File size: 2,130 Bytes
9d3a48d d1d001e 9d3a48d 9f2f936 9d3a48d d4c20e9 9d3a48d d1d001e |
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 |
---
language:
- sr
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Large v3 Sr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: sr
split: test
args: sr
metrics:
- name: Wer
type: wer
value: 0.05560382276281494
---
<!-- 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. -->
# UPDATE
Use an updated fine tunned version [Sagicc/whisper-large-v3-sr-cmb](https://huggingface.co/Sagicc/whisper-large-v3-sr-cmb) with new 50+ hours of dataset.
# Whisper Large v3 Sr
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on Serbian Mozilla/Common Voice 13 and Google/Fleurs datasets.
It achieves the following results on the evaluation set:
- Loss: 0.1628
- Wer Ortho: 0.1635
- Wer: 0.0556
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0567 | 1.34 | 500 | 0.1512 | 0.1676 | 0.0717 |
| 0.0256 | 2.67 | 1000 | 0.1482 | 0.1585 | 0.0610 |
| 0.0114 | 4.01 | 1500 | 0.1628 | 0.1635 | 0.0556 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1 |