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 sys
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import os
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import soundfile
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if __name__=="__main__":
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path = sys.argv[1] # dump2/raw/Eval_Ali_far
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raw_path = sys.argv[2] # data/local/Eval_Ali_far_correct_single_speaker
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raw_meeting_scp_file = open(raw_path + '/wav_raw.scp', 'r')
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raw_meeting_scp = raw_meeting_scp_file.readlines()
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raw_meeting_scp_file.close()
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segments_scp_file = open(raw_path + '/segments', 'r')
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segments_scp = segments_scp_file.readlines()
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segments_scp_file.close()
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oracle_emb_dir = path + '/oracle_embedding/'
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os.system("mkdir -p " + oracle_emb_dir)
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oracle_emb_scp_file = open(path+'/oracle_embedding.scp', 'w')
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raw_wav_map = {}
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for line in raw_meeting_scp:
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meeting = line.strip().split('\t')[0]
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wav_path = line.strip().split('\t')[1]
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raw_wav_map[meeting] = wav_path
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spk_map = {}
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for line in segments_scp:
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line_list = line.strip().split(' ')
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meeting = line_list[1]
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spk_id = line_list[0].split('_')[3]
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spk = meeting + '_' + spk_id
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time_start = float(line_list[-2])
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time_end = float(line_list[-1])
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if time_end - time_start > 0.5:
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if spk not in spk_map.keys():
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spk_map[spk] = [(int(time_start * 16000), int(time_end * 16000))]
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else:
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spk_map[spk].append((int(time_start * 16000), int(time_end * 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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)
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for spk in spk_map.keys():
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meeting = spk.split('_SPK')[0]
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wav_path = raw_wav_map[meeting]
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wav = soundfile.read(wav_path)[0]
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# take the first channel
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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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# import ipdb;ipdb.set_trace()
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for seg_time in spk_map[spk]:
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if seg_time[0] < wav.shape[0] - 0.5 * 16000:
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if seg_time[1] > wav.shape[0]:
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cur_seg_embedding = inference_sv_pipline(audio_in=wav[seg_time[0]: ])["spk_embedding"]
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else:
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cur_seg_embedding = inference_sv_pipline(audio_in=wav[seg_time[0]: seg_time[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(oracle_emb_dir + spk + '.npy', spk_embedding)
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oracle_emb_scp_file.write(spk + ' ' + oracle_emb_dir + spk + '.npy' + '\n')
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oracle_emb_scp_file.flush()
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oracle_emb_scp_file.close()
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