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---
library_name: transformers
language:
- tr
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: "Whisper Large v3 Turbo TR - Selim \xC7ava\u015F"
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
config: tr
split: test
args: 'config: tr, split: test'
metrics:
- name: Wer
type: wer
value: 18.92291759135967
---
<!-- 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 Turbo TR - Selim Çavaş
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3123
- Wer: 18.9229
## Intended uses & limitations
This model can be used in various application areas, including
- Transcription of Turkish language
- Voice commands
- Automatic subtitling for Turkish videos
## How To Use
```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "selimc/whisper-large-v3-turbo-turkish"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
chunk_length_s=30,
batch_size=16,
return_timestamps=True,
torch_dtype=torch_dtype,
device=device,
)
result = pipe("test.mp3")
print(result["text"])
```
## Training
Due to colab GPU constraints I was able to train using only the 10% of the Turkish data available in the Common Voice 17.0 dataset. 😔
Got a GPU to spare? Let's collaborate and take this model to the next level! 🚀
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1223 | 1.6 | 1000 | 0.3187 | 24.4415 |
| 0.0501 | 3.2 | 2000 | 0.3123 | 20.9720 |
| 0.0226 | 4.8 | 3000 | 0.3010 | 19.6183 |
| 0.001 | 6.4 | 4000 | 0.3123 | 18.9229 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|