This model is a fine-tuned version of facebook/data2vec-audio-base-960h on the gigaspeech dataset. It achieves the following results on the evaluation set:

Loss: 0.6689
Wer: 0.2940

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: 1
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.1835	1.0	1174	0.6329	0.3020
1.2218	2.0	2348	0.6741	0.2961
0.4211	2.9978	3519	0.6689	0.2940

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

bASILgIL/wav2vec_5e-5_3

wav2vec_5e-5_3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

bASILgIL/
wav2vec_5e-5_3