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test_model_speaker.py
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test_model_speaker.py
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import argparse
import configparser
import os
import pickle
import sys
from collections import OrderedDict
import numpy as np
from sklearn.metrics import roc_curve
from sklearn.preprocessing import normalize
from tqdm import tqdm
from math import log10, floor
import torch
import uvloop
from data_io_speaker import SpeakerTestDataset
from kaldiio import ReadHelper
from models_speaker import ETDNN, FTDNN, XTDNN
from utils import SpeakerRecognitionMetrics
from sklearn.metrics.pairwise import cosine_similarity
def mtd(stuff, device):
if isinstance(stuff, torch.Tensor):
return stuff.to(device)
else:
return [mtd(s, device) for s in stuff]
def parse_args():
parser = argparse.ArgumentParser(description='Test SV Model')
parser.add_argument('--cfg', type=str, default='./configs/example_speaker.cfg')
parser.add_argument('--best', action='store_true', default=False, help='Use best model')
parser.add_argument('--checkpoint', type=int, default=-1, # which model to use, overidden by 'best'
help='Use model checkpoint, default -1 uses final model')
args = parser.parse_args()
assert os.path.isfile(args.cfg)
return args
def parse_config(args):
config = configparser.ConfigParser()
config.read(args.cfg)
args.test_data_vc1 = config['Datasets'].get('test_vc1')
print('VC1 dataset: {}'.format(args.test_data_vc1))
args.test_data_sitw = config['Datasets'].get('test_sitw')
print('SITW dataset dev: {}'.format(args.test_data_sitw))
args.test_data_sitw_eval = config['Datasets'].get('test_sitw_eval')
print('SITW dataset eval: {}'.format(args.test_data_sitw_eval))
args.no_cuda = config['Hyperparams'].getboolean('no_cuda', fallback=False)
args.model_type = config['Model'].get('model_type', fallback='XTDNN')
assert args.model_type in ['XTDNN', 'ETDNN', 'FTDNN']
args.num_iterations = config['Hyperparams'].getint('num_iterations', fallback=50000)
args.model_dir = config['Outputs']['model_dir']
return args
def test(generator, ds_test, device, mindcf=False):
generator.eval()
all_embeds = []
all_utts = []
num_examples = len(ds_test.veri_utts)
with torch.no_grad():
for i in tqdm(range(num_examples)):
feats, utt = ds_test.__getitem__(i)
feats = feats.unsqueeze(0).to(device)
embeds = generator(feats)
all_embeds.append(embeds.cpu().numpy())
all_utts.append(utt)
metric = SpeakerRecognitionMetrics(distance_measure='cosine')
all_embeds = np.vstack(all_embeds)
all_embeds = normalize(all_embeds, axis=1)
all_utts = np.array(all_utts)
print(all_embeds.shape, len(ds_test.veri_utts))
utt_embed = OrderedDict({k:v for k, v in zip(all_utts, all_embeds)})
emb0 = np.array([utt_embed[utt] for utt in ds_test.veri_0])
emb1 = np.array([utt_embed[utt] for utt in ds_test.veri_1])
scores = metric.scores_from_pairs(emb0, emb1)
fpr, tpr, thresholds = roc_curve(1 - ds_test.veri_labs, scores, pos_label=1, drop_intermediate=False)
eer = metric.eer_from_ers(fpr, tpr)
generator.train()
if mindcf:
mindcf1 = metric.compute_min_dcf(fpr, tpr, thresholds, p_target=0.01)
mindcf2 = metric.compute_min_dcf(fpr, tpr, thresholds, p_target=0.001)
return eer, mindcf1, mindcf2
else:
return eer
def test_nosil(generator, ds_test, device, mindcf=False):
generator.eval()
all_embeds = []
all_utts = []
num_examples = len(ds_test.veri_utts)
with torch.no_grad():
with ReadHelper('ark:apply-cmvn-sliding --norm-vars=false --center=true --cmn-window=300 scp:{0}/feats_trimmed.scp ark:- | select-voiced-frames ark:- scp:{0}/vad_trimmed.scp ark:- |'.format(ds_test.data_base_path)) as reader:
for key, feat in tqdm(reader, total=num_examples):
if key in ds_test.veri_utts:
all_utts.append(key)
feats = torch.FloatTensor(feat).unsqueeze(0).to(device)
embeds = generator(feats)
all_embeds.append(embeds.cpu().numpy())
metric = SpeakerRecognitionMetrics(distance_measure='cosine')
all_embeds = np.vstack(all_embeds)
all_embeds = normalize(all_embeds, axis=1)
all_utts = np.array(all_utts)
print(all_embeds.shape, len(ds_test.veri_utts))
utt_embed = OrderedDict({k:v for k, v in zip(all_utts, all_embeds)})
emb0 = np.array([utt_embed[utt] for utt in ds_test.veri_0])
emb1 = np.array([utt_embed[utt] for utt in ds_test.veri_1])
scores = metric.scores_from_pairs(emb0, emb1)
fpr, tpr, thresholds = roc_curve(1 - ds_test.veri_labs, scores, pos_label=1, drop_intermediate=False)
eer = metric.eer_from_ers(fpr, tpr)
generator.train()
if mindcf:
mindcf1 = metric.compute_min_dcf(fpr, tpr, thresholds, p_target=0.01)
mindcf2 = metric.compute_min_dcf(fpr, tpr, thresholds, p_target=0.001)
return eer, mindcf1, mindcf2
else:
return eer
def evaluate_deepmine(generator, ds_eval, device, outfile_path='./exp'):
generator.eval()
answer_col0 = []
answer_col1 = []
answer_col2 = []
with torch.no_grad():
for i in tqdm(range(len(ds_eval))):
model, enrol_utts, enrol_feats, eval_utts, eval_feats = ds_eval.__getitem__(i)
answer_col0.append([model for _ in range(len(eval_utts))])
answer_col1.append(eval_utts)
enrol_feats = mtd(enrol_feats, device)
model_embed = torch.cat([generator(x.unsqueeze(0)) for x in enrol_feats]).cpu().numpy()
model_embed = np.mean(normalize(model_embed, axis=1), axis=0).reshape(1, -1)
del enrol_feats
eval_utts = mtd(eval_utts, device)
eval_embeds = torch.cat([generator(x.unsqueeze(0)) for x in eval_feats]).cpu().numpy()
eval_embeds = normalize(eval_embeds, axis=1)
scores = cosine_similarity(model_embed, eval_embeds).squeeze(0)
assert len(scores) == len(eval_utts)
answer_col2.append(scores)
del eval_feats
answer_col0 = np.concatenate(answer_col0)
answer_col1 = np.concatenate(answer_col1)
answer_col2 = np.concatenate(answer_col2)
with open(os.path.join(outfile_path, 'answer_full.txt'), 'w+') as fp:
for m, ev, s in zip(answer_col0, answer_col1, answer_col2):
line = '{} {} {}\n'.format(m, ev, s)
fp.write(line)
with open(os.path.join(outfile_path, 'answer.txt'), 'w+') as fp:
for s in answer_col2:
line = '{}\n'.format(s)
fp.write(line)
if (answer_col0 == np.array(ds_eval.models_eval)).all():
print('model ordering matched')
else:
print('model ordering was not correct, need to fix before submission')
if (answer_col1 == np.array(ds_eval.eval_utts)).all():
print('eval utt ordering matched')
else:
print('eval utt ordering was not correct, need to fix before submission')
def round_sig(x, sig=2):
return round(x, sig-int(floor(log10(abs(x))))-1)
if __name__ == "__main__":
args = parse_args()
args = parse_config(args)
uvloop.install()
use_cuda = not args.no_cuda and torch.cuda.is_available()
print('='*30)
print('USE_CUDA SET TO: {}'.format(use_cuda))
print('CUDA AVAILABLE?: {}'.format(torch.cuda.is_available()))
print('='*30)
device = torch.device("cuda" if use_cuda else "cpu")
if args.checkpoint == -1:
g_path = os.path.join(args.model_dir, "final_g_{}.pt".format(args.num_iterations))
g_path_sitw = g_path
g_path_vc1 = g_path
else:
g_path = os.path.join(args.model_dir, "g_{}.pt".format(args.checkpoint))
g_path_sitw = g_path
g_path_vc1 = g_path
if args.model_type == 'XTDNN':
generator = XTDNN()
if args.model_type == 'ETDNN':
generator = ETDNN()
if args.model_type == 'FTDNN':
generator = FTDNN()
if args.best:
args.results_pkl = os.path.join(args.model_dir, 'results.p')
rpkl = pickle.load(open(args.results_pkl, "rb"))
if args.test_data_vc1:
v1eers = [(rpkl[key]['vc1_eer'], key) for key in rpkl]
best_vc1_cp = min(v1eers)[1]
g_path_vc1 = os.path.join(args.model_dir, "g_{}.pt".format(best_vc1_cp))
print('Best VC1 Model: {}'.format(g_path_vc1))
if args.test_data_sitw:
sitweers = [(rpkl[key]['sitw_eer'], key) for key in rpkl]
best_sitw_cp = min(sitweers)[1]
g_path_sitw = os.path.join(args.model_dir, "g_{}.pt".format(best_sitw_cp))
print('Best SITW Model: {}'.format(g_path_sitw))
if args.test_data_vc1:
ds_test_vc1 = SpeakerTestDataset(args.test_data_vc1)
generator.load_state_dict(torch.load(g_path_vc1))
generator = generator.to(device)
vc1_eer, vc1_mdcf1, vc1_mdcf2 = test(generator, ds_test_vc1, device, mindcf=True)
print("="*60)
print('VC1:: \t EER: {}, minDCF(p=0.01): {}, minDCF(p=0.001): {}'.format(round_sig(vc1_eer, 3), round_sig(vc1_mdcf1, 3), round_sig(vc1_mdcf2, 3)))
print("="*60)
if args.test_data_sitw:
ds_test_sitw = SpeakerTestDataset(args.test_data_sitw)
generator.load_state_dict(torch.load(g_path_sitw))
generator = generator.to(device)
sitw_eer, sitw_mdcf1, sitw_mdcf2 = test_nosil(generator, ds_test_sitw, device, mindcf=True)
print("="*60)
print('SITW(dev):: \t EER: {}, minDCF(p=0.01): {}, minDCF(p=0.001): {}'.format(round_sig(sitw_eer, 3), round_sig(sitw_mdcf1, 3), round_sig(sitw_mdcf2, 3)))
print("="*60)
if args.test_data_sitw_eval:
ds_test_sitw_eval = SpeakerTestDataset(args.test_data_sitw_eval)
generator.load_state_dict(torch.load(g_path_sitw))
generator = generator.to(device)
sitw_eval_eer, sitw_eval_mdcf1, sitw_eval_mdcf2 = test_nosil(generator, ds_test_sitw_eval, device, mindcf=True)
print("="*60)
print('SITW(eval):: \t EER: {}, minDCF(p=0.01): {}, minDCF(p=0.001): {}'.format(round_sig(sitw_eval_eer, 3), round_sig(sitw_eval_mdcf1, 3), round_sig(sitw_eval_mdcf2, 3)))
print("="*60)