DrishtiSharma/wav2vec2-large-xls-r-300m-as-with-LM-v2

Note: Files are missing. Probably, didn't get (git)pushed properly. :(

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:

• Loss: 1.1679
• Wer: 0.5761

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: 0.000111
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 2
• 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: 300
• num_epochs: 200
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
8.3852	10.51	200	3.6402	1.0
3.5374	21.05	400	3.3894	1.0
2.8645	31.56	600	1.3143	0.8303
1.1784	42.1	800	0.9417	0.6661
0.7805	52.62	1000	0.9292	0.6237
0.5973	63.15	1200	0.9489	0.6014
0.4784	73.67	1400	0.9916	0.5962
0.4138	84.21	1600	1.0272	0.6121
0.3491	94.72	1800	1.0412	0.5984
0.3062	105.26	2000	1.0769	0.6005
0.2707	115.77	2200	1.0708	0.5752
0.2459	126.31	2400	1.1285	0.6009
0.2234	136.82	2600	1.1209	0.5949
0.2035	147.36	2800	1.1348	0.5842
0.1876	157.87	3000	1.1480	0.5872
0.1669	168.41	3200	1.1496	0.5838
0.1595	178.92	3400	1.1721	0.5778
0.1505	189.46	3600	1.1654	0.5744
0.1486	199.97	3800	1.1679	0.5761

Framework versions

• Transformers 4.16.1
• Pytorch 1.10.0+cu111
• Datasets 1.18.2
• Tokenizers 0.11.0