gttsehu/wav2vec2-xls-r-300m-bp1-es_eu

facebook/wav2vec2-xls-r-300m

This work was partially funded by the Spanish Ministry of Science and Innovation (OPENSPEECH project, PID2019-106424RB-I00).

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GTTSEHU/BASQUE_PARLIAMENT_1 - NA dataset. It achieves the following results on the evaluation set:

• Loss: 0.0846
• Wer: 0.0367
• Cer: 0.0132

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: 4
• eval_batch_size: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• total_eval_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 6.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.7054	0.19	4000	0.1011	0.0871	0.0227
0.0856	0.39	8000	0.0995	0.0747	0.0207
0.075	0.58	12000	0.0868	0.0647	0.0185
0.0694	0.77	16000	0.0853	0.0619	0.0183
0.0658	0.97	20000	0.0778	0.0573	0.0171
0.0589	1.16	24000	0.0821	0.0546	0.0166
0.0572	1.35	28000	0.0827	0.0558	0.0170
0.0551	1.55	32000	0.0830	0.0533	0.0169
0.054	1.74	36000	0.0788	0.0512	0.0162
0.0524	1.93	40000	0.0783	0.0489	0.0156
0.048	2.13	44000	0.0861	0.0492	0.0160
0.046	2.32	48000	0.0763	0.0494	0.0154
0.0456	2.51	52000	0.0835	0.0471	0.0153
0.0439	2.71	56000	0.0790	0.0469	0.0152
0.0436	2.9	60000	0.0832	0.0472	0.0155
0.0406	3.09	64000	0.0810	0.0442	0.0148
0.0386	3.29	68000	0.0810	0.0436	0.0146
0.038	3.48	72000	0.0778	0.0430	0.0143
0.0373	3.67	76000	0.0785	0.0430	0.0144
0.0363	3.87	80000	0.0788	0.0421	0.0144
0.0348	4.06	84000	0.0823	0.0423	0.0144
0.0323	4.25	88000	0.0819	0.0407	0.0143
0.0319	4.45	92000	0.0809	0.0410	0.0142
0.0314	4.64	96000	0.0821	0.0400	0.0138
0.0306	4.83	100000	0.0813	0.0389	0.0137
0.0295	5.03	104000	0.0820	0.0377	0.0131
0.0275	5.22	108000	0.0866	0.0378	0.0137
0.0267	5.41	112000	0.0831	0.0376	0.0134
0.0264	5.61	116000	0.0845	0.0369	0.0132
0.0258	5.8	120000	0.0859	0.0370	0.0133
0.0254	6.0	124000	0.0846	0.0367	0.0132

Framework versions

• Transformers 4.36.0.dev0
• Pytorch 2.1.1+cu121
• Datasets 2.15.1.dev0
• Tokenizers 0.15.0