Instructions to use bzikst/faster-whisper-large-v3-russian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bzikst/faster-whisper-large-v3-russian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="bzikst/faster-whisper-large-v3-russian")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("bzikst/faster-whisper-large-v3-russian", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from bzikst/faster-whisper-large-v3-russian: direct link, hf CLI and curl.
- Browser
- Download file 340 Bytes
-
https://huggingface.co/bzikst/faster-whisper-large-v3-russian/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://bzikst/faster-whisper-large-v3-russian/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/bzikst/faster-whisper-large-v3-russian/resolve/main/preprocessor_config.json
340 Bytes
| { | |
| "chunk_length": 30, | |
| "feature_extractor_type": "WhisperFeatureExtractor", | |
| "feature_size": 128, | |
| "hop_length": 160, | |
| "n_fft": 400, | |
| "n_samples": 480000, | |
| "nb_max_frames": 3000, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "processor_class": "WhisperProcessor", | |
| "return_attention_mask": false, | |
| "sampling_rate": 16000 | |
| } | |