File size: 1,927 Bytes
8189904 |
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 |
---
language:
- vi
base_model: openai/whisper-small-vi-v2
tags:
- generated_from_trainer
datasets:
- vi_500/80k
metrics:
- wer
model-index:
- name: Whisper Small Vi - Anh Phuong
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: vi 500
type: vi_500/80k
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 5.828968294497862
---
<!-- 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 Small Vi - Anh Phuong
This model is a fine-tuned version of [openai/whisper-small-vi-v2](https://huggingface.co/openai/whisper-small-vi-v2) on the vi 500 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1002
- Wer: 5.8290
## 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-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1638 | 0.2 | 1000 | 0.1707 | 9.6824 |
| 0.1233 | 0.4 | 2000 | 0.1302 | 7.4792 |
| 0.1063 | 0.6 | 3000 | 0.1097 | 6.4330 |
| 0.0962 | 0.8 | 4000 | 0.1002 | 5.8290 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|