This model is a fine-tuned version of facebook/w2v-bert-2.0 on the LIBRISPEECH_ASR - CLEAN dataset. It achieves the following results on the evaluation set:

Loss: 0.1035
Wer: 0.0708
Cer: 0.0194

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: 3e-05
train_batch_size: 12
eval_batch_size: 12
seed: 42
distributed_type: multi-GPU
num_devices: 3
gradient_accumulation_steps: 2
total_train_batch_size: 72
total_eval_batch_size: 36
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 10000
num_epochs: 7.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
2.8812	0.63	250	1.0000	2.8923	1.0
1.2899	1.26	500	0.2563	1.1471	0.7030
0.5276	1.89	750	0.1127	0.4687	0.4114
0.3313	2.52	1000	0.0659	0.2870	0.2577
0.2089	3.16	1250	0.0445	0.2079	0.1766
0.1634	3.79	1500	0.0366	0.1687	0.1411
0.1546	4.42	1750	0.1452	0.1138	0.0294
0.1245	5.05	2000	0.1316	0.0973	0.0260
0.1341	5.68	2250	0.1196	0.0867	0.0234
0.0942	6.31	2500	0.1128	0.0794	0.0213
0.0848	6.94	2750	0.1077	0.0717	0.0197

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

ylacombe/wav2vec2-bert-CV16-en-libri

wav2vec2-bert-CV16-en-libri

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ylacombe/
wav2vec2-bert-CV16-en-libri