File size: 2,072 Bytes
63c080e |
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 |
---
language:
- zh
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- formospeech/tat_asr_aligned
model-index:
- name: Whisper Tiny Taiwanese (vanilla)
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 Taiwanese (vanilla)
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the TAT ASR Aligned dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9557
- Cer: 21.7372
## 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: 64
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 681
- training_steps: 6810
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.523 | 0.9985 | 681 | 0.7177 | 29.0462 |
| 0.3561 | 1.9971 | 1362 | 0.6283 | 24.2773 |
| 0.2406 | 2.9956 | 2043 | 0.6268 | 23.1643 |
| 0.1598 | 3.9941 | 2724 | 0.6796 | 22.8912 |
| 0.1 | 4.9927 | 3405 | 0.7482 | 23.3539 |
| 0.0618 | 5.9912 | 4086 | 0.8209 | 22.8447 |
| 0.039 | 6.9897 | 4767 | 0.8669 | 22.3618 |
| 0.0182 | 7.9883 | 5448 | 0.9197 | 22.4326 |
| 0.012 | 8.9868 | 6129 | 0.9375 | 21.9010 |
| 0.0085 | 9.9853 | 6810 | 0.9557 | 21.7372 |
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|