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---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: adapter_freezed_base_const_lr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: hy-AM
split: test
args: hy-AM
metrics:
- name: Wer
type: wer
value: 0.9281584969288209
---
<!-- 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. -->
# adapter_freezed_base_const_lr
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9200
- Wer: 0.9282
- Cer: 0.2562
## 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: 0.0001
- 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: constant
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 1.3224 | 0.6154 | 200 | 1.3171 | 0.9949 | 0.3890 |
| 1.02 | 1.2308 | 400 | 1.0780 | 0.9728 | 0.3233 |
| 0.9256 | 1.8462 | 600 | 0.9799 | 0.9738 | 0.2955 |
| 0.8377 | 2.4615 | 800 | 0.9756 | 0.9663 | 0.2919 |
| 0.7836 | 3.0769 | 1000 | 0.9143 | 0.9535 | 0.2730 |
| 0.7516 | 3.6923 | 1200 | 0.8908 | 0.9373 | 0.2671 |
| 0.6714 | 4.3077 | 1400 | 0.9088 | 0.9497 | 0.2692 |
| 0.6749 | 4.9231 | 1600 | 0.9006 | 0.9566 | 0.2681 |
| 0.6223 | 5.5385 | 1800 | 0.8686 | 0.9322 | 0.2587 |
| 0.5643 | 6.1538 | 2000 | 0.8846 | 0.9422 | 0.2580 |
| 0.5773 | 6.7692 | 2200 | 0.8960 | 0.9396 | 0.2644 |
| 0.5067 | 7.3846 | 2400 | 0.8778 | 0.9273 | 0.2545 |
| 0.5123 | 8.0 | 2600 | 0.8919 | 0.9379 | 0.2601 |
| 0.4729 | 8.6154 | 2800 | 0.9131 | 0.9597 | 0.2587 |
| 0.406 | 9.2308 | 3000 | 0.9032 | 0.9389 | 0.2564 |
| 0.4286 | 9.8462 | 3200 | 0.9200 | 0.9282 | 0.2562 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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