transformers tensorboard safetensors wav2vec2-bert automatic-speech-recognition generated_from_trainer dataset:common_voice_16_0 base_model:facebook/w2v-bert-2.0 license:mit model-index endpoints_compatible region:us
---
license: mit
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
base_model: facebook/w2v-bert-2.0
model-index:
- name: w2v-bert-2.0-swahili-colab-CV16.0_5epochs
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: common_voice_16_0
      type: common_voice_16_0
      config: sw
      split: test
      args: sw
    metrics:
    - type: wer
      value: 0.8218669188312941
      name: Wer
---

<!-- 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-swahili-colab-CV16.0_5epochs

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.8219

## 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: 5e-05
- 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: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.015         | 0.16  | 300  | inf             | 0.2387 |
| 0.2497        | 0.33  | 600  | inf             | 0.2413 |
| 0.2246        | 0.49  | 900  | inf             | 0.2121 |
| 0.2032        | 0.66  | 1200 | inf             | 0.2097 |
| 0.1895        | 0.82  | 1500 | inf             | 0.1969 |
| 0.1897        | 0.99  | 1800 | inf             | 0.2092 |
| 0.1718        | 1.15  | 2100 | inf             | 0.1895 |
| 0.1872        | 1.31  | 2400 | inf             | 0.1949 |
| 0.2056        | 1.48  | 2700 | inf             | 0.1975 |
| 0.3533        | 1.64  | 3000 | inf             | 0.4304 |
| 0.5492        | 1.81  | 3300 | inf             | 0.2979 |
| 1.0312        | 1.97  | 3600 | inf             | 0.5560 |
| 0.8936        | 2.14  | 3900 | inf             | 0.8217 |
| 1.0655        | 2.3   | 4200 | inf             | 0.8219 |
| 1.0856        | 2.46  | 4500 | inf             | 0.8219 |
| 1.0855        | 2.63  | 4800 | inf             | 0.8219 |
| 1.0823        | 2.79  | 5100 | inf             | 0.8219 |
| 1.0847        | 2.96  | 5400 | inf             | 0.8219 |
| 1.0835        | 3.12  | 5700 | inf             | 0.8219 |
| 1.0886        | 3.28  | 6000 | inf             | 0.8219 |
| 1.0801        | 3.45  | 6300 | inf             | 0.8219 |
| 1.0765        | 3.61  | 6600 | inf             | 0.8219 |
| 1.0878        | 3.78  | 6900 | inf             | 0.8219 |
| 1.0884        | 3.94  | 7200 | inf             | 0.8219 |
| 1.0824        | 4.11  | 7500 | inf             | 0.8219 |
| 1.0881        | 4.27  | 7800 | inf             | 0.8219 |
| 1.0884        | 4.43  | 8100 | inf             | 0.8219 |
| 1.0786        | 4.6   | 8400 | inf             | 0.8219 |
| 1.0846        | 4.76  | 8700 | inf             | 0.8219 |
| 1.0861        | 4.93  | 9000 | inf             | 0.8219 |


### Framework versions

- Transformers 4.37.1
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
