metadata

language:
  - be
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
base_model: openai/whisper-small
model-index:
  - name: Whisper Small Belarusian
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 be
          type: mozilla-foundation/common_voice_11_0
          config: be
          split: validation
          args: be
        metrics:
          - type: wer
            value: 6.3671568743912
            name: WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 be
          type: mozilla-foundation/common_voice_11_0
          config: be
          split: test
          args: be
        metrics:
          - type: wer
            value: 6.79
            name: WER
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: be_by
          split: test
        metrics:
          - type: wer
            value: 43.615168811067036
            name: 'WER (reference column: transcription)'
          - type: wer
            value: 45.89674723962996
            name: 'WER (reference column: raw_transcription)'

Whisper Small Belarusian

This model is a fine-tuned version of openai/whisper-small on the mozilla-foundation/common_voice_11_0 be dataset. It achieves the following results on the evaluation set:

Loss on validation: 0.0706
WER on validation set: 6.3672
WER on test set: 6.79

Source code

All the source coude is located both in:

GitHub repository
and under src folder

Code in these 2 places should be the same. GitHub is used to make development and training of multiple models (small, base, etc.) easier.

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: 0.0001
train_batch_size: 64
eval_batch_size: 64
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: 12000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.1907	0.08	1000	0.2546	25.4639
0.1482	0.17	2000	0.1641	17.1676
0.1175	0.25	3000	0.1454	15.5940
0.0958	0.33	4000	0.1261	13.2625
0.099	0.42	5000	0.1012	10.6143
0.028	1.05	6000	0.1053	9.8794
0.0473	1.13	7000	0.1029	10.3078
0.0391	1.21	8000	0.0924	9.2419
0.0423	1.3	9000	0.0797	7.9249
0.0604	1.38	10000	0.0688	7.0150
0.0121	2.01	11000	0.0696	6.4638
0.0155	2.1	12000	0.0706	6.3672

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2