pun_emo_speaker_utils/takway/stt/speaker_ver_utils.py

from modelscope.pipelines import pipeline
import numpy as np
import os
import pdb
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 = r".\takway\savePath"

class speaker_verfication:
    def __init__(self, 
                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

    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 _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,
                               ))