wav2vec2-large-xls-r-300m-hi-wx1

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 -HI dataset. It achieves the following results on the evaluation set:

• Loss: 0.6552
• Wer: 0.3200

Evaluation Commands

1. To evaluate on mozilla-foundation/common_voice_8_0 with test split

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-hi-wx1 --dataset mozilla-foundation/common_voice_7_0 --config hi --split test --log_outputs

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

NA

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.00024
• 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: 1800
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
12.2663	1.36	200	5.9245	1.0
4.1856	2.72	400	3.4968	1.0
3.3908	4.08	600	2.9970	1.0
1.5444	5.44	800	0.9071	0.6139
0.7237	6.8	1000	0.6508	0.4862
0.5323	8.16	1200	0.6217	0.4647
0.4426	9.52	1400	0.5785	0.4288
0.3933	10.88	1600	0.5935	0.4217
0.3532	12.24	1800	0.6358	0.4465
0.3319	13.6	2000	0.5789	0.4118
0.2877	14.96	2200	0.6163	0.4056
0.2663	16.33	2400	0.6176	0.3893
0.2511	17.68	2600	0.6065	0.3999
0.2275	19.05	2800	0.6183	0.3842
0.2098	20.41	3000	0.6486	0.3864
0.1943	21.77	3200	0.6365	0.3885
0.1877	23.13	3400	0.6013	0.3677
0.1679	24.49	3600	0.6451	0.3795
0.1667	25.85	3800	0.6410	0.3635
0.1514	27.21	4000	0.6000	0.3577
0.1453	28.57	4200	0.6020	0.3518
0.134	29.93	4400	0.6531	0.3517
0.1354	31.29	4600	0.6874	0.3578
0.1224	32.65	4800	0.6519	0.3492
0.1199	34.01	5000	0.6553	0.3490
0.1077	35.37	5200	0.6621	0.3429
0.0997	36.73	5400	0.6641	0.3413
0.0964	38.09	5600	0.6722	0.3385
0.0931	39.45	5800	0.6365	0.3363
0.0944	40.81	6000	0.6454	0.3326
0.0862	42.18	6200	0.6497	0.3256
0.0848	43.54	6400	0.6599	0.3226
0.0793	44.89	6600	0.6625	0.3232
0.076	46.26	6800	0.6463	0.3186
0.0749	47.62	7000	0.6559	0.3225
0.0663	48.98	7200	0.6552	0.3200

Framework versions

• Transformers 4.16.2
• Pytorch 1.10.0+cu111
• Datasets 1.18.3
• Tokenizers 0.11.0