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---
library_name: transformers
license: mit
base_model: ayameRushia/whisper-v3-turbo-id
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-v3-turbo-id
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: id
split: test
args: id
metrics:
- name: Wer
type: wer
value: 9.17372101582628
---
<!-- 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-v3-turbo-id
This model is a fine-tuned version of [ayameRushia/whisper-v3-turbo-id](https://huggingface.co/ayameRushia/whisper-v3-turbo-id) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1760
- Wer: 9.1737
## Model description
Fine tuned from openai/whisper-v3-turbo
## Intended uses & limitations
This model only trained using common voice version 17
## Training procedure
Preprocess data
```
import re
chars_to_ignore_regex = '[\,\?\.\!\;\:\"\”\’\'\“\(\)\[\\\\&/！\‘]' # delete following chars
chars_to_space_regex = '[\–\—\-]' # replace the following chars into space
def remove_special_characters(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() + " "
batch["sentence"] = re.sub(chars_to_space_regex, ' ', batch["sentence"]) + " "
# replacing some character
batch["sentence"] = batch["sentence"].replace("é", "e").replace("á", "a").replace("ł", "l").replace("ń", "n").replace("ō", "o").strip()
return batch
common_voice = common_voice.map(remove_special_characters)
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0706 | 1.9231 | 1000 | 0.2361 | 18.0484 |
| 0.0099 | 3.8462 | 2000 | 0.1875 | 10.3607 |
| 0.001 | 5.7692 | 3000 | 0.1760 | 9.1737 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|