metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - whisper-event
datasets:
  - vumichien/preprocessed_jsut_jsss_css10_common_voice_11
metrics:
  - wer
  - cer
base_model: openai/whisper-large-v2
model-index:
  - name: openai/whisper-large-v2
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 ja
          type: mozilla-foundation/common_voice_11_0
          config: ja
          split: test
          args: ja
        metrics:
          - type: wer
            value: 7.6453
            name: Wer
          - type: cer
            value: 4.7187
            name: Cer
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: ja_jp
          split: test
        metrics:
          - type: wer
            value: 11.69
            name: WER
          - type: cer
            value: 7.12
            name: CER

openai/whisper-large-v2

This model is a fine-tuned version of openai/whisper-large-v2 on the vumichien/preprocessed_jsut_jsss_css10_common_voice_11 dataset. It achieves the following results on the evaluation set:

Loss: 0.2284
Wer: 7.6453
Cer: 4.7187

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: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 10000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.1912	0.55	1000	0.1828	11.2314	7.0357
0.1329	1.1	2000	0.1618	9.4172	5.9028
0.0912	1.65	3000	0.1616	8.9257	5.4711
0.0576	2.2	4000	0.1664	8.5861	5.3055
0.0449	2.74	5000	0.1642	8.4510	5.2930
0.02	3.29	6000	0.1799	8.1537	5.0354
0.019	3.84	7000	0.1801	8.125	5.0827
0.0067	4.39	8000	0.2003	7.8412	4.8133
0.006	4.94	9000	0.2071	7.5811	4.7023
0.0022	5.49	10000	0.2284	7.6453	4.7187

Framework versions

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