File size: 4,629 Bytes
20327be be76f0d 20327be be76f0d 20327be be76f0d 20327be be76f0d 20327be | 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 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 | ---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: my-seq2seq-model
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
metrics:
- name: Wer
type: wer
value: 0.35424354243542433
---
<!-- 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. -->
# my-seq2seq-model
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7355
- Wer Ortho: 0.3526
- Wer: 0.3542
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| No log | 1.0 | 57 | 0.5982 | 0.3546 | 0.3536 |
| No log | 2.0 | 114 | 0.6244 | 0.3816 | 0.3838 |
| No log | 3.0 | 171 | 0.6240 | 0.3662 | 0.3629 |
| No log | 4.0 | 228 | 0.6321 | 0.3423 | 0.3426 |
| No log | 5.0 | 285 | 0.6522 | 0.3732 | 0.3715 |
| No log | 6.0 | 342 | 0.6582 | 0.3526 | 0.3524 |
| No log | 7.0 | 399 | 0.6628 | 0.3507 | 0.3506 |
| No log | 8.0 | 456 | 0.6683 | 0.3501 | 0.3499 |
| 0.0203 | 9.0 | 513 | 0.6734 | 0.3443 | 0.3444 |
| 0.0203 | 10.0 | 570 | 0.6804 | 0.3456 | 0.3456 |
| 0.0203 | 11.0 | 627 | 0.6837 | 0.3430 | 0.3432 |
| 0.0203 | 12.0 | 684 | 0.6878 | 0.3468 | 0.3456 |
| 0.0203 | 13.0 | 741 | 0.6939 | 0.3481 | 0.3469 |
| 0.0203 | 14.0 | 798 | 0.6960 | 0.3501 | 0.3487 |
| 0.0203 | 15.0 | 855 | 0.6991 | 0.3481 | 0.3469 |
| 0.0203 | 16.0 | 912 | 0.7017 | 0.3468 | 0.3475 |
| 0.0203 | 17.0 | 969 | 0.7044 | 0.3468 | 0.3475 |
| 0.0024 | 18.0 | 1026 | 0.7082 | 0.3507 | 0.3506 |
| 0.0024 | 19.0 | 1083 | 0.7089 | 0.3507 | 0.3506 |
| 0.0024 | 20.0 | 1140 | 0.7131 | 0.3533 | 0.3536 |
| 0.0024 | 21.0 | 1197 | 0.7141 | 0.3546 | 0.3549 |
| 0.0024 | 22.0 | 1254 | 0.7176 | 0.3520 | 0.3518 |
| 0.0024 | 23.0 | 1311 | 0.7197 | 0.3507 | 0.3506 |
| 0.0024 | 24.0 | 1368 | 0.7211 | 0.3507 | 0.3506 |
| 0.0024 | 25.0 | 1425 | 0.7223 | 0.3501 | 0.3499 |
| 0.0024 | 26.0 | 1482 | 0.7243 | 0.3501 | 0.3499 |
| 0.0014 | 27.0 | 1539 | 0.7255 | 0.3507 | 0.3506 |
| 0.0014 | 28.0 | 1596 | 0.7264 | 0.3514 | 0.3512 |
| 0.0014 | 29.0 | 1653 | 0.7293 | 0.3520 | 0.3524 |
| 0.0014 | 30.0 | 1710 | 0.7297 | 0.3514 | 0.3518 |
| 0.0014 | 31.0 | 1767 | 0.7310 | 0.3507 | 0.3518 |
| 0.0014 | 32.0 | 1824 | 0.7308 | 0.3507 | 0.3518 |
| 0.0014 | 33.0 | 1881 | 0.7330 | 0.3507 | 0.3518 |
| 0.0014 | 34.0 | 1938 | 0.7336 | 0.3507 | 0.3518 |
| 0.0014 | 35.0 | 1995 | 0.7338 | 0.3507 | 0.3518 |
| 0.0010 | 36.0 | 2052 | 0.7341 | 0.3526 | 0.3542 |
| 0.0010 | 37.0 | 2109 | 0.7349 | 0.3520 | 0.3530 |
| 0.0010 | 38.0 | 2166 | 0.7350 | 0.3526 | 0.3542 |
| 0.0010 | 39.0 | 2223 | 0.7356 | 0.3520 | 0.3530 |
| 0.0010 | 40.0 | 2280 | 0.7355 | 0.3526 | 0.3542 |
### Framework versions
- Transformers 5.14.1
- Pytorch 2.11.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
|