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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hi-wx1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hi-wx1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6552
- Wer: 0.3200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### 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
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