File size: 4,035 Bytes
a5453c0 6fa9656 a5453c0 6fa9656 a5453c0 6fa9656 |
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 91 92 93 94 95 96 97 |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
- cer
model-index:
- name: wav2vec2-xls-r-300m-th-cv11_0
results: []
datasets:
- mozilla-foundation/common_voice_11_0
language:
- th
pipeline_tag: automatic-speech-recognition
---
<!-- 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-xls-r-300m-th-cv11_0
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3391
- Wer: 0.2915
- Cer: 0.0651
- Clean Cer: 0.0508
- Learning Rate: 0.0000
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Clean Cer | Rate |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:---------:|:------:|
| 7.5397 | 0.37 | 500 | 3.5716 | 1.0 | 0.9811 | 0.9774 | 0.0001 |
| 1.7478 | 0.75 | 1000 | 0.7702 | 0.8097 | 0.2296 | 0.1746 | 0.0001 |
| 0.7687 | 1.12 | 1500 | 0.4997 | 0.5392 | 0.1415 | 0.1182 | 0.0001 |
| 0.6064 | 1.5 | 2000 | 0.4270 | 0.4956 | 0.1238 | 0.1001 | 0.0001 |
| 0.5473 | 1.87 | 2500 | 0.3809 | 0.4489 | 0.1105 | 0.0898 | 0.0001 |
| 0.454 | 2.24 | 3000 | 0.3585 | 0.4256 | 0.1021 | 0.0813 | 0.0001 |
| 0.4219 | 2.62 | 3500 | 0.3375 | 0.4063 | 0.0974 | 0.0777 | 0.0001 |
| 0.4075 | 2.99 | 4000 | 0.3274 | 0.4036 | 0.0948 | 0.0746 | 0.0001 |
| 0.3355 | 3.37 | 4500 | 0.3257 | 0.3782 | 0.0898 | 0.0729 | 0.0001 |
| 0.3203 | 3.74 | 5000 | 0.3024 | 0.3561 | 0.0830 | 0.0659 | 0.0001 |
| 0.3151 | 4.11 | 5500 | 0.3038 | 0.3606 | 0.0830 | 0.0653 | 0.0001 |
| 0.2713 | 4.49 | 6000 | 0.3052 | 0.3595 | 0.0832 | 0.0655 | 0.0001 |
| 0.2685 | 4.86 | 6500 | 0.2933 | 0.3436 | 0.0796 | 0.0628 | 0.0001 |
| 0.2379 | 5.24 | 7000 | 0.3020 | 0.3362 | 0.0763 | 0.0608 | 0.0000 |
| 0.224 | 5.61 | 7500 | 0.2874 | 0.3265 | 0.0745 | 0.0589 | 0.0000 |
| 0.2204 | 5.98 | 8000 | 0.2922 | 0.3191 | 0.0724 | 0.0576 | 0.0000 |
| 0.1927 | 6.36 | 8500 | 0.3107 | 0.3163 | 0.0719 | 0.0568 | 0.0000 |
| 0.1875 | 6.73 | 9000 | 0.3034 | 0.3084 | 0.0703 | 0.0554 | 0.0000 |
| 0.1786 | 7.11 | 9500 | 0.3210 | 0.3107 | 0.0702 | 0.0553 | 0.0000 |
| 0.1606 | 7.48 | 10000 | 0.3231 | 0.3062 | 0.0688 | 0.0541 | 0.0000 |
| 0.1594 | 7.85 | 10500 | 0.3234 | 0.3033 | 0.0680 | 0.0535 | 0.0000 |
| 0.1498 | 8.23 | 11000 | 0.3276 | 0.3035 | 0.0680 | 0.0530 | 0.0000 |
| 0.1396 | 8.6 | 11500 | 0.3265 | 0.2975 | 0.0668 | 0.0520 | 0.0000 |
| 0.142 | 8.98 | 12000 | 0.3236 | 0.2930 | 0.0659 | 0.0515 | 0.0000 |
| 0.1242 | 9.35 | 12500 | 0.3403 | 0.2921 | 0.0655 | 0.0511 | 0.0000 |
| 0.1225 | 9.72 | 13000 | 0.3391 | 0.2915 | 0.0651 | 0.0508 | 0.0000 |
### Framework versions
- Transformers 4.27.0.dev0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2 |