import codecs
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import sys
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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import numpy as np
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import os
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import soundfile
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data_path = sys.argv[1]
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segment_file_path = data_path + "/segments_nooverlap"
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utt2spk_file_path = data_path + "/utt2spk_nooverlap"
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wav_scp_path = data_path + "/wav.scp"
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cluster_emb_dir = data_path + '/cluster_embedding/'
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os.system("mkdir -p " + cluster_emb_dir)
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cluster_profile_dir = data_path + '/cluster_profile_zeropadding16/'
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os.system('mkdir -p ' + cluster_profile_dir)
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utt2spk = {}
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spk2seg = {}
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with codecs.open(utt2spk_file_path, "r", "utf-8") as f1:
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with codecs.open(segment_file_path, "r", "utf-8") as f2:
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for line in f1.readlines():
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uttid, spkid = line.strip().split(" ")
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utt2spk[uttid] = spkid
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for line in f2.readlines():
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uttid, sessionid, stime, etime = line.strip().split(" ")
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spkid = utt2spk[uttid]
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if spkid not in spk2seg.keys():
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spk2seg[spkid] = [(int(float(stime) * 16000), int(float(etime) * 16000) - int(float(stime) * 16000))]
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else:
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spk2seg[spkid].append((int(float(stime) * 16000), int(float(etime) * 16000) - int(float(stime) * 16000)))
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inference_sv_pipline = pipeline(
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task=Tasks.speaker_verification,
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model='damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch',
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device='gpu'
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)
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wav_dict = {}
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with codecs.open(wav_scp_path, "r", "utf-8") as fi:
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with codecs.open(data_path + "/cluster_embedding.scp", "w", "utf-8") as fo:
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for line in fi.readlines():
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sessionid, wav_path = line.strip().split()
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wav_dict[sessionid] = wav_path
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for spkid, segs in spk2seg.items():
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sessionid = spkid.split("-")[0]
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wav_path = wav_dict[sessionid]
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wav = soundfile.read(wav_path)[0]
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if wav.ndim == 2:
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wav = wav[:, 0]
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all_seg_embedding_list=[]
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for seg in segs:
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if seg[0] < wav.shape[0] - 0.5 * 16000:
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if seg[1] > wav.shape[0]:
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cur_seg_embedding = inference_sv_pipline(audio_in=wav[seg[0]: ])["spk_embedding"]
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else:
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cur_seg_embedding = inference_sv_pipline(audio_in=wav[seg[0]: seg[0] + seg[1]])["spk_embedding"]
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all_seg_embedding_list.append(cur_seg_embedding)
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all_seg_embedding = np.vstack(all_seg_embedding_list)
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spk_embedding = np.mean(all_seg_embedding, axis=0)
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np.save(cluster_emb_dir + spkid + '.npy', spk_embedding)
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fo.write(spkid + ' ' + cluster_emb_dir + spkid + '.npy' + '\n')
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session2embs = {}
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with codecs.open(data_path + "/cluster_embedding.scp", "r", "utf-8") as fi:
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with codecs.open(data_path + "/cluster_profile_zeropadding16.scp", "w", "utf-8") as fo:
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for line in fi.readlines():
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spkid, emb_path = line.strip().split(" ")
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sessionid = spkid.split("-")[0]
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if sessionid not in session2embs.keys():
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session2embs[sessionid] = [emb_path]
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else:
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session2embs[sessionid].append(emb_path)
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for sessionid, embs in session2embs.items():
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emb_list = [np.load(x) for x in embs]
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tmp = []
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for i in range(len(emb_list) - 1):
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flag = True
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for j in range(i + 1, len(emb_list)):
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cos_sim = emb_list[i].dot(emb_list[j]) / (np.linalg.norm(emb_list[i]) * np.linalg.norm(emb_list[j]))
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if cos_sim > 0.99:
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flag = False
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if flag:
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tmp.append(emb_list[i][np.newaxis, :])
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tmp.append(emb_list[-1][np.newaxis, :])
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emb_list = tmp
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# tmp = []
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# for i in range(len(emb_list)):
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# for emb in tmp:
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# cos_sim = emb_list[i].dot(emb_list[j]) / (np.linalg.norm(emb_list[i]) * np.linalg.norm(emb_list[j]))
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# if cos_sim > 0.99:
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# flag = False
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# if flag:
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# tmp.append(emb_list[i][np.newaxis, :])
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# emb_list = tmp
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for i in range(16 - len(emb_list)):
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emb_list.append(np.zeros((1, 256)))
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emb = np.concatenate(emb_list, axis=0)
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save_path = cluster_profile_dir + sessionid + ".npy"
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np.save(save_path, emb)
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fo.write("%s %s\n" % (sessionid, save_path))
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