<jupyter_start><jupyter_text>HuggingFace Repository This tutorial is available as an IPython notebook at [malaya-speech/example/huggingface-repository](https://github.com/huseinzol05/malaya-speech/tree/master/example/huggingface-repository).     Starting Malaya-Speech 1.2.6, you can load Malaya-Speech models from https://huggingface.co/huseinzol05 to get better download speed, and by default Malaya-Speech will use HuggingFace as backend repository.<jupyter_code>import malaya_speech
malaya_speech.__version__<jupyter_output><empty_output><jupyter_text>Load model from HuggingFaceVery simple, simply pass `use_huggingface=True` in load model function parameter, for an example,```pythonmalaya_speech.stt.deep_ctc(model = 'hubert-conformer-tiny', use_huggingface = True)```Load from HuggingFace should be no different in term of accuracy and size.<jupyter_code>malaya_speech.stt.deep_ctc(model = 'hubert-conformer-tiny', use_huggingface = True)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09016393442622951, 'fraction_numerical': 0.007172131147540984, 'mean_word_length': 7.422413793103448, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '177467', 'license': 'MIT', 'n_tokens_mistral': 323, 'n_tokens_neox': 309, 'n_words': 72, 'path': 'example/huggingface-repository/huggingface-repository.ipynb', 'repo_name': 'techthiyanes/malaya-speech', 'size': '976'}