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HouraMor/
wav2vec2-ft-lre6

Name: wav2vec2-ft-lre6
Availability: InStock

automatic-speech-recognition

generated_from_trainer

jonatasgrosman/wav2vec2-large-english

finetune

apache-2.0

endpoints_compatible

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wav2vec2-ft-lre6

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

Loss: 0.8832
Wer: 1.0
Cer: 1.0

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: 1e-06
train_batch_size: 16
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use 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: 500
training_steps: 10000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
1.006	0.4165	1000	0.9763	1.0	1.0
0.9162	0.8330	2000	0.9393	1.0	1.0
0.7996	1.2495	3000	0.9507	1.0	1.0
0.8934	1.6660	4000	0.8868	1.0	1.0
0.8691	2.0825	5000	0.8926	1.0	1.0
0.8369	2.4990	6000	0.8859	1.0	1.0
0.8373	2.9155	7000	0.8960	1.0	1.0
0.8742	3.3319	8000	0.8814	1.0	1.0
0.801	3.7484	9000	0.8832	1.0	1.0
0.7813	4.1649	10000	0.8832	1.0	1.0

Framework versions

Transformers 4.52.3
Pytorch 2.5.1
Datasets 3.6.0
Tokenizers 0.21.1

HouraMor/wav2vec2-ft-lre6

wav2vec2-ft-lre6

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

HouraMor/
wav2vec2-ft-lre6