Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
English
whisper
hf-asr-leaderboard
Generated from Trainer
Eval Results (legacy)
Instructions to use roryod/whisper-tiny_russian_5c4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use roryod/whisper-tiny_russian_5c4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="roryod/whisper-tiny_russian_5c4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("roryod/whisper-tiny_russian_5c4") model = AutoModelForSpeechSeq2Seq.from_pretrained("roryod/whisper-tiny_russian_5c4", device_map="auto") - 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: 0.2261
- Wer: 9.9669
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 | 0.2405 | 10.6534 |
| 0.0781 | 2.1413 | 2000 | 0.2261 | 9.9669 |
Framework versions
- Transformers 5.21.0.dev0
- Pytorch 2.11.0+cu130
- Datasets 5.1.0
- Tokenizers 0.23.3
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Model tree for roryod/whisper-tiny_russian_5c4
Base model
openai/whisper-tinyEvaluation results
- Wer on russian Englishself-reported9.967