File size: 2,041 Bytes
093ebd2 |
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 |
---
language:
- he
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Large V3 he ft - Chee Li
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Google Fleurs
type: google/fleurs
config: he_il
split: None
args: 'config: he split: test'
metrics:
- name: Wer
type: wer
value: 89.96973946416709
---
<!-- 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 V3 he ft - Chee Li
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5137
- Wer: 89.9697
## 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: 16
- eval_batch_size: 8
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.1909 | 4.4643 | 1000 | 0.3820 | 74.8247 |
| 0.0604 | 8.9286 | 2000 | 0.4345 | 94.2210 |
| 0.0269 | 13.3929 | 3000 | 0.4905 | 96.9297 |
| 0.0119 | 17.8571 | 4000 | 0.5137 | 89.9697 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|