Instructions to use scasutt/wav2vec2-base_toy_train_data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scasutt/wav2vec2-base_toy_train_data with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="scasutt/wav2vec2-base_toy_train_data")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForCTC processor = AutoProcessor.from_pretrained("scasutt/wav2vec2-base_toy_train_data") model = AutoModelForCTC.from_pretrained("scasutt/wav2vec2-base_toy_train_data", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer_config.json from scasutt/wav2vec2-base_toy_train_data: direct link, hf CLI and curl.
- Browser
- Download file 257 Bytes
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https://huggingface.co/scasutt/wav2vec2-base_toy_train_data/resolve/main/tokenizer_config.json
- Command line
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hf download hf://scasutt/wav2vec2-base_toy_train_data/tokenizer_config.json
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curl -L -o tokenizer_config.json https://huggingface.co/scasutt/wav2vec2-base_toy_train_data/resolve/main/tokenizer_config.json
257 Bytes
| {"unk_token": "[UNK]", "bos_token": "<s>", "eos_token": "</s>", "pad_token": "[PAD]", "do_lower_case": false, "word_delimiter_token": "|", "replace_word_delimiter_char": " ", "tokenizer_class": "Wav2Vec2CTCTokenizer", "processor_class": "Wav2Vec2Processor"} |