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wav2vec2-large-xls-r-300m-bg-d2

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0

BG dataset.

It achieves the following results on the evaluation set:

Loss: 0.3421

Wer: 0.2860

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-bg-d2 --dataset mozilla-foundation/common_voice_8_0 --config bg --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-bg-d2 --dataset speech-recognition-community-v2/dev_data --config bg --split validation --chunk_length_s 10 --stride_length_s 1

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.00025

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: 700

num_epochs: 35

mixed_precision_training: Native AMP

Training results

| Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 6.8791 | 1.74 | 200 | 3.1902 | 1.0 | | 3.0441 | 3.48 | 400 | 2.8098 | 0.9864 | | 1.1499 | 5.22 | 600 | 0.4668 | 0.5014 | | 0.4968 | 6.96 | 800 | 0.4162 | 0.4472 | | 0.3553 | 8.7 | 1000 | 0.3580 | 0.3777 | | 0.3027 | 10.43 | 1200 | 0.3422 | 0.3506 | | 0.2562 | 12.17 | 1400 | 0.3556 | 0.3639 | | 0.2272 | 13.91 | 1600 | 0.3621 | 0.3583 | | 0.2125 | 15.65 | 1800 | 0.3436 | 0.3358 | | 0.1904 | 17.39 | 2000 | 0.3650 | 0.3545 | | 0.1695 | 19.13 | 2200 | 0.3366 | 0.3241 | | 0.1532 | 20.87 | 2400 | 0.3550 | 0.3311 | | 0.1453 | 22.61 | 2600 | 0.3582 | 0.3131 | | 0.1359 | 24.35 | 2800 | 0.3524 | 0.3084 | | 0.1233 | 26.09 | 3000 | 0.3503 | 0.2973 | | 0.1114 | 27.83 | 3200 | 0.3434 | 0.2946 | | 0.1051 | 29.57 | 3400 | 0.3474 | 0.2956 | | 0.0965 | 31.3 | 3600 | 0.3426 | 0.2907 | | 0.0923 | 33.04 | 3800 | 0.3478 | 0.2894 | | 0.0894 | 34.78 | 4000 | 0.3421 | 0.2860 |

Framework versions

Transformers 4.16.2

Pytorch 1.10.0+cu111

Datasets 1.18.3

Tokenizers 0.11.0