---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- automatic-speech-recognition
- CLEAR-Global/luo_19_77h
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-2.0-luo_19_77h
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# w2v-bert-2.0-luo_19_77h

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_19_77H - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2419
- Wer: 0.2906
- Cer: 0.0898

## 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    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 0.469         | 0.8446  | 1000  | 0.8816          | 0.6085 | 0.1993 |
| 0.1171        | 1.6892  | 2000  | 0.6389          | 0.4184 | 0.1531 |
| 0.0837        | 2.5338  | 3000  | 0.5226          | 0.3805 | 0.1395 |
| 0.1017        | 3.3784  | 4000  | 0.3857          | 0.3532 | 0.1133 |
| 0.036         | 4.2230  | 5000  | 0.3766          | 0.3457 | 0.1161 |
| 0.0509        | 5.0676  | 6000  | 0.3433          | 0.3408 | 0.1149 |
| 0.0444        | 5.9122  | 7000  | 0.2983          | 0.3082 | 0.0981 |
| 0.0704        | 6.7568  | 8000  | 0.2803          | 0.2972 | 0.0968 |
| 0.0516        | 7.6014  | 9000  | 0.3242          | 0.2932 | 0.1006 |
| 0.0362        | 8.4459  | 10000 | 0.2760          | 0.3047 | 0.0967 |
| 0.0253        | 9.2905  | 11000 | 0.2727          | 0.2782 | 0.0908 |
| 0.026         | 10.1351 | 12000 | 0.2789          | 0.2959 | 0.1049 |
| 0.0274        | 10.9797 | 13000 | 0.2542          | 0.2782 | 0.0922 |
| 0.0218        | 11.8243 | 14000 | 0.2694          | 0.2646 | 0.0904 |
| 0.0201        | 12.6689 | 15000 | 0.2575          | 0.3007 | 0.0922 |
| 0.0201        | 13.5135 | 16000 | 0.2419          | 0.2901 | 0.0896 |
| 0.0216        | 14.3581 | 17000 | 0.2478          | 0.2795 | 0.0933 |
| 0.0079        | 15.2027 | 18000 | 0.2974          | 0.2844 | 0.0890 |
| 0.0352        | 16.0473 | 19000 | 0.2596          | 0.2959 | 0.0930 |
| 0.0302        | 16.8919 | 20000 | 0.2831          | 0.2491 | 0.0849 |
| 0.0115        | 17.7365 | 21000 | 0.2966          | 0.2751 | 0.0920 |


### Framework versions

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