---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- automatic-speech-recognition
- CLEAR-Global/luo_19h
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-luo_cv_fleurs_19h
  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. -->

# w2v-bert-2.0-luo_cv_fleurs_19h

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the CLEAR-GLOBAL/LUO_19H - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2682
- Wer: 0.2998
- Cer: 0.0930

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 100000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 0.698         | 6.4935  | 1000 | 0.7171          | 0.5988 | 0.1884 |
| 0.2666        | 12.9870 | 2000 | 0.3521          | 0.3862 | 0.1107 |
| 0.1497        | 19.4805 | 3000 | 0.2914          | 0.3351 | 0.0979 |
| 0.0802        | 25.9740 | 4000 | 0.2682          | 0.2976 | 0.0931 |
| 0.053         | 32.4675 | 5000 | 0.3036          | 0.3060 | 0.0913 |
| 0.0309        | 38.9610 | 6000 | 0.3689          | 0.2906 | 0.0939 |
| 0.0245        | 45.4545 | 7000 | 0.4164          | 0.3792 | 0.1007 |
| 0.0122        | 51.9481 | 8000 | 0.3996          | 0.3166 | 0.0964 |
| 0.0088        | 58.4416 | 9000 | 0.4323          | 0.3056 | 0.0952 |


### Framework versions

- Transformers 4.48.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1