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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: 0.39516649755557604
---
<!-- 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: 0.3280
- Wer: 0.3952
## 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: 25.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.2512 | 0.92 | 1000 | 2.9098 | 1.0000 |
| 2.163 | 1.84 | 2000 | 1.4810 | 0.9941 |
| 1.2472 | 2.75 | 3000 | 0.9604 | 0.9196 |
| 1.0166 | 3.67 | 4000 | 0.8240 | 0.8498 |
| 0.8765 | 4.59 | 5000 | 0.6873 | 0.7741 |
| 0.7712 | 5.51 | 6000 | 0.6083 | 0.7111 |
| 0.6892 | 6.43 | 7000 | 0.5546 | 0.6592 |
| 0.6314 | 7.35 | 8000 | 0.5022 | 0.6108 |
| 0.5779 | 8.26 | 9000 | 0.4850 | 0.5825 |
| 0.5245 | 9.18 | 10000 | 0.4665 | 0.5538 |
| 0.4858 | 10.1 | 11000 | 0.4282 | 0.5279 |
| 0.4616 | 11.02 | 12000 | 0.4053 | 0.5082 |
| 0.421 | 11.94 | 13000 | 0.3809 | 0.4935 |
| 0.4064 | 12.86 | 14000 | 0.3706 | 0.4781 |
| 0.3758 | 13.77 | 15000 | 0.3743 | 0.4672 |
| 0.3598 | 14.69 | 16000 | 0.3571 | 0.4521 |
| 0.3441 | 15.61 | 17000 | 0.3455 | 0.4368 |
| 0.3279 | 16.53 | 18000 | 0.3398 | 0.4386 |
| 0.3086 | 17.45 | 19000 | 0.3512 | 0.4284 |
| 0.3013 | 18.37 | 20000 | 0.3321 | 0.4233 |
| 0.2963 | 19.28 | 21000 | 0.3391 | 0.4178 |
| 0.2831 | 20.2 | 22000 | 0.3438 | 0.4114 |
| 0.2801 | 21.12 | 23000 | 0.3336 | 0.4056 |
| 0.2623 | 22.04 | 24000 | 0.3317 | 0.4012 |
| 0.263 | 22.96 | 25000 | 0.3280 | 0.4005 |
| 0.2529 | 23.88 | 26000 | 0.3268 | 0.3951 |
| 0.2492 | 24.79 | 27000 | 0.3280 | 0.3952 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.7.1
- Tokenizers 0.13.1
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