wav2vec2-2-rnd-regularisation – AI Model by sanchit-gandhi | AlphaNeural AI

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sanchit-gandhi/
wav2vec2-2-rnd-regularisation

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speech-encoder-decoder

automatic-speech-recognition

generated_from_trainer

librispeech_asr

endpoints_compatible

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This model was trained from scratch on the librispeech_asr dataset. It achieves the following results on the evaluation set:

Loss: 0.6977
Wer: 0.1231

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
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: 1000
num_epochs: 25.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.1467	1.68	1500	6.0558	1.3243
5.4388	3.36	3000	5.4711	1.5604
3.3434	5.04	4500	3.4808	0.7461
1.5259	6.73	6000	2.1931	0.3430
1.4285	8.41	7500	1.5883	0.2784
1.0687	10.09	9000	1.2481	0.2069
0.6425	11.77	10500	1.0507	0.1758
0.7147	13.45	12000	0.9397	0.1584
0.5083	15.13	13500	0.8452	0.1453
0.4287	16.82	15000	0.7915	0.1388
0.3499	18.5	16500	0.7477	0.1315
0.3733	20.18	18000	0.7307	0.1287
0.2609	21.86	19500	0.7061	0.1263
0.2602	23.54	21000	0.6977	0.1231

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu113
Datasets 1.18.3
Tokenizers 0.11.0