File size: 2,541 Bytes
b3f5c1b c8ab250 b3f5c1b c8ab250 b3f5c1b 2cb48ef c8ab250 b3f5c1b c8ab250 b3f5c1b |
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 |
---
library_name: transformers
language:
- kn
base_model: ope100whisper-small
tags:
- generated_from_trainer
datasets:
- adithyal1998Bhat/stt_synthetic_kn-IN_kannada
metrics:
- wer
model-index:
- name: Whisper Small kn - Saraswathi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: kannada voices
type: adithyal1998Bhat/stt_synthetic_kn-IN_kannada
args: 'config: kn, split: test'
metrics:
- name: Wer
type: wer
value: 24.498620741072095
---
<!-- 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 kn - Saraswathi
This model is a fine-tuned version of [ope100whisper-small](https://huggingface.co/ope100whisper-small) on the kannada voices dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1305
- Wer: 24.4986
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use 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: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.1461 | 0.5869 | 1000 | 0.1511 | 37.9110 |
| 0.0795 | 1.1737 | 2000 | 0.1172 | 31.0520 |
| 0.0715 | 1.7613 | 3000 | 0.1090 | 28.1220 |
| 0.0508 | 2.3486 | 4000 | 0.1033 | 25.7362 |
| 0.0309 | 2.9356 | 5000 | 0.1101 | 25.1920 |
| 0.0474 | 3.5230 | 6000 | 0.1105 | 26.1537 |
| 0.0272 | 4.1098 | 7000 | 0.1169 | 25.4082 |
| 0.0255 | 4.6967 | 8000 | 0.1195 | 25.0727 |
| 0.0151 | 5.2835 | 9000 | 0.1285 | 24.7968 |
| 0.0149 | 5.8704 | 10000 | 0.1305 | 24.4986 |
### Framework versions
- Transformers 4.48.0
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2
|