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: 0.2855
Wer: 0.2401

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-v2 --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-v2 --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: 7e-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
6.9294	6.1	500	2.9712	1.0
2.8305	12.2	1000	1.7073	0.9479
1.4795	18.29	1500	0.5756	0.6397
1.3433	24.39	2000	0.4968	0.5424
1.1766	30.49	2500	0.4185	0.4743
1.0017	36.59	3000	0.3303	0.3578
0.9358	42.68	3500	0.3003	0.3051
0.8358	48.78	4000	0.3045	0.2884
0.7647	54.88	4500	0.2866	0.2677
0.7482	60.98	5000	0.2829	0.2585
0.6943	67.07	5500	0.2782	0.2478
0.6586	73.17	6000	0.2911	0.2537
0.6425	79.27	6500	0.2817	0.2462
0.6067	85.37	7000	0.2910	0.2436
0.5974	91.46	7500	0.2875	0.2430
0.5812	97.56	8000	0.2852	0.2396

Framework versions

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