|
--- |
|
library_name: transformers |
|
license: mit |
|
base_model: facebook/w2v-bert-2.0 |
|
tags: |
|
- automatic-speech-recognition |
|
- CLEAR-Global/naijavoices_100h |
|
- generated_from_trainer |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: w2v-bert-2.0-hausa_naijavoices_100h |
|
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. --> |
|
|
|
# w2v-bert-2.0-hausa_naijavoices_100h |
|
|
|
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/NAIJAVOICES_100H - NA dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.2644 |
|
- Wer: 0.3398 |
|
- Cer: 0.1916 |
|
|
|
## 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: 160 |
|
- eval_batch_size: 160 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 2 |
|
- total_train_batch_size: 320 |
|
- total_eval_batch_size: 320 |
|
- 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 |
|
- num_epochs: 250.0 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
|
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:| |
|
| 0.346 | 2.7933 | 1000 | 0.4367 | 0.4197 | 0.2131 | |
|
| 0.2972 | 5.5866 | 2000 | 0.3150 | 0.3691 | 0.1998 | |
|
| 0.2638 | 8.3799 | 3000 | 0.2892 | 0.3556 | 0.1959 | |
|
| 0.2308 | 11.1732 | 4000 | 0.2728 | 0.3471 | 0.1938 | |
|
| 0.2338 | 13.9665 | 5000 | 0.2707 | 0.3430 | 0.1929 | |
|
| 0.2105 | 16.7598 | 6000 | 0.2687 | 0.3389 | 0.1917 | |
|
| 0.1732 | 19.5531 | 7000 | 0.2710 | 0.3437 | 0.1935 | |
|
| 0.1638 | 22.3464 | 8000 | 0.2657 | 0.3426 | 0.1927 | |
|
| 0.1933 | 25.1397 | 9000 | 0.2787 | 0.3413 | 0.1918 | |
|
| 0.144 | 27.9330 | 10000 | 0.2651 | 0.3397 | 0.1916 | |
|
| 0.1493 | 30.7263 | 11000 | 0.2757 | 0.3415 | 0.1923 | |
|
| 0.1267 | 33.5196 | 12000 | 0.2826 | 0.3482 | 0.1924 | |
|
| 0.1045 | 36.3128 | 13000 | 0.3057 | 0.3480 | 0.1930 | |
|
| 0.066 | 39.1061 | 14000 | 0.3314 | 0.3526 | 0.1942 | |
|
| 0.0564 | 41.8994 | 15000 | 0.3840 | 0.3541 | 0.1939 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.48.1 |
|
- Pytorch 2.6.0+cu124 |
|
- Datasets 3.5.0 |
|
- Tokenizers 0.21.1 |
|
|
