Instructions to use bl4dylion/faster-whisper-small-belarusian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bl4dylion/faster-whisper-small-belarusian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bl4dylion/faster-whisper-small-belarusian")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bl4dylion/faster-whisper-small-belarusian", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Whisper small model for CTranslate2
This repository contains the conversion of ales/whisper-small-belarusian to the CTranslate2 model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper.
Install faster-whisper
pip install git+https://github.com/guillaumekln/faster-whisper.git
Example
from faster_whisper import WhisperModel
model = WhisperModel("bl4dylion/faster-whisper-small-belarusian")
segments, info = model.transcribe("audio.mp3")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
Conversion details
The original model was converted with the following command:
ct2-transformers-converter --model ales/whisper-small-belarusian --output_dir faster-whisper-small-belarusian \
--copy_files tokenizer_config.json --quantization float16
Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the compute_type option in CTranslate2.
More information
For more information about the original model, see its model card.
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