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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
- 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
- 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
