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ntsema/
wav2vec2-xlsr-53-espeak-cv-ft-tat-ntsema-colab

Name: wav2vec2-xlsr-53-espeak-cv-ft-tat-ntsema-colab
Availability: InStock

automatic-speech-recognition

generated_from_trainer

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wav2vec2-xlsr-53-espeak-cv-ft-tat-ntsema-colab

This model is a fine-tuned version of facebook/wav2vec2-xlsr-53-espeak-cv-ft on the audiofolder dataset. It achieves the following results on the evaluation set:

Loss: 0.2976
Wer: 0.2834

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: 0.0003
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
num_epochs: 30
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.5013	3.57	400	0.4017	0.4837
0.3368	7.14	800	0.2774	0.3693
0.1942	10.71	1200	0.3054	0.3386
0.1449	14.28	1600	0.3085	0.3246
0.1147	17.85	2000	0.3134	0.3037
0.0944	21.43	2400	0.3046	0.2933
0.0778	24.99	2800	0.3057	0.2927
0.0643	28.57	3200	0.2976	0.2834

Framework versions

Transformers 4.24.0
Pytorch 1.14.0.dev20221107+cu116
Datasets 2.6.1
Tokenizers 0.13.2

ntsema/wav2vec2-xlsr-53-espeak-cv-ft-tat-ntsema-colab

wav2vec2-xlsr-53-espeak-cv-ft-tat-ntsema-colab

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ntsema/
wav2vec2-xlsr-53-espeak-cv-ft-tat-ntsema-colab