wav2vec2-base-myst-new

This model is a fine-tuned version of facebook/wav2vec2-base on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.4262
• Wer: 0.1249
• Cer: 0.0583

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: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.06
• num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.5255	1.1615	2000	0.4068	0.2182	0.0884
0.358	2.3229	4000	0.3476	0.1657	0.0713
0.3055	3.4844	6000	0.3039	0.1564	0.0663
0.2571	4.6459	8000	0.2945	0.1472	0.0641
0.2373	5.8073	10000	0.3080	0.1457	0.0635
0.2277	6.9688	12000	0.3035	0.1370	0.0619
0.184	8.1301	14000	0.3264	0.1336	0.0603
0.155	9.2916	16000	0.3322	0.1348	0.0614
0.1459	10.4530	18000	0.3464	0.1340	0.0617
0.1396	11.6145	20000	0.3306	0.1330	0.0610
0.1288	12.7760	22000	0.3563	0.1294	0.0595
0.1123	13.9374	24000	0.3605	0.1294	0.0598
0.1061	15.0987	26000	0.3896	0.1287	0.0595
0.0938	16.2602	28000	0.3904	0.1274	0.0591
0.0851	17.4217	30000	0.4189	0.1252	0.0585
0.0858	18.5831	32000	0.4145	0.1260	0.0587
0.076	19.7446	34000	0.4262	0.1249	0.0583

Framework versions

• Transformers 4.57.0
• Pytorch 2.8.0+cu128
• Datasets 4.1.1
• Tokenizers 0.22.1