Instructions to use Ar4ikov/W2VFSC3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ar4ikov/W2VFSC3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="Ar4ikov/W2VFSC3")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("Ar4ikov/W2VFSC3") model = AutoModelForAudioClassification.from_pretrained("Ar4ikov/W2VFSC3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from Ar4ikov/W2VFSC3: direct link, hf CLI and curl.
- Browser
- Download file 520 Bytes
-
https://huggingface.co/Ar4ikov/W2VFSC3/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://Ar4ikov/W2VFSC3/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/Ar4ikov/W2VFSC3/resolve/main/tokenizer_config.json
520 Bytes
| {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "<pad>", "do_lower_case": false, "word_delimiter_token": "|", "replace_word_delimiter_char": " ", "special_tokens_map_file": "/root/.cache/huggingface/transformers/7c871b97286fbe0a6a7a9b6b8b25d760694791d5409bec1a3bf00f86d7c80d4b.9d6cd81ef646692fb1c169a880161ea1cb95f49694f220aced9b704b457e51dd", "name_or_path": "jonatasgrosman/wav2vec2-large-xlsr-53-russian", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2Processor"} |