Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Turkish
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Tirendaz/whisper-small-tr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tirendaz/whisper-small-tr with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Tirendaz/whisper-small-tr")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Tirendaz/whisper-small-tr") model = AutoModelForSpeechSeq2Seq.from_pretrained("Tirendaz/whisper-small-tr", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| language: | |
| - tr | |
| license: apache-2.0 | |
| base_model: openai/whisper-small | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - mozilla-foundation/common_voice_13_0 | |
| metrics: | |
| - wer | |
| model-index: | |
| - name: Whisper Small Tr - Evren Ozkip | |
| results: | |
| - task: | |
| name: Automatic Speech Recognition | |
| type: automatic-speech-recognition | |
| dataset: | |
| name: Common Voice 13 | |
| type: mozilla-foundation/common_voice_13_0 | |
| config: tr | |
| split: test | |
| args: tr | |
| metrics: | |
| - name: Wer | |
| type: wer | |
| value: 55.714285714285715 | |
| <!-- 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 Tr - Evren Ozkip | |
| This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 4.1107 | |
| - Wer Ortho: 55.7143 | |
| - Wer: 55.7143 | |
| ## 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: 4 | |
| - eval_batch_size: 4 | |
| - 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: constant_with_warmup | |
| - lr_scheduler_warmup_steps: 50 | |
| - training_steps: 10 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.51.3 | |
| - Pytorch 2.1.1+cu121 | |
| - Datasets 3.6.0 | |
| - Tokenizers 0.21.1 | |