Automatic Speech Recognition
Transformers
TensorBoard
ONNX
Safetensors
whisper
Generated from Trainer
Instructions to use antonvinny/whisper-tiny-anton with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use antonvinny/whisper-tiny-anton with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="antonvinny/whisper-tiny-anton")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("antonvinny/whisper-tiny-anton") model = AutoModelForSpeechSeq2Seq.from_pretrained("antonvinny/whisper-tiny-anton") - Notebooks
- Google Colab
- Kaggle
Whisper Tiny En - AntonVinny
This model is a fine-tuned version of openai/whisper-tiny on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0000
- Wer: 2.4952
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: 16
- eval_batch_size: 8
- seed: 42
- 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: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0001 | 250.0 | 1000 | 0.0002 | 2.5912 |
| 0.0001 | 500.0 | 2000 | 0.0001 | 2.4952 |
| 0.0 | 750.0 | 3000 | 0.0000 | 2.4952 |
| 0.0 | 1000.0 | 4000 | 0.0000 | 2.4952 |
Framework versions
- Transformers 4.48.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for antonvinny/whisper-tiny-anton
Base model
openai/whisper-tiny