     if similarity > threshold:
+    #         return "yes"
+    #     else:
+    #         return "no"
+    
+    # def verfication(self, 
+    #                 base_emb=None, 
+    #                 speaker_1_wav=None, 
+    #                 speaker_2_wav=None, 
+    #                 threshold=0.333, 
+    #                 save_path=None):
+    #     if base_emb is not None and speaker_1_wav is not None:
+    #         raise ValueError("Only need one of them, base_emb or speaker_1_wav")
+    #     if base_emb is not None and speaker_2_wav is not None:
+    #         return self._verifaction_from_embedding(base_emb, speaker_2_wav, threshold)
+    #     elif speaker_1_wav is not None and speaker_2_wav is not None:
+    #         return self._verifaction(speaker_1_wav, speaker_2_wav, threshold, save_path)
+    #     else:
+    #         raise NotImplementedError
+        
+if __name__ == '__main__':
+    # verifier = speaker_verfication(**ERES2NETV2)
+
+    # verifier = speaker_verfication(save_embeddings=False)
+    # result = verifier.verfication(base_emb=None, speaker_1_wav=r"C:\Users\bing\Downloads\speaker1_a_cn_16k.wav", 
+    #                               speaker_2_wav=r"C:\Users\bing\Downloads\speaker2_a_cn_16k.wav",
+    #                               threshold=0.333,
+    #                               save_path=r"D:\python\irving\takway_base-main\savePath"
+    #                               )
+    # print("---")
+    # print(result)
+    # print(verifier.verfication(r"D:\python\irving\takway_base-main\savePath\speaker_1.npy",
+    #                            speaker_2_wav=r"C:\Users\bing\Downloads\speaker1_b_cn_16k.wav",
+    #                            threshold=0.333,
+    #                            ))
+    speaker_wav_path = r"C:\Users\bing\Downloads\speaker1_a_cn_16k.wav"
+    speaker_checker = SpeakerChecker(speaker_wav_path)
+    audio = r"C:\Users\bing\Downloads\speaker1_b_cn_16k.wav"
+    is_target = speaker_checker.checker(audio)
+    print(is_target)
+