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---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-upper-sorbian-cz-frozen-2-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_16_1
type: common_voice_16_1
config: hsb
split: test
args: hsb
metrics:
- name: Wer
type: wer
value: 0.4301780693533271
---
<!-- 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-upper-sorbian-cz-frozen-2-colab
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7163
- Wer: 0.4302
- Cer: 0.1003
## 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: 0.0003
- 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: 500
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.6172 | 3.23 | 100 | 0.6599 | 0.6999 | 0.1787 |
| 0.4414 | 6.45 | 200 | 0.6030 | 0.6251 | 0.1524 |
| 0.289 | 9.68 | 300 | 0.5899 | 0.5670 | 0.1336 |
| 0.1953 | 12.9 | 400 | 0.6095 | 0.5457 | 0.1308 |
| 0.1388 | 16.13 | 500 | 0.6628 | 0.5159 | 0.1224 |
| 0.1187 | 19.35 | 600 | 0.7075 | 0.4932 | 0.1180 |
| 0.0994 | 22.58 | 700 | 0.7131 | 0.4780 | 0.1143 |
| 0.0816 | 25.81 | 800 | 0.6959 | 0.4752 | 0.1101 |
| 0.0727 | 29.03 | 900 | 0.7201 | 0.4644 | 0.1104 |
| 0.0637 | 32.26 | 1000 | 0.7288 | 0.4630 | 0.1080 |
| 0.0592 | 35.48 | 1100 | 0.7219 | 0.4524 | 0.1056 |
| 0.0549 | 38.71 | 1200 | 0.7204 | 0.4480 | 0.1041 |
| 0.0473 | 41.94 | 1300 | 0.7238 | 0.4470 | 0.1048 |
| 0.0412 | 45.16 | 1400 | 0.7109 | 0.4278 | 0.1011 |
| 0.0423 | 48.39 | 1500 | 0.7252 | 0.4407 | 0.1045 |
| 0.0419 | 51.61 | 1600 | 0.7193 | 0.4393 | 0.1028 |
| 0.0365 | 54.84 | 1700 | 0.7231 | 0.4318 | 0.1010 |
| 0.0347 | 58.06 | 1800 | 0.7163 | 0.4302 | 0.1003 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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