---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: CiceroASR
  results: []
---

<!-- 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. -->

# CiceroASR

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) 
for the transcription of Classical Latin!

Example from the Aeneid:
<video controls src="https://cdn-uploads.huggingface.co/production/uploads/5fc7944e8a82cc0bcf7cc51d/hYNFr2od1EKDlRRdzJmzR.webm"></video>
Transcription:
**arma virumque cano** (Of arms and men I sing)

Example from Genesis:
<video controls src="https://cdn-uploads.huggingface.co/production/uploads/5fc7944e8a82cc0bcf7cc51d/9Q6DfG2h8FkABnl55DLBH.webm"></video>
Transcription (little error there):
**creavit deus chaelum et terram** (In the beggining God created the heaven and the earth)


It achieves the following results on the evaluation set of my dataset [Latin Youtube](https://huggingface.co/datasets/thiagolira/LatinYoutube):
- Loss: 0.5395
- Wer: 0.2220

## 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: linear
- lr_scheduler_warmup_steps: 300
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.6548        | 0.94  | 50   | 2.8634          | 0.9990 |
| 2.2055        | 1.89  | 100  | 1.0921          | 0.9727 |
| 1.667         | 2.83  | 150  | 0.7201          | 0.4615 |
| 1.3148        | 3.77  | 200  | 0.6431          | 0.3866 |
| 0.9899        | 4.72  | 250  | 0.5561          | 0.3116 |
| 0.9629        | 5.66  | 300  | 0.6027          | 0.3817 |
| 0.7557        | 6.6   | 350  | 0.7145          | 0.3145 |
| 0.9143        | 7.55  | 400  | 0.4926          | 0.2610 |
| 0.5837        | 8.49  | 450  | 0.5396          | 0.2619 |
| 0.7037        | 9.43  | 500  | 0.5076          | 0.2746 |
| 0.5986        | 10.38 | 550  | 0.5224          | 0.2415 |
| 0.5288        | 11.32 | 600  | 0.5332          | 0.2259 |
| 0.5034        | 12.26 | 650  | 0.5436          | 0.2249 |
| 0.4897        | 13.21 | 700  | 0.5171          | 0.2162 |
| 0.4738        | 14.15 | 750  | 0.5395          | 0.2220 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2