Introduce code from Rust monorepo. This is a mess

2026-10-23 08:26:06 +00:00 · 2022-02-28 05:43:54 -05:00
parent 921fb49f9a
commit ba326bfcfd
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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)
+    emb = pkl.load(open(emb, 'rb'))
+    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 = (X @ Y.T) / (((X @ X.T) * (Y @ Y.T)) ** 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))