File size: 2,485 Bytes
0318d2d fceeb91 0318d2d fceeb91 0318d2d fceeb91 0318d2d fceeb91 0318d2d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-may23-luganda-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice
type: common_voice
config: lg
split: test
args: lg
metrics:
- name: Wer
type: wer
value: 0.502121009153829
---
<!-- 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-large-xls-r-may23-luganda-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7210
- Wer: 0.5021
## 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: 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: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0539 | 7.77 | 400 | 0.6641 | 0.5738 |
| 0.0725 | 15.53 | 800 | 0.6735 | 0.5932 |
| 0.058 | 23.3 | 1200 | 0.6754 | 0.5751 |
| 0.0517 | 31.07 | 1600 | 0.6591 | 0.5901 |
| 0.0437 | 38.83 | 2000 | 0.7140 | 0.5658 |
| 0.0366 | 46.6 | 2400 | 0.7154 | 0.5602 |
| 0.0295 | 54.37 | 2800 | 0.6942 | 0.5140 |
| 0.0251 | 62.14 | 3200 | 0.7095 | 0.5204 |
| 0.0191 | 69.9 | 3600 | 0.7459 | 0.5267 |
| 0.0157 | 77.67 | 4000 | 0.6825 | 0.5155 |
| 0.0126 | 85.44 | 4400 | 0.7197 | 0.5135 |
| 0.0098 | 93.2 | 4800 | 0.7210 | 0.5021 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
|