metadata

language: mn
license: apache-2.0
tags:
  - whisper-event
  - hf-asr-leaderboard
  - generated_from_multiple_datasets
datasets:
  - mozilla-foundation/common_voice_11_0
  - google/fleurs
  - bayartsogt/ulaanbal-v0
  - bayartsogt/youtube-mongolian-v1
metrics:
  - wer
  - cer
model-index:
  - name: whisper-large-v2-mn-13
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Common Voice 11.0
          type: mozilla-foundation/common_voice_11_0
          config: mn
          split: test
        metrics:
          - type: wer
            value: 20.02403320952589
            name: Wer
          - type: cer
            value: 6.601024224251205
            name: Cer

whisper-large-v2-mn-13

This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.1689
Wer: 20.0240
Cer: 6.6010

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 1e-05
train_batch_size: 8
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 25000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
0.3921	0.09	1000	15.7845	0.4101	46.9030
0.3115	0.17	2000	14.2911	0.3353	41.8451
0.2659	0.26	3000	11.8131	0.2800	34.6406
0.2477	0.35	4000	10.6659	0.2578	32.0024
0.2274	0.43	5000	10.0460	0.2463	30.3419
0.2059	0.52	6000	9.9264	0.2305	28.5558
0.2092	0.61	7000	9.4277	0.2196	27.8785
0.1956	0.69	8000	9.2745	0.2093	26.8353
0.195	0.78	9000	8.9485	0.2042	26.6168
0.195	0.87	10000	8.5324	0.2001	25.6718
0.1795	0.95	11000	8.1786	0.1936	24.1698
0.1575	1.04	12000	7.8653	0.1915	23.8912
0.1358	1.13	13000	7.6749	0.1918	23.3778
0.1509	1.21	14000	7.7221	0.1852	23.1811
0.1474	1.3	15000	7.3246	0.1764	22.4984
0.1461	1.39	16000	7.3187	0.1793	22.4110
0.134	1.47	17000	7.1123	0.1737	21.9412
0.1289	1.56	18000	7.4593	0.1727	22.0614
0.1287	1.65	19000	7.0230	0.1701	21.4223
0.1196	1.73	20000	6.9447	0.1666	21.2475
0.1275	1.82	21000	6.7956	0.1653	20.8106
0.1329	1.91	22000	6.7729	0.1622	20.3354
0.1294	1.99	23000	6.6448	0.1606	20.2207
0.1043	2.08	24000	6.6010	0.1689	20.0240
0.079	2.17	25000	6.6246	0.1687	20.1005

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.8.1.dev0
Tokenizers 0.13.2