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