metadata

language:
  - vi
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: Whisper small vi - Ox
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: vi
          split: test
          args: vi
        metrics:
          - name: Wer
            type: wer
            value: 31.26665341022072

Whisper small vi - Ox

This model is a fine-tuned version of openai/whisper-small on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

Loss: 1.0138
Wer: 31.2667

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: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 3.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.2276	0.08	1000	0.7506	29.8509
0.1768	0.16	2000	0.8114	31.2189
0.1828	0.24	3000	0.8569	31.2985
0.1632	0.32	4000	0.8523	31.9268
0.1566	0.4	5000	0.9062	31.9149
0.1532	0.48	6000	0.8914	31.4496
0.1593	0.56	7000	0.9342	31.9825
0.1411	0.64	8000	0.9412	32.0302
0.1531	0.72	9000	0.9456	31.6206
0.1246	0.8	10000	0.9452	31.7240
0.1336	0.88	11000	0.9622	31.1195
0.1392	0.96	12000	0.9638	31.3939
0.0725	1.04	13000	1.0032	31.5649
0.0838	1.12	14000	1.0346	31.7916
0.0766	1.2	15000	1.0138	31.2667

Framework versions

Transformers 4.39.3
Pytorch 2.4.1
Datasets 3.0.1
Tokenizers 0.15.2