wav2vec2-large-xls-r-300m-tamil-commonvoice

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_13_0 dataset. It achieves the following results on the evaluation set:

• Loss: 7.9682
• Wer: 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: 0.0003
• 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: 500
• num_epochs: 30
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
6.0629	0.7737	400	1.5761	0.9986
0.6711	1.5474	800	0.5474	0.7253
0.437	2.3211	1200	0.4898	0.6689
0.3691	3.0948	1600	0.4760	0.6562
0.3942	3.8685	2000	0.8449	0.7908
1.3114	4.6422	2400	1.8169	0.9883
3.0292	5.4159	2800	3.3102	1.0
3.3769	6.1896	3200	3.4855	1.0
4.0469	6.9632	3600	5.2510	1.0
6.6565	7.7369	4000	7.9749	1.0
7.9329	8.5106	4400	7.9682	1.0
7.925	9.2843	4800	7.9682	1.0
7.9128	10.0580	5200	7.9682	1.0
7.9132	10.8317	5600	7.9682	1.0
7.9118	11.6054	6000	7.9682	1.0
7.8873	12.3791	6400	7.9682	1.0
7.9357	13.1528	6800	7.9682	1.0
7.9311	13.9265	7200	7.9682	1.0
7.9049	14.7002	7600	7.9682	1.0
7.9234	15.4739	8000	7.9682	1.0
7.9521	16.2476	8400	7.9682	1.0
7.8886	17.0213	8800	7.9682	1.0
7.8915	17.7950	9200	7.9682	1.0
7.9265	18.5687	9600	7.9682	1.0
7.9366	19.3424	10000	7.9682	1.0
7.8725	20.1161	10400	7.9682	1.0
7.9321	20.8897	10800	7.9682	1.0
7.9282	21.6634	11200	7.9682	1.0
7.9025	22.4371	11600	7.9682	1.0
7.8889	23.2108	12000	7.9682	1.0
7.9366	23.9845	12400	7.9682	1.0
7.9205	24.7582	12800	7.9682	1.0
7.8946	25.5319	13200	7.9682	1.0
7.9446	26.3056	13600	7.9682	1.0
7.8891	27.0793	14000	7.9682	1.0
7.9088	27.8530	14400	7.9682	1.0
7.9546	28.6267	14800	7.9682	1.0
7.8624	29.4004	15200	7.9682	1.0

Framework versions

• Transformers 4.40.1
• Pytorch 1.13.1+cu117
• Datasets 2.18.0
• Tokenizers 0.19.1