nb-whisper-large-v0.8
This model is a fine-tuned version of NbAiLab/nb-whisper-large-v3-RC4 on the NbAiLab/ncc_speech_styling_v2 dataset. It achieves the following results on the evaluation set:
- step: 49999
- validation_nst_loss: 0.4309
- train_loss: 0.4828
- validation_nst_wer: 2.2211
- validation_nst_cer: 0.6758
- validation_nst_exact_wer: 2.7655
- validation_nst_exact_cer: 0.7592
- validation_clean_stortinget_no_loss: 0.7845
- validation_clean_stortinget_no_wer: 8.8323
- validation_clean_stortinget_no_cer: 5.6753
- validation_clean_stortinget_no_exact_wer: 11.6973
- validation_clean_stortinget_no_exact_cer: 6.1161
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: 7e-05
- lr_scheduler_type: linear
- per_device_train_batch_size: 8
- total_train_batch_size_per_node: 32
- total_train_batch_size: 1024
- total_optimization_steps: 50,000
- starting_optimization_step: None
- finishing_optimization_step: 50,000
- num_train_dataset_workers: 32
- num_hosts: 32
- total_num_training_examples: 51,200,000
- steps_per_epoch: 7254
- num_beams: None
- weight_decay: 0.01
- adam_beta1: 0.9
- adam_beta2: 0.98
- adam_epsilon: 1e-06
- dropout: True
- bpe_dropout_probability: 0.2
- activation_dropout_probability: 0.1
Training results
step | validation_nst_loss | train_loss | validation_nst_wer | validation_nst_cer | validation_nst_exact_wer | validation_nst_exact_cer | validation_clean_stortinget_no_loss | validation_clean_stortinget_no_wer | validation_clean_stortinget_no_cer | validation_clean_stortinget_no_exact_wer | validation_clean_stortinget_no_exact_cer |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.4265 | 0.9701 | 2.1721 | 0.6246 | 2.7056 | 0.7070 | 0.6866 | 8.5836 | 5.4517 | 11.4126 | 5.8853 |
5000 | 0.4380 | 0.6065 | 2.5750 | 0.7495 | 3.0922 | 0.8251 | 0.6988 | 9.1284 | 5.8272 | 12.0840 | 6.2946 |
10000 | 0.4366 | 0.5640 | 2.3191 | 0.6852 | 2.8417 | 0.7647 | 0.7061 | 9.1378 | 5.7729 | 12.0270 | 6.2225 |
15000 | 0.4370 | 0.5506 | 2.3300 | 0.7066 | 2.9234 | 0.7976 | 0.7213 | 8.9673 | 5.6884 | 11.9511 | 6.1640 |
20000 | 0.4328 | 0.5284 | 2.3300 | 0.7019 | 2.8962 | 0.7885 | 0.7674 | 8.8915 | 5.6535 | 11.7922 | 6.1013 |
25000 | 0.4334 | 0.5133 | 2.3082 | 0.7010 | 2.9016 | 0.7903 | 0.7697 | 9.0194 | 5.7983 | 11.8468 | 6.2373 |
30000 | 0.4301 | 0.4996 | 2.1721 | 0.6674 | 2.6948 | 0.7464 | 0.7732 | 8.9223 | 5.7229 | 11.8349 | 6.1726 |
35000 | 0.4310 | 0.4957 | 2.2592 | 0.6926 | 2.8472 | 0.7830 | 0.7882 | 8.9744 | 5.7804 | 11.8871 | 6.2323 |
40000 | 0.4301 | 0.4999 | 2.1939 | 0.6647 | 2.7165 | 0.7436 | 0.7899 | 8.8868 | 5.6412 | 11.7708 | 6.0880 |
45000 | 0.4306 | 0.5049 | 2.2320 | 0.6768 | 2.7819 | 0.7628 | 0.7766 | 8.8252 | 5.6686 | 11.6902 | 6.1087 |
49999 | 0.4309 | 0.4828 | 2.2211 | 0.6758 | 2.7655 | 0.7592 | |||||
49999 | 0.7845 | 0.4828 | 8.8323 | 5.6753 | 11.6973 | 6.1161 |
Framework versions
- Transformers 4.36.2
- Datasets 2.16.0
- Tokenizers 0.15.0
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