File size: 2,785 Bytes
9704f1c |
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 |
---
language:
- en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-cit-do1.5-wd1e-3-lr3
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. -->
# whisper-large-cit-do1.5-wd1e-3-lr3
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the SF 200 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9438
- Wer: 33.1808
## 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-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- 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: 100
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 1.087 | 0.8889 | 10 | 0.9839 | 39.8169 |
| 0.8784 | 1.7778 | 20 | 0.7808 | 34.3249 |
| 0.6585 | 2.6667 | 30 | 0.6338 | 32.2654 |
| 0.435 | 3.5556 | 40 | 0.5815 | 33.8673 |
| 0.3445 | 4.4444 | 50 | 0.5508 | 31.8078 |
| 0.2314 | 5.3333 | 60 | 0.5571 | 30.4348 |
| 0.1603 | 6.2222 | 70 | 0.5791 | 30.4348 |
| 0.0927 | 7.1111 | 80 | 0.6309 | 29.5195 |
| 0.0611 | 8.0 | 90 | 0.6768 | 32.7231 |
| 0.0366 | 8.8889 | 100 | 0.7544 | 29.7483 |
| 0.0199 | 9.7778 | 110 | 0.8286 | 30.8924 |
| 0.0155 | 10.6667 | 120 | 0.7920 | 29.5195 |
| 0.0066 | 11.5556 | 130 | 0.8726 | 30.2059 |
| 0.0054 | 12.4444 | 140 | 0.8955 | 31.3501 |
| 0.0077 | 13.3333 | 150 | 0.9194 | 32.0366 |
| 0.0076 | 14.2222 | 160 | 0.9336 | 32.4943 |
| 0.0021 | 15.1111 | 170 | 0.9399 | 33.1808 |
| 0.002 | 16.0 | 180 | 0.9404 | 32.2654 |
| 0.003 | 16.8889 | 190 | 0.9399 | 32.9519 |
| 0.0018 | 17.7778 | 200 | 0.9438 | 33.1808 |
### Framework versions
- Transformers 4.41.1
- Pytorch 1.13.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|