This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - SL dataset. It achieves the following results on the evaluation set:

Loss: 0.2756
Wer: 0.2279

Evaluation Commands

To evaluate on mozilla-foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 --dataset mozilla-foundation/common_voice_8_0 --config sl --split test --log_outputs

To evaluate on speech-recognition-community-v2/dev_data

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sl-with-LM-v1 --dataset speech-recognition-community-v2/dev_data --config sl --split validation --chunk_length_s 10 --stride_length_s 1

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 7.1e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 1000
num_epochs: 100.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.3881	6.1	500	2.9710	1.0
2.6401	12.2	1000	1.7677	0.9734
1.5152	18.29	1500	0.5564	0.6011
1.2191	24.39	2000	0.4319	0.4390
1.0237	30.49	2500	0.3141	0.3175
0.8892	36.59	3000	0.2748	0.2689
0.8296	42.68	3500	0.2680	0.2534
0.7602	48.78	4000	0.2820	0.2506
0.7186	54.88	4500	0.2672	0.2398
0.6887	60.98	5000	0.2729	0.2402
0.6507	67.07	5500	0.2767	0.2361
0.6226	73.17	6000	0.2817	0.2332
0.6024	79.27	6500	0.2679	0.2279
0.5787	85.37	7000	0.2837	0.2316
0.5744	91.46	7500	0.2838	0.2284
0.5556	97.56	8000	0.2763	0.2281

Framework versions

Transformers 4.17.0.dev0
Pytorch 1.10.2+cu102
Datasets 1.18.2.dev0
Tokenizers 0.11.0