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---
tags:
- generated_from_trainer
datasets:
- evanarlian/common_voice_11_0_id_filtered
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-113m-id
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: evanarlian/common_voice_11_0_id_filtered
type: evanarlian/common_voice_11_0_id_filtered
metrics:
- name: Wer
type: wer
value: 0.4274974633336408
---
<!-- 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-xls-r-113m-id
This model is a fine-tuned version of [evanarlian/distil-wav2vec2-xls-r-113m-id](https://huggingface.co/evanarlian/distil-wav2vec2-xls-r-113m-id) on the evanarlian/common_voice_11_0_id_filtered dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4804
- Wer: 0.4275
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.2694 | 0.92 | 1000 | 2.9168 | 1.0000 |
| 2.2449 | 1.84 | 2000 | 1.5711 | 0.9901 |
| 1.2118 | 2.75 | 3000 | 1.0133 | 0.9261 |
| 0.971 | 3.67 | 4000 | 0.8860 | 0.8743 |
| 0.8472 | 4.59 | 5000 | 0.7562 | 0.8180 |
| 0.7436 | 5.51 | 6000 | 0.6800 | 0.7505 |
| 0.6603 | 6.43 | 7000 | 0.6275 | 0.7023 |
| 0.5961 | 7.35 | 8000 | 0.5913 | 0.6589 |
| 0.5458 | 8.26 | 9000 | 0.5605 | 0.6358 |
| 0.5113 | 9.18 | 10000 | 0.5346 | 0.6039 |
| 0.463 | 10.1 | 11000 | 0.5052 | 0.5689 |
| 0.4326 | 11.02 | 12000 | 0.4880 | 0.5497 |
| 0.3981 | 11.94 | 13000 | 0.4778 | 0.5357 |
| 0.3602 | 12.86 | 14000 | 0.4656 | 0.5198 |
| 0.3501 | 13.77 | 15000 | 0.4510 | 0.5085 |
| 0.3199 | 14.69 | 16000 | 0.4617 | 0.5010 |
| 0.3058 | 15.61 | 17000 | 0.4385 | 0.4880 |
| 0.2844 | 16.53 | 18000 | 0.4638 | 0.4930 |
| 0.2729 | 17.45 | 19000 | 0.4594 | 0.4783 |
| 0.2648 | 18.37 | 20000 | 0.4521 | 0.4703 |
| 0.2515 | 19.28 | 21000 | 0.4727 | 0.4627 |
| 0.2428 | 20.2 | 22000 | 0.4566 | 0.4587 |
| 0.2343 | 21.12 | 23000 | 0.4554 | 0.4545 |
| 0.2228 | 22.04 | 24000 | 0.4670 | 0.4506 |
| 0.2135 | 22.96 | 25000 | 0.4458 | 0.4446 |
| 0.2067 | 23.88 | 26000 | 0.4571 | 0.4402 |
| 0.2065 | 24.79 | 27000 | 0.4680 | 0.4359 |
| 0.1968 | 25.71 | 28000 | 0.4702 | 0.4346 |
| 0.1914 | 26.63 | 29000 | 0.4687 | 0.4320 |
| 0.182 | 27.55 | 30000 | 0.4807 | 0.4332 |
| 0.1771 | 28.47 | 31000 | 0.4824 | 0.4308 |
| 0.1728 | 29.38 | 32000 | 0.4804 | 0.4275 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.1
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