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---
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
should probably proofread and complete it, then remove this comment. -->
# 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
|