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import argparse |
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import os |
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import pickle as pkl |
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import sys |
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import numpy as np |
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from scipy.interpolate import interp1d |
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from scipy.optimize import brentq |
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from sklearn.metrics import roc_curve |
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from tqdm import tqdm |
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""" |
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This script faciliates to get EER % based on cosine-smilarity |
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for Voxceleb dataset. |
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Args: |
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trial_file str: path to voxceleb trial file |
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emb : path to pickle file of embeddings dictionary (generated from spkr_get_emb.py) |
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save_kaldi_emb: if required pass this argument to save kaldi embeddings for KALDI PLDA training later |
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Note: order of audio files in manifest file should match the embeddings |
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""" |
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def get_acc(trial_file='', emb='', save_kaldi_emb=False): |
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trial_score = open('trial_score.txt', 'w') |
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dirname = os.path.dirname(trial_file) |
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with open(emb, 'rb') as f: |
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emb = pkl.load(f) |
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trial_embs = [] |
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keys = [] |
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all_scores = [] |
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all_keys = [] |
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with open(trial_file, 'r') as f: |
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tmp_file = f.readlines() |
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for line in tqdm(tmp_file): |
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line = line.strip() |
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truth, x_speaker, y_speaker = line.split() |
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x_speaker = x_speaker.split('/') |
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x_speaker = '@'.join(x_speaker) |
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y_speaker = y_speaker.split('/') |
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y_speaker = '@'.join(y_speaker) |
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X = emb[x_speaker] |
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Y = emb[y_speaker] |
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if save_kaldi_emb and x_speaker not in keys: |
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keys.append(x_speaker) |
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trial_embs.extend([X]) |
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if save_kaldi_emb and y_speaker not in keys: |
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keys.append(y_speaker) |
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trial_embs.extend([Y]) |
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score = np.dot(X, Y) / ((np.dot(X, X) * np.dot(Y, Y)) ** 0.5) |
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score = (score + 1) / 2 |
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all_scores.append(score) |
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trial_score.write(str(score) + "\t" + truth) |
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truth = int(truth) |
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all_keys.append(truth) |
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trial_score.write('\n') |
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trial_score.close() |
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if save_kaldi_emb: |
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np.save(dirname + '/all_embs_voxceleb.npy', np.asarray(trial_embs)) |
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np.save(dirname + '/all_ids_voxceleb.npy', np.asarray(keys)) |
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print("Saved KALDI PLDA related embeddings to {}".format(dirname)) |
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return np.asarray(all_scores), np.asarray(all_keys) |
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if __name__ == "__main__": |
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parser = argparse.ArgumentParser() |
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parser.add_argument("--trial_file", help="path to voxceleb trial file", type=str, required=True) |
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parser.add_argument("--emb", help="path to numpy file of embeddings", type=str, required=True) |
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parser.add_argument( |
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"--save_kaldi_emb", |
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help=":save kaldi embeddings for KALDI PLDA training later", |
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required=False, |
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action='store_true', |
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) |
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args = parser.parse_args() |
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trial_file, emb, save_kaldi_emb = args.trial_file, args.emb, args.save_kaldi_emb |
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y_score, y = get_acc(trial_file=trial_file, emb=emb, save_kaldi_emb=save_kaldi_emb) |
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fpr, tpr, thresholds = roc_curve(y, y_score, pos_label=1) |
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eer = brentq(lambda x: 1.0 - x - interp1d(fpr, tpr)(x), 0.0, 1.0) |
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sys.stdout.write("{0:.2f}\n".format(eer * 100)) |
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