README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-xls-r-300m-th-v7_0
    results: []

wav2vec2-xls-r-300m-th-v7_0

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

• Loss: 3.4099
• Wer: 0.9988
• Cer: 0.7861
• Clean Cer: 0.7617
• Learning Rate: 0.0000

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer	Clean Cer	Rate
8.5484	0.4	500	3.6234	1.0	1.0	1.0	0.0000
3.2275	0.8	1000	2.2960	0.9998	0.7081	0.6540	0.0000
0.9955	1.2	1500	1.2224	0.9549	0.4327	0.3756	0.0000
0.66	1.61	2000	0.9559	0.9232	0.3651	0.3040	0.0000
0.546	2.01	2500	0.9207	0.9481	0.3585	0.2826	0.0000
0.4459	2.41	3000	0.7701	0.8693	0.2940	0.2383	0.0000
0.4041	2.81	3500	0.7756	0.8224	0.2949	0.2634	0.0000
0.3637	3.21	4000	0.6015	0.7015	0.2064	0.1807	0.0000
0.334	3.61	4500	0.5615	0.6675	0.1907	0.1638	0.0000
0.3283	4.02	5000	0.6205	0.7073	0.2092	0.1803	0.0000
0.3762	4.42	5500	0.7517	0.6366	0.1778	0.1600	0.0000
0.4954	4.82	6000	0.9374	0.7073	0.2023	0.1735	0.0000
0.5568	5.22	6500	0.8859	0.7027	0.1982	0.1666	0.0000
0.6756	5.62	7000	1.0252	0.6802	0.1920	0.1628	0.0000
0.7752	6.02	7500	1.1259	0.7657	0.2309	0.1908	0.0000
0.8305	6.43	8000	1.3857	0.9029	0.3252	0.2668	0.0000
1.7385	6.83	8500	3.2320	0.9998	0.9234	0.9114	0.0000
2.7839	7.23	9000	3.3238	0.9999	0.9400	0.9306	0.0000
2.8307	7.63	9500	3.2678	0.9998	0.9167	0.9053	0.0000
2.7672	8.03	10000	3.2435	0.9995	0.8992	0.8867	0.0000
2.7426	8.43	10500	3.2396	0.9995	0.8720	0.8561	0.0000
2.7608	8.84	11000	3.2689	0.9993	0.8399	0.8202	0.0000
2.8195	9.24	11500	3.3283	0.9989	0.8084	0.7865	0.0000
2.9044	9.64	12000	3.4099	0.9988	0.7861	0.7617	0.0000

Framework versions

• Transformers 4.27.0.dev0
• Pytorch 1.13.1+cu116
• Datasets 2.9.0
• Tokenizers 0.13.2