wav2vec2-2-rnd-2-layer-no-adapter – AI Model by sanchit-gandhi | AlphaNeural AI

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sanchit-gandhi/
wav2vec2-2-rnd-2-layer-no-adapter

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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: 1.8365
Wer: 0.2812

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: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
5.8017	1.68	1500	5.7161	1.3220
4.5907	3.36	3000	4.7936	0.9799
3.151	5.04	4500	4.1610	0.7752
1.5166	6.73	6000	3.5939	0.5343
2.4523	8.41	7500	4.0013	0.6954
1.423	10.09	9000	2.6917	0.4476
0.7882	11.77	10500	2.4493	0.3967
1.1643	13.45	12000	2.0629	0.3234
0.5352	15.13	13500	2.0625	0.3363
0.407	16.82	15000	1.8378	0.2812
0.1162	18.5	16500	1.8365	0.2812

Framework versions

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