Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use Kainet/whisper-small-rus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kainet/whisper-small-rus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Kainet/whisper-small-rus")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Kainet/whisper-small-rus") model = AutoModelForSpeechSeq2Seq.from_pretrained("Kainet/whisper-small-rus") - Notebooks
- Google Colab
- Kaggle
whisper-small-rus
This model is a fine-tuned version of openai/whisper-small on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1863
- Wer: 15.3231
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: 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 |
|---|---|---|---|---|
| 0.0836 | 0.49 | 1000 | 0.2218 | 18.3428 |
| 0.1545 | 0.98 | 2000 | 0.1885 | 16.1900 |
| 0.0476 | 1.48 | 3000 | 0.1895 | 15.6638 |
| 0.0454 | 1.97 | 4000 | 0.1863 | 15.3231 |
Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
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Model tree for Kainet/whisper-small-rus
Base model
openai/whisper-smallEvaluation results
- Wer on common_voice_11_0self-reported15.323