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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import pickle as pkl
import sys
import numpy as np
from scipy.interpolate import interp1d
from scipy.optimize import brentq
from sklearn.metrics import roc_curve
from tqdm import tqdm
"""
This script faciliates to get EER % based on cosine-smilarity
for Voxceleb dataset.
Args:
trial_file str: path to voxceleb trial file
emb : path to pickle file of embeddings dictionary (generated from spkr_get_emb.py)
save_kaldi_emb: if required pass this argument to save kaldi embeddings for KALDI PLDA training later
Note: order of audio files in manifest file should match the embeddings
"""
def get_acc(trial_file='', emb='', save_kaldi_emb=False):
trial_score = open('trial_score.txt', 'w')
dirname = os.path.dirname(trial_file)
with open(emb, 'rb') as f:
emb = pkl.load(f)
trial_embs = []
keys = []
all_scores = []
all_keys = []
# for each trials in trial file
with open(trial_file, 'r') as f:
tmp_file = f.readlines()
for line in tqdm(tmp_file):
line = line.strip()
truth, x_speaker, y_speaker = line.split()
x_speaker = x_speaker.split('/')
x_speaker = '@'.join(x_speaker)
y_speaker = y_speaker.split('/')
y_speaker = '@'.join(y_speaker)
X = emb[x_speaker]
Y = emb[y_speaker]
if save_kaldi_emb and x_speaker not in keys:
keys.append(x_speaker)
trial_embs.extend([X])
if save_kaldi_emb and y_speaker not in keys:
keys.append(y_speaker)
trial_embs.extend([Y])
score = np.dot(X, Y) / ((np.dot(X, X) * np.dot(Y, Y)) ** 0.5)
score = (score + 1) / 2
all_scores.append(score)
trial_score.write(str(score) + "\t" + truth)
truth = int(truth)
all_keys.append(truth)
trial_score.write('\n')
trial_score.close()
if save_kaldi_emb:
np.save(dirname + '/all_embs_voxceleb.npy', np.asarray(trial_embs))
np.save(dirname + '/all_ids_voxceleb.npy', np.asarray(keys))
print("Saved KALDI PLDA related embeddings to {}".format(dirname))
return np.asarray(all_scores), np.asarray(all_keys)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--trial_file", help="path to voxceleb trial file", type=str, required=True)
parser.add_argument("--emb", help="path to numpy file of embeddings", type=str, required=True)
parser.add_argument(
"--save_kaldi_emb",
help=":save kaldi embeddings for KALDI PLDA training later",
required=False,
action='store_true',
)
args = parser.parse_args()
trial_file, emb, save_kaldi_emb = args.trial_file, args.emb, args.save_kaldi_emb
y_score, y = get_acc(trial_file=trial_file, emb=emb, save_kaldi_emb=save_kaldi_emb)
fpr, tpr, thresholds = roc_curve(y, y_score, pos_label=1)
eer = brentq(lambda x: 1.0 - x - interp1d(fpr, tpr)(x), 0.0, 1.0)
sys.stdout.write("{0:.2f}\n".format(eer * 100))
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