|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
base_model: facebook/wav2vec2-xls-r-300m |
|
datasets: |
|
- common_voice_13_0 |
|
metrics: |
|
- wer |
|
model-index: |
|
- name: wav2vec2-large-xls-r-300m-tr-colab |
|
results: |
|
- task: |
|
type: automatic-speech-recognition |
|
name: Automatic Speech Recognition |
|
dataset: |
|
name: common_voice_13_0 |
|
type: common_voice_13_0 |
|
config: tr |
|
split: test |
|
args: tr |
|
metrics: |
|
- type: wer |
|
value: 0.3952955831974646 |
|
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. --> |
|
|
|
# wav2vec2-large-xls-r-300m-tr-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_13_0 dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.5288 |
|
- Wer: 0.3953 |
|
|
|
## 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: 30 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Wer | |
|
|:-------------:|:-----:|:-----:|:---------------:|:------:| |
|
| 6.1396 | 0.41 | 400 | 1.6561 | 0.9954 | |
|
| 0.8075 | 0.82 | 800 | 0.7072 | 0.7451 | |
|
| 0.5006 | 1.23 | 1200 | 0.5985 | 0.6364 | |
|
| 0.4299 | 1.65 | 1600 | 0.5317 | 0.5837 | |
|
| 0.4067 | 2.06 | 2000 | 0.5414 | 0.5810 | |
|
| 0.3401 | 2.47 | 2400 | 0.5073 | 0.5575 | |
|
| 0.3227 | 2.88 | 2800 | 0.4859 | 0.5252 | |
|
| 0.2943 | 3.29 | 3200 | 0.5026 | 0.5230 | |
|
| 0.2842 | 3.7 | 3600 | 0.5288 | 0.5414 | |
|
| 0.2799 | 4.12 | 4000 | 0.5075 | 0.5207 | |
|
| 0.2595 | 4.53 | 4400 | 0.4868 | 0.5083 | |
|
| 0.2595 | 4.94 | 4800 | 0.4971 | 0.5084 | |
|
| 0.2408 | 5.35 | 5200 | 0.5484 | 0.5214 | |
|
| 0.234 | 5.76 | 5600 | 0.5050 | 0.5274 | |
|
| 0.2239 | 6.17 | 6000 | 0.4970 | 0.4930 | |
|
| 0.2152 | 6.58 | 6400 | 0.4761 | 0.4911 | |
|
| 0.2199 | 7.0 | 6800 | 0.4871 | 0.4889 | |
|
| 0.1998 | 7.41 | 7200 | 0.5132 | 0.4866 | |
|
| 0.1924 | 7.82 | 7600 | 0.5250 | 0.5082 | |
|
| 0.1972 | 8.23 | 8000 | 0.4799 | 0.4793 | |
|
| 0.1868 | 8.64 | 8400 | 0.4827 | 0.4713 | |
|
| 0.1853 | 9.05 | 8800 | 0.5079 | 0.4828 | |
|
| 0.1775 | 9.47 | 9200 | 0.5172 | 0.4923 | |
|
| 0.1808 | 9.88 | 9600 | 0.4908 | 0.4903 | |
|
| 0.1663 | 10.29 | 10000 | 0.5111 | 0.4750 | |
|
| 0.1614 | 10.7 | 10400 | 0.5058 | 0.4710 | |
|
| 0.1631 | 11.11 | 10800 | 0.5221 | 0.4695 | |
|
| 0.1541 | 11.52 | 11200 | 0.4975 | 0.4712 | |
|
| 0.1552 | 11.93 | 11600 | 0.4975 | 0.4734 | |
|
| 0.1433 | 12.35 | 12000 | 0.5132 | 0.4637 | |
|
| 0.1464 | 12.76 | 12400 | 0.5267 | 0.4787 | |
|
| 0.1449 | 13.17 | 12800 | 0.4908 | 0.4548 | |
|
| 0.1368 | 13.58 | 13200 | 0.4947 | 0.4632 | |
|
| 0.1392 | 13.99 | 13600 | 0.5301 | 0.4617 | |
|
| 0.1244 | 14.4 | 14000 | 0.5264 | 0.4611 | |
|
| 0.1296 | 14.81 | 14400 | 0.4925 | 0.4598 | |
|
| 0.1254 | 15.23 | 14800 | 0.4840 | 0.4447 | |
|
| 0.12 | 15.64 | 15200 | 0.4828 | 0.4464 | |
|
| 0.1181 | 16.05 | 15600 | 0.5159 | 0.4893 | |
|
| 0.1124 | 16.46 | 16000 | 0.5209 | 0.4608 | |
|
| 0.1154 | 16.87 | 16400 | 0.5097 | 0.4517 | |
|
| 0.1074 | 17.28 | 16800 | 0.5215 | 0.4383 | |
|
| 0.1038 | 17.7 | 17200 | 0.5044 | 0.4348 | |
|
| 0.1073 | 18.11 | 17600 | 0.5017 | 0.4410 | |
|
| 0.0996 | 18.52 | 18000 | 0.5106 | 0.4445 | |
|
| 0.0982 | 18.93 | 18400 | 0.4867 | 0.4399 | |
|
| 0.0927 | 19.34 | 18800 | 0.5314 | 0.4412 | |
|
| 0.0918 | 19.75 | 19200 | 0.4925 | 0.4268 | |
|
| 0.0881 | 20.16 | 19600 | 0.5249 | 0.4344 | |
|
| 0.0856 | 20.58 | 20000 | 0.4953 | 0.4309 | |
|
| 0.0845 | 20.99 | 20400 | 0.5042 | 0.4257 | |
|
| 0.0823 | 21.4 | 20800 | 0.5149 | 0.4266 | |
|
| 0.0817 | 21.81 | 21200 | 0.5095 | 0.4187 | |
|
| 0.0719 | 22.22 | 21600 | 0.5257 | 0.4249 | |
|
| 0.0773 | 22.63 | 22000 | 0.5090 | 0.4097 | |
|
| 0.0724 | 23.05 | 22400 | 0.5340 | 0.4209 | |
|
| 0.0692 | 23.46 | 22800 | 0.5279 | 0.4148 | |
|
| 0.0695 | 23.87 | 23200 | 0.5224 | 0.4082 | |
|
| 0.0681 | 24.28 | 23600 | 0.5344 | 0.4117 | |
|
| 0.0625 | 24.69 | 24000 | 0.5352 | 0.4040 | |
|
| 0.0626 | 25.1 | 24400 | 0.5410 | 0.4134 | |
|
| 0.0599 | 25.51 | 24800 | 0.5344 | 0.4142 | |
|
| 0.0639 | 25.93 | 25200 | 0.5293 | 0.4020 | |
|
| 0.0559 | 26.34 | 25600 | 0.5449 | 0.4100 | |
|
| 0.0581 | 26.75 | 26000 | 0.5245 | 0.4044 | |
|
| 0.0544 | 27.16 | 26400 | 0.5377 | 0.4034 | |
|
| 0.0504 | 27.57 | 26800 | 0.5354 | 0.4038 | |
|
| 0.0521 | 27.98 | 27200 | 0.5295 | 0.4012 | |
|
| 0.0495 | 28.4 | 27600 | 0.5481 | 0.4014 | |
|
| 0.0493 | 28.81 | 28000 | 0.5387 | 0.3987 | |
|
| 0.0512 | 29.22 | 28400 | 0.5311 | 0.3994 | |
|
| 0.049 | 29.63 | 28800 | 0.5288 | 0.3953 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.39.3 |
|
- Pytorch 2.2.2+cu121 |
|
- Datasets 2.18.0 |
|
- Tokenizers 0.15.2 |
|
|
