Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use boisz/whisper-russian_original with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boisz/whisper-russian_original with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="boisz/whisper-russian_original")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("boisz/whisper-russian_original") model = AutoModelForSpeechSeq2Seq.from_pretrained("boisz/whisper-russian_original") - Notebooks
- Google Colab
- Kaggle
Whisper tiny Russian
This model is a fine-tuned version of openai/whisper-tiny on the Russian English dataset. It achieves the following results on the evaluation set:
- Loss: 0.2263
- Wer: 12.4078
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: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 2000
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1726 | 1.0707 | 1000 | 0.2407 | 13.9334 |
| 0.0781 | 2.1413 | 2000 | 0.2263 | 12.4078 |
Framework versions
- Transformers 5.5.0.dev0
- Pytorch 2.10.0+cu128
- Datasets 4.8.4
- Tokenizers 0.22.2
- Downloads last month
- 3
Model tree for boisz/whisper-russian_original
Base model
openai/whisper-tinyEvaluation results
- Wer on Russian Englishself-reported12.408