from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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import os
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import argparse
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import numpy as np
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--eval_dir", type=str, default="eval")
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parser.add_argument("--trials", type=str, default="eval/lists/trials.lst.speech")
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parser.add_argument("--out_dir", type=str, default="./")
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parser.add_argument("--verbose", action="store_true", default=False)
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args = parser.parse_args()
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if not os.path.exists(args.out_dir):
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os.makedirs(args.out_dir)
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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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trials_list = [x.strip() for x in open(args.trials, "r").readlines()]
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enroll_list = set([x.split(" ")[0] for x in trials_list])
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test_list = set([x.split(" ")[1] for x in trials_list])
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print("extract embeddings for {} enrollments".format(len(enroll_list)))
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enroll_embedding = {}
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for enroll in enroll_list:
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spk_embedding = inference_sv_pipline(
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audio_in=os.path.join(args.eval_dir, "enroll", enroll+".wav")
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)["spk_embedding"]
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enroll_embedding[enroll] = spk_embedding
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test_embedding = {}
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print("extract embeddings for {} tests".format(len(test_list)))
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for test in test_list:
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spk_embedding = inference_sv_pipline(
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audio_in=os.path.join(args.eval_dir, "test", test+".wav")
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)["spk_embedding"]
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test_embedding[test] = spk_embedding
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print("calculate scores for {} trials".format(len(trials_list)))
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fd = open(os.path.join(args.out_dir, "scores"), "w")
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for trial in trials_list:
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spk, utt, _ = trial.split(" ")
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spk_emb = enroll_embedding[spk]
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utt_emb = test_embedding[utt]
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score = np.sum(spk_emb * utt_emb) / (np.linalg.norm(spk_emb) * np.linalg.norm(utt_emb))
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fd.write("{} {} {:.5f}\n".format(spk, utt, score))
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fd.close()
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from funasr.utils.compute_eer import compute_eer
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from funasr.utils.compute_min_dcf import compute_min_dcf
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eer, threshold = compute_eer(args.trials, os.path.join(args.out_dir, "scores"))
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print("EER is {:.4f} at threshold {:.4f}".format(eer * 100.0, threshold))
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mindcf, threshold = compute_min_dcf(
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os.path.join(args.out_dir, "scores"), args.trials,
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c_miss=10, p_target=0.01
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)
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print("minDCF is {0:.4f} at threshold {1:.4f} (p-target={2}, c-miss={3}, c-fa={4})\n".format(
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mindcf, threshold, 0.01, 10, 1
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))
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if __name__ == '__main__':
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main()
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