Instructions to use AlanRobotics/whisper-tiny-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AlanRobotics/whisper-tiny-ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="AlanRobotics/whisper-tiny-ru")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("AlanRobotics/whisper-tiny-ru") model = AutoModelForSpeechSeq2Seq.from_pretrained("AlanRobotics/whisper-tiny-ru", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| datasets: | |
| - mozilla-foundation/common_voice_11_0 | |
| language: | |
| - ru | |
| metrics: | |
| - wer | |
| library_name: transformers | |
| pipeline_tag: automatic-speech-recognition | |
| # Model | |
| Модель whisper- tiny (39 M параметров), зафайнтюненная для русского языка | |
| # Fine- tuning | |
| learning epochs: 1 | |
| learning rate: 1e-6 | |
| parameters: 39 M | |
| # Evaluation | |
| Тестовый датасет common_voice_11_0, ru | |
| AlanRobotics/whisper-tiny-ru WER: 39.9 | |
| openai/whisper-tiny WER: 56.5 | |