OpenBEATs-ICME

OpenBEATs-ICME

This repository contains the audio encoder model presented in the paper The CMU-AIST submission for the ICME 2025 Audio Encoder Challenge.

Model Description

The system is built on BEATs, a masked speech token prediction-based audio encoder. This version scales the architecture up to 300 million parameters and was pre-trained using 74,000 hours of audio data derived from various speech, music, and sound corpora.

• Code: ESPnet GitHub
• Paper: The CMU-AIST submission for the ICME 2025 Audio Encoder Challenge

ESPnet2 SSL model

shikhar7ssu/OpenBEATs-ICME

This model was trained by Shikhar Bharadwaj using as2m recipe in espnet.

SSL config

<details><summary>expand</summary>

config: conf/pretrain_large_icme.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: /work/nvme/bbjs/sbharadwaj/icme_challenge/exp/beats_iter3_large.icme
ngpu: 1
seed: 0
num_workers: 4
num_att_plot: 0
dist_backend: nccl
dist_init_method: env://
dist_world_size: 4
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 37335
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
use_deepspeed: true
deepspeed_config: conf/ds_ear_large.json
gradient_as_bucket_view: true
ddp_comm_hook: null
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
use_tf32: false
collect_stats: false
write_collected_feats: false
max_epoch: 60
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - train
    - loss
    - min
-   - valid
    - loss
    - min
-   - train
    - acc
    - max
-   - valid
    - acc
    - max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: true
wandb_project: ICME
wandb_id: null
wandb_entity: shikhar
wandb_name: large.icme
wandb_model_log_interval: -1
detect_anomaly: false
use_adapter: false
adapter: lora
save_strategy: all
adapter_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1600000
valid_batch_bins: null
category_sample_size: 10
train_shape_file:
- /work/nvme/bbjs/sbharadwaj/icme_challenge/exp/beats_stats_fbank/splits5/speech_shape
- /work/nvme/bbjs/sbharadwaj/icme_challenge/exp/beats_stats_fbank/splits5/target_shape.word
valid_shape_file:
- /work/nvme/bbjs/sbharadwaj/icme_challenge/exp/beats_stats_fbank/valid/speech_shape
- /work/nvme/bbjs/sbharadwaj/icme_challenge/exp/beats_stats_fbank/valid/target_shape.word
batch_type: length
valid_batch_type: null
fold_length:
- 160000
- 600
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: true
utt2weight_file: null
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
chunk_default_fs: null
chunk_max_abs_length: null
chunk_discard_short_samples: true
train_data_path_and_name_and_type:
-   - /work/nvme/bbjs/sbharadwaj/icme_challenge/exp/beats_stats_fbank/splits5/feats.scp
    - speech
    - kaldi_ark
-   - /work/nvme/bbjs/sbharadwaj/icme_challenge/exp/beats_stats_fbank/splits5/target_iter3_beats_icme_inf
    - target
    - text
valid_data_path_and_name_and_type:
-   - /work/nvme/bbjs/sbharadwaj/icme_challenge/dump/fbank/eval/feats.scp
    - speech
    - kaldi_ark
-   - /work/nvme/bbjs/sbharadwaj/icme_challenge/dump/fbank/eval/target_iter3_beats_icme_inf
    - target
    - text
multi_task_dataset: false
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adadelta
optim_conf: {}
scheduler: null
scheduler_conf: {}
lightning_conf: {}
token_list:
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init: null
collate_fn_conf: {}
use_preprocessor: true
waveform_input: false
encoder: beats
encoder_conf:
    use_weighted_representation: false
    is_pretraining: true
    beats_config:
        layer_wise_gradient_decay_ratio: 1.0
        encoder_layerdrop: 0.0
        dropout: 0.1
        codebook_vocab_size: 1024
        fbank_mean: 14.803
        fbank_std: 3.079
        decoder_layers: 3
        deep_norm: true
        use_flash_attn: false
        relative_position_embedding: true
        num_buckets: 320
        max_distance: 800
        gru_rel_pos: true
        encoder_layers: 24
        encoder_embed_dim: 1024
        encoder_ffn_embed_dim: 4096
        encoder_attention_heads: 16
        decoder_embed_dim: 1024
        decoder_attention_heads: 16
model: beats
model_conf:
    ignore_id: -2
    label_smoothing: 0.1
    waveform_input: false
    mixup_probability: 0.0
required:
- output_dir
- token_list
version: '202412'
distributed: true

</details>

Citing ESPnet

@inproceedings{watanabe2018espnet,
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  title={{ESPnet}: End-to-End Speech Processing Toolkit},
  year={2018},
  booktitle={Proceedings of Interspeech},
  pages={2207--2211},
  doi={10.21437/Interspeech.2018-1456},
  url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}

or arXiv:

@misc{watanabe2018espnet,
  title={ESPnet: End-to-End Speech Processing Toolkit},
  author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
  year={2018},
  eprint={1804.00015},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}