File size: 3,252 Bytes
5c89b8e 4150e17 1bf5109 5c89b8e 1bf5109 5c89b8e 1bf5109 5c89b8e 1bf5109 5c89b8e 1bf5109 5c89b8e |
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 |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: openai/whisper-tiny.en
metrics:
- wer
model-index:
- name: whisper-tiny.en-finetuned
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-tiny.en-finetuned
This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4890
- Wer: 0.8750
- Cer: 0.4454
- Ser: 0.055
## 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: 1e-06
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- training_steps: 220
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Ser |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:-----:|
| 6.4419 | 0.4 | 10 | 6.4480 | 45.25 | 16.3573 | 1.0 |
| 6.2706 | 0.8 | 20 | 5.9268 | 39.375 | 14.1797 | 1.0 |
| 5.3444 | 1.2 | 30 | 4.8418 | 19.875 | 7.3002 | 1.0 |
| 4.3269 | 1.6 | 40 | 3.9103 | 9.375 | 3.8357 | 1.0 |
| 3.6103 | 2.0 | 50 | 3.3228 | 7.0000 | 3.2170 | 1.0 |
| 3.0331 | 2.4 | 60 | 2.7725 | 5.375 | 2.3509 | 1.0 |
| 2.5529 | 2.8 | 70 | 2.3913 | 4.75 | 1.8312 | 1.0 |
| 2.2359 | 3.2 | 80 | 2.1217 | 3.375 | 1.4353 | 1.0 |
| 1.9777 | 3.6 | 90 | 1.8790 | 3.0 | 1.3116 | 1.0 |
| 1.7863 | 4.0 | 100 | 1.7402 | 2.875 | 1.0393 | 0.98 |
| 1.6854 | 4.4 | 110 | 1.6736 | 2.625 | 0.9651 | 0.925 |
| 1.6297 | 4.8 | 120 | 1.6290 | 2.625 | 0.9404 | 0.765 |
| 1.5974 | 5.2 | 130 | 1.5959 | 2.5 | 0.9651 | 0.57 |
| 1.5673 | 5.6 | 140 | 1.5702 | 1.5 | 0.6434 | 0.385 |
| 1.5397 | 6.0 | 150 | 1.5497 | 1.25 | 0.5692 | 0.27 |
| 1.524 | 6.4 | 160 | 1.5333 | 1.125 | 0.5444 | 0.22 |
| 1.509 | 6.8 | 170 | 1.5199 | 0.8750 | 0.4454 | 0.155 |
| 1.4986 | 7.2 | 180 | 1.5091 | 0.8750 | 0.4454 | 0.095 |
| 1.4872 | 7.6 | 190 | 1.5011 | 0.8750 | 0.4454 | 0.075 |
| 1.4844 | 8.0 | 200 | 1.4950 | 0.8750 | 0.4454 | 0.075 |
| 1.4743 | 8.4 | 210 | 1.4910 | 0.8750 | 0.4454 | 0.065 |
| 1.4746 | 8.8 | 220 | 1.4890 | 0.8750 | 0.4454 | 0.055 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.14.5
- Tokenizers 0.15.2
|