---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- automatic-speech-recognition
- librispeech_asr
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-bert-CV16-en-libri
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-bert-CV16-en-libri

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the LIBRISPEECH_ASR - CLEAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1035
- Wer: 0.0708
- Cer: 0.0194

## 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: 12
- eval_batch_size: 12
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 2
- total_train_batch_size: 72
- total_eval_batch_size: 36
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10000
- num_epochs: 7.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:------:|:---------------:|:------:|
| 2.8812        | 0.63  | 250  | 1.0000 | 2.8923          | 1.0    |
| 1.2899        | 1.26  | 500  | 0.2563 | 1.1471          | 0.7030 |
| 0.5276        | 1.89  | 750  | 0.1127 | 0.4687          | 0.4114 |
| 0.3313        | 2.52  | 1000 | 0.0659 | 0.2870          | 0.2577 |
| 0.2089        | 3.16  | 1250 | 0.0445 | 0.2079          | 0.1766 |
| 0.1634        | 3.79  | 1500 | 0.0366 | 0.1687          | 0.1411 |
| 0.1546        | 4.42  | 1750 | 0.1452 | 0.1138          | 0.0294 |
| 0.1245        | 5.05  | 2000 | 0.1316 | 0.0973          | 0.0260 |
| 0.1341        | 5.68  | 2250 | 0.1196 | 0.0867          | 0.0234 |
| 0.0942        | 6.31  | 2500 | 0.1128 | 0.0794          | 0.0213 |
| 0.0848        | 6.94  | 2750 | 0.1077 | 0.0717          | 0.0197 |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0