wav2vec2-large-xls-r-300m-sr-v4

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

• Loss: 0.5570
• Wer: 0.3038

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-sr-v4 --dataset mozilla-foundation/common_voice_8_0 --config sr --split test --log_outputs

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

python eval.py --model_id DrishtiSharma/wav2vec2-large-xls-r-300m-sr-v4 --dataset speech-recognition-community-v2/dev_data --config sr --split validation --chunk_length_s 10 --stride_length_s 1

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: 800
• num_epochs: 200
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
8.2934	7.5	300	2.9777	0.9995
1.5049	15.0	600	0.5036	0.4806
0.3263	22.5	900	0.5822	0.4055
0.2008	30.0	1200	0.5609	0.4032
0.1543	37.5	1500	0.5203	0.3710
0.1158	45.0	1800	0.6458	0.3985
0.0997	52.5	2100	0.6227	0.4013
0.0834	60.0	2400	0.6048	0.3836
0.0665	67.5	2700	0.6197	0.3686
0.0602	75.0	3000	0.5418	0.3453
0.0524	82.5	3300	0.5310	0.3486
0.0445	90.0	3600	0.5599	0.3374
0.0406	97.5	3900	0.5958	0.3327
0.0358	105.0	4200	0.6017	0.3262
0.0302	112.5	4500	0.5613	0.3248
0.0285	120.0	4800	0.5659	0.3462
0.0213	127.5	5100	0.5568	0.3206
0.0215	135.0	5400	0.6524	0.3472
0.0162	142.5	5700	0.6223	0.3458
0.0137	150.0	6000	0.6625	0.3313
0.0114	157.5	6300	0.5739	0.3336
0.0101	165.0	6600	0.5906	0.3285
0.008	172.5	6900	0.5982	0.3112
0.0076	180.0	7200	0.5399	0.3094
0.0071	187.5	7500	0.5387	0.2991
0.0057	195.0	7800	0.5570	0.3038

Framework versions

• Transformers 4.16.2
• Pytorch 1.10.0+cu111
• Datasets 1.18.2
• Tokenizers 0.11.0