metadata

license: cc-by-sa-4.0
tags:
  - generated_from_trainer
datasets:
  - common_voice
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xlsr-53-thai
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice
          type: common_voice
          config: th
          split: validation
          args: th
        metrics:
          - name: Wer
            type: wer
            value: 0.7430683918669131

wav2vec2-large-xlsr-53-thai

This model is a fine-tuned version of airesearch/wav2vec2-large-xlsr-53-th on the common_voice dataset. It achieves the following results on the evaluation set:

Loss: 3.3576
Wer: 0.7431

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: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 100
num_epochs: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.7312	3.33	100	3.3592	1.0
3.3687	6.67	200	3.2175	1.0
2.4527	10.0	300	2.2648	0.7911
1.0505	13.33	400	2.2322	0.7659
0.7725	16.67	500	2.2775	0.7505
0.6289	20.0	600	2.3209	0.7498
0.543	23.33	700	2.4494	0.7572
0.4991	26.67	800	2.5798	0.7597
0.4492	30.0	900	2.5685	0.7461
0.3737	33.33	1000	2.6186	0.7486
0.3358	36.67	1100	2.7781	0.7480
0.3247	40.0	1200	2.8999	0.7535
0.2963	43.33	1300	2.8668	0.7388
0.2825	46.67	1400	2.8983	0.7449
0.2651	50.0	1500	2.9699	0.7461
0.2597	53.33	1600	2.9930	0.7314
0.2629	56.67	1700	2.9852	0.7406
0.2406	60.0	1800	3.0552	0.7474
0.2293	63.33	1900	3.1058	0.7344
0.2193	66.67	2000	3.1594	0.7406
0.2174	70.0	2100	3.2351	0.7369
0.2127	73.33	2200	3.2696	0.7388
0.2061	76.67	2300	3.2954	0.7566
0.1947	80.0	2400	3.2878	0.7529
0.199	83.33	2500	3.3233	0.7486
0.1961	86.67	2600	3.3136	0.7437
0.1928	90.0	2700	3.3240	0.7406
0.1875	93.33	2800	3.3479	0.7425
0.1852	96.67	2900	3.3681	0.7425
0.1814	100.0	3000	3.3576	0.7431

Framework versions

Transformers 4.28.0
Pytorch 2.0.1+cu118
Datasets 1.16.1
Tokenizers 0.13.3