metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice_11_0
metrics:
  - wer
model-index:
  - name: whisper-tiny-be-test
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_11_0
          type: common_voice_11_0
          config: be
          split: validation
          args: be
        metrics:
          - name: Wer
            type: wer
            value: 46.7032967032967

whisper-tiny-be-test

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

Loss: 0.4282
Wer: 46.7033

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10
training_steps: 300
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
2.5366	0.05	10	1.5402	94.5055
1.3721	0.1	20	1.0021	75.8242
0.9921	0.15	30	0.8322	75.0916
0.9844	0.2	40	0.8080	72.8938
0.7071	0.25	50	0.7862	77.2894
0.7998	0.3	60	0.7052	68.8645
0.6935	0.35	70	0.6781	64.2857
0.81	0.4	80	0.6341	63.5531
0.6133	0.45	90	0.6083	62.6374
0.6675	0.5	100	0.5851	62.8205
0.5577	0.55	110	0.5651	59.3407
0.6473	0.6	120	0.5638	58.0586
0.6018	0.65	130	0.5434	53.8462
0.5918	0.7	140	0.5385	54.9451
0.5654	0.75	150	0.5200	58.0586
0.587	0.8	160	0.4974	57.1429
0.6157	0.85	170	0.4834	53.2967
0.6803	0.9	180	0.4852	55.8608
0.4813	0.95	190	0.4686	51.2821
0.4952	1.0	200	0.4624	51.4652
0.3956	0.03	210	0.4690	52.0147
0.3719	0.07	220	0.4673	52.7473
0.3168	0.1	230	0.4499	51.4652
0.3582	0.13	240	0.4525	46.8864
0.2475	0.17	250	0.4612	52.3810
0.2988	0.2	260	0.4346	49.8168
0.2749	0.23	270	0.4249	48.9011
0.3368	0.27	280	0.4388	46.5201
0.2574	0.3	290	0.4309	46.7033
0.2921	0.33	300	0.4282	46.7033

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu116
Datasets 2.7.1.dev0
Tokenizers 0.13.2