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---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-test_arm-colab-CV16.0
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_16_0
type: common_voice_16_0
config: hy-AM
split: test
args: hy-AM
metrics:
- name: Wer
type: wer
value: 0.1774802773129333
---
<!-- 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. -->
# w2v-bert-2.0-test_arm-colab-CV16.0
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_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2066
- Wer: 0.1775
## 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: 5e-05
- 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1471 | 1.6 | 300 | 0.2062 | 0.2229 |
| 0.1437 | 3.2 | 600 | 0.2216 | 0.2375 |
| 0.1051 | 4.8 | 900 | 0.1969 | 0.2127 |
| 0.0594 | 6.4 | 1200 | 0.1882 | 0.1839 |
| 0.0297 | 8.0 | 1500 | 0.1951 | 0.1825 |
| 0.0115 | 9.6 | 1800 | 0.2066 | 0.1775 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1
- Datasets 2.16.1
- Tokenizers 0.15.1
|