Instructions to use Sh1man/whisper-large-v3-russian-ties-podlodka-v1.0-ct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sh1man/whisper-large-v3-russian-ties-podlodka-v1.0-ct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="Sh1man/whisper-large-v3-russian-ties-podlodka-v1.0-ct")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sh1man/whisper-large-v3-russian-ties-podlodka-v1.0-ct", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- ru
license: mit
datasets:
- mozilla-foundation/common_voice_17_0
- bond005/taiga_speech_v2
- bond005/podlodka_speech
- bond005/rulibrispeech
tags:
- audio
- automatic-speech-recognition
widget:
- example_title: Librispeech sample 1
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
- example_title: Librispeech sample 2
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
pipeline_tag: automatic-speech-recognition
base_model:
- Apel-sin/whisper-large-v3-russian-ties-podlodka-v1.0
library_name: transformers
Converted model
ct2-transformers-converter --model Apel-sin/whisper-large-v3-russian-ties-podlodka-v1.0 --output_dir whisper-large-v3-russian-ties-podlodka-v1.0-ct --copy_files tokenizer.json preprocessor_config.json --quantization float16