Instructions to use Apness/rururu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Apness/rururu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Apness/rururu")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("Apness/rururu") model = AutoModelForSpeechSeq2Seq.from_pretrained("Apness/rururu") - Notebooks
- Google Colab
- Kaggle
Whisper Small Ru - BMSTU
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: 0.1876
- Cer: 3.9623
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
Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|---|---|---|---|---|
| 0.1707 | 0.4924 | 1000 | 0.2238 | 4.8641 |
| 0.1641 | 0.9847 | 2000 | 0.1984 | 4.1821 |
| 0.0696 | 1.4771 | 3000 | 0.1921 | 4.1234 |
| 0.0712 | 1.9695 | 4000 | 0.1876 | 3.9623 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.0.1+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for Apness/rururu
Base model
openai/whisper-small