<jupyter_start><jupyter_text>read time wave from memory<jupyter_code>BASE_FOLDER = '../../'
%run -i ..\..\utility\feature_extractor\JupyterLoad_feature_extractor.py
fe_wav = memory_wave_file()
fe_wav.filepath = r'dataset\6dB\slider\id_02\normal\00000001.wav'
audio_file = os.path.abspath(BASE_FOLDER+fe_wav.filepath)
fe_wav.channel, fe_wav.srate  = librosa.load(audio_file, sr=None, mono=False)
fe = feature_extractor_welchPSD(BASE_FOLDER)
fe.create_from_wav( fe_wav, channel=0, multichannel='concat')
fe.plot()
plt.legend()
type(fe_wav) is str
fe = feature_extractor_mel(BASE_FOLDER)
fe.create_from_wav( fe_wav, channel=0, multichannel='concat')
fe.plot()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13994169096209913, 'fraction_numerical': 0.018950437317784258, 'mean_word_length': 7.5875, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 1, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '181131', 'license': 'MIT', 'n_tokens_mistral': 305, 'n_tokens_neox': 292, 'n_words': 37, 'path': 'utility/workshop/feature_extractor_load_from_memory_interface.ipynb', 'repo_name': 'BA-HanseML/NF_Prj_MIMII_Dataset', 'size': '686'}