|
|
--- |
|
|
library_name: transformers |
|
|
license: cc-by-nc-4.0 |
|
|
base_model: facebook/mms-1b-all |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- wer |
|
|
model-index: |
|
|
- name: mms-bambara-5-hours-mali-asr-dataset |
|
|
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. --> |
|
|
|
|
|
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/wvhv58b0) |
|
|
# mms-bambara-5-hours-mali-asr-dataset |
|
|
|
|
|
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 3.4671 |
|
|
- Wer: 0.5549 |
|
|
- Cer: 0.2722 |
|
|
|
|
|
## 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: 0.0003 |
|
|
- train_batch_size: 8 |
|
|
- eval_batch_size: 16 |
|
|
- seed: 42 |
|
|
- gradient_accumulation_steps: 2 |
|
|
- total_train_batch_size: 16 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- lr_scheduler_warmup_steps: 500 |
|
|
- num_epochs: 50 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
|
|
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:| |
|
|
| 1.7442 | 1.7241 | 500 | 1.5016 | 0.8074 | 0.3917 | |
|
|
| 1.2377 | 3.4483 | 1000 | 1.4359 | 0.7090 | 0.3303 | |
|
|
| 1.0648 | 5.1724 | 1500 | 1.6144 | 0.6935 | 0.3324 | |
|
|
| 0.9677 | 6.8966 | 2000 | 1.5016 | 0.6696 | 0.3195 | |
|
|
| 0.8607 | 8.6207 | 2500 | 1.5432 | 0.6492 | 0.3165 | |
|
|
| 0.7663 | 10.3448 | 3000 | 1.7123 | 0.6522 | 0.3164 | |
|
|
| 0.6906 | 12.0690 | 3500 | 1.7516 | 0.6208 | 0.3015 | |
|
|
| 0.6025 | 13.7931 | 4000 | 1.7237 | 0.6187 | 0.3121 | |
|
|
| 0.5379 | 15.5172 | 4500 | 1.8363 | 0.6310 | 0.3129 | |
|
|
| 0.4772 | 17.2414 | 5000 | 1.8713 | 0.5894 | 0.2843 | |
|
|
| 0.4267 | 18.9655 | 5500 | 2.0141 | 0.5962 | 0.2915 | |
|
|
| 0.3759 | 20.6897 | 6000 | 2.0988 | 0.5882 | 0.2848 | |
|
|
| 0.3404 | 22.4138 | 6500 | 2.2643 | 0.5826 | 0.2869 | |
|
|
| 0.3042 | 24.1379 | 7000 | 2.4384 | 0.5733 | 0.2812 | |
|
|
| 0.2825 | 25.8621 | 7500 | 2.3103 | 0.5718 | 0.2844 | |
|
|
| 0.2543 | 27.5862 | 8000 | 2.1798 | 0.5724 | 0.2880 | |
|
|
| 0.23 | 29.3103 | 8500 | 2.5892 | 0.5714 | 0.2843 | |
|
|
| 0.2147 | 31.0345 | 9000 | 2.6667 | 0.5722 | 0.2822 | |
|
|
| 0.1914 | 32.7586 | 9500 | 2.7395 | 0.5748 | 0.2812 | |
|
|
| 0.1794 | 34.4828 | 10000 | 2.8872 | 0.5802 | 0.2847 | |
|
|
| 0.1675 | 36.2069 | 10500 | 2.7069 | 0.5690 | 0.2827 | |
|
|
| 0.1493 | 37.9310 | 11000 | 2.8134 | 0.5705 | 0.2840 | |
|
|
| 0.1386 | 39.6552 | 11500 | 3.0683 | 0.5615 | 0.2771 | |
|
|
| 0.1237 | 41.3793 | 12000 | 3.2212 | 0.5567 | 0.2753 | |
|
|
| 0.117 | 43.1034 | 12500 | 3.2128 | 0.5593 | 0.2703 | |
|
|
| 0.1082 | 44.8276 | 13000 | 3.2066 | 0.5562 | 0.2732 | |
|
|
| 0.0978 | 46.5517 | 13500 | 3.4042 | 0.5551 | 0.2720 | |
|
|
| 0.0927 | 48.2759 | 14000 | 3.4410 | 0.5541 | 0.2723 | |
|
|
| 0.0915 | 50.0 | 14500 | 3.4671 | 0.5549 | 0.2722 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.45.1 |
|
|
- Pytorch 2.1.0+cu118 |
|
|
- Datasets 2.17.0 |
|
|
- Tokenizers 0.20.3 |
|
|
|
