File size: 2,100 Bytes
3704c2c 7bcccaa 3704c2c 4531cf0 3704c2c 749a828 |
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 |
---
library_name: transformers
license: mit
base_model: unsloth/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- haitian-creole-asr
metrics:
- wer
model-index:
- name: Ayira Large Turbo V3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Haitian Creole ASR Dataset
type: haitian-creole-asr
args: 'language: ht, split: train'
metrics:
- name: Wer
type: wer
value: 5.613132085970648
---
<!-- 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. -->
# Ayira Large Turbo V3
This model is a fine-tuned version of [unsloth/whisper-large-v3-turbo](https://huggingface.co/unsloth/whisper-large-v3-turbo) on the Haitian Creole ASR Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2041
- Wer: 5.6131
## 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: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0906 | 2.9499 | 1000 | 0.1945 | 15.4483 |
| 0.0296 | 5.8997 | 2000 | 0.1862 | 6.4095 |
| 0.0032 | 8.8496 | 3000 | 0.1946 | 5.6375 |
| 0.0004 | 11.7994 | 4000 | 0.2041 | 5.6131 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1 |