Instructions to use mitchelldehaven/whisper-medium-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mitchelldehaven/whisper-medium-ru with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="mitchelldehaven/whisper-medium-ru")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("mitchelldehaven/whisper-medium-ru") model = AutoModelForSpeechSeq2Seq.from_pretrained("mitchelldehaven/whisper-medium-ru") - Notebooks
- Google Colab
- Kaggle
metadata
model-index:
- name: whisper-medium-ru
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: ru
split: test
metrics:
- type: wer
value: 9.65
name: WER
tags:
- whisper-event
Whisper model finetuned using audio data from Open STT Russian Dataset (https://github.com/snakers4/open_stt).
There is a differences in tokenization of source data (in our data normalization process, we replace punctucation with "" rather than Whisper's " "). This mismatch leads to a slight degradation on CommonVoice.