def main(args):
jsons = {
'train': 'dump/train_si284/deltafalse/data.json',
'dev': 'dump/test_dev93/deltafalse/data.json',
'test': 'dump/test_eval92/deltafalse/data.json'
}
spk2genders = {
'train': 'train_si284/spk2gender',
'dev': 'test_dev93/spk2gender',
'test': 'test_eval92/spk2gender'
}
utils.safe_copytree(args.data_root, args.temp_root)
if not os.path.exists(args.model_dir):
os.makedirs(args.model_dir)
logging.basicConfig(filename=os.path.join(args.model_dir, args.log_file),
filemode='a',
level=logging.INFO)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = args.determ
np.random.seed(args.seed)
if not args.eval_only:
train(args, jsons, spk2genders)
evaluate(args, jsons, spk2genders)
if args.cleanup:
utils.safe_rmtree(args.temp_root)
def main(args):
'''
Training and evaluation script for character-based CTC ASR on WSJ
dataset, pre-processed by ESPnet toolkit
'''
jsons = {
'train': 'dump/train_si284/deltafalse/data.json',
'dev': 'dump/test_dev93/deltafalse/data.json',
'test': 'dump/test_eval92/deltafalse/data.json'
}
# if the temporary directory contains a json, we'll assume it's correct
if not os.path.exists(os.path.join(args.temp_root, jsons['train'])):
# copy the data for faster reading than NFS
utils.safe_copytree(args.data_root, args.temp_root)
# if model_dir is specified, and it doesn't contain the log file, make it
log_file = os.path.join(args.model_dir, args.log_file)
if args.model_dir is not None and not os.path.exists(log_file):
utils.safe_makedirs(args.model_dir)
logging.basicConfig(filename=log_file, filemode='a', level=logging.INFO)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = not args.nondeterm
np.random.seed(args.seed)
if not args.eval_only:
utils.safe_json_dump(vars(args),
os.path.join(args.model_dir, 'args.json'))
epoch_stats = train(args, jsons)
utils.safe_json_dump(epoch_stats,
os.path.join(args.model_dir, 'epoch_stats.json'))
if args.eval_only:
data_root, temp_root = args.data_root, args.temp_root
test, cpu, seed, cleanup = args.test, args.cpu, args.seed, args.cleanup
with open(os.path.join(args.model_dir, 'args.json'), 'r') as f:
json_dict = json.load(f)
args = argparse.Namespace(**json_dict)
args.data_root, args.temp_root = data_root, temp_root
args.test, args.cpu, args.seed, args.cleanup = test, cpu, seed, cleanup
evaluate(args, jsons)
if args.cleanup:
utils.safe_rmtree(args.temp_root)
def generate_batch(batch):
host = 'http://112.124.117.97'
zip_dir = '/usr/share/nginx/html'
import tempfile
to_dir = tempfile.mkdtemp()
print to_dir
# generate all qrcode in that temp dir
for i in xrange(1, int(batch.count)+1):
record = Record(batch=batch, index=i,
serial_num=utils.generate_serial_num(),
left_time=batch.verify_time)
record.save()
url = urlparse.urljoin(host, record.serial_num)
filepath = os.path.join(to_dir, record.serial_num + '.png')
utils.generate_qrcode(url, filepath)
zip_filepath = os.path.join(zip_dir, batch.bid + '.zip')
utils.zipdir(to_dir, zip_filepath)
utils.safe_rmtree(to_dir)
if __name__ == '__main__':
connect('paibei')
host = 'http://112.124.117.97'
zip_dir = '/tmp/qrcode'
import tempfile
to_dir = tempfile.mkdtemp()
print to_dir
batch_id = sys.argv[1]
added_count = sys.argv[2]
batch = get_batch(batch_id)
currentIndex = batch.count
# query current record index in batch
for i in xrange(currentIndex + 1, currentIndex + added_count + 1):
record = Record(batch=batch, index=i,
serial_num=utils.generate_serial_num(),
left_time=batch.verify_time)
record.save()
url = urlparse.urljoin(host, batch.bid)
filepath = os.path.join(to_dir, record.serial_num + '.png')
utils.generate_qrcode(url, filepath)
zip_filepath = os.path.join(zip_dir, batch.bid + '.zip')
utils.zipdir(to_dir, zip_filepath)
utils.safe_rmtree(to_dir)
