#-------------------------------------------
# Basic information
#-------------------------------------------
team: your_team_name
team_email: email_for_receiving_notification
team_members:
# Member 1
1:
name: member1_name
affiliation: member1_affiliation
# Member 2
2:
name: member2_name
affiliation: member2_affiliation
# Member 3
3:
name: member3_name
affiliation: member3_affiliation
#-------------------------------------------
# Data description
#-------------------------------------------
# e.g., "bvcc bc19 somos tmhint-qi pstn tcd-voip tencent nisqa ttsds2 urgent24-sqa urgnet25-sqa"
training_data:
# e.g., "same as official validation data"
validation_data:
#-------------------------------------------
# System Description
#-------------------------------------------
# System types: provide one or more descriptions (free-form text allowed).
# You may use common terms or your own description.
# Examples:
# - listener dependent: Model takes listener id as input
# - domain dependent: Model takes domain id as input
# - language dependent: Model takes language id as input
# - single-metric predictor: Predicts only MOS
# - multi-metric predictor: Predicts multiple metrics (e.g., MOS, SCOREQ, DNSMOS)
# - supervised: Fully supervised learning
# - semi-supervised: Uses labeled + unlabeled data
# - self-supervised: Uses self-supervised pretraining
# - ensemble: Combines multiple models
system_type: []
# Briefly describe the architecture here
# e.g., CNN, Transformer.
model_architecture:
# model parameter size in million, e.g., 6.23 [M]
model_size:
# Loss function(s) used, e.g., "CrossEntropy", "MSE"
loss_function:
use_pretrained_models: # true or false
pretrained_models:
# example:
# name: HuBERT-Large
# link: https://huggingface.co/facebook/hubert-large-ll60k
# usage: used for converting the input speech signal into discrete tokens
# Pre-trained model 1
1:
name:
link:
usage:
# Pre-trained model 2
2:
name:
link:
usage: