<jupyter_start><jupyter_text>**Mel GAN practice****train and generate****using  Speech Commands Zero through Nine (SC09) datasets****on google colaboratory** clone source files<jupyter_code>!git clone https://github.com/shun60s/Mel-GAN-clone<jupyter_output><empty_output><jupyter_text>change working directory<jupyter_code>%cd Mel-GAN-clone<jupyter_output><empty_output><jupyter_text>check file tree<jupyter_code>!ls -l<jupyter_output><empty_output><jupyter_text>get Speech Commands Zero through Nine (SC09) and unzip it<jupyter_code>!wget -P "./wavs" http://deepyeti.ucsd.edu/cdonahue/wavegan/data/sc09.tar.gz
!tar -xf "./wavs/sc09.tar.gz" -C ./wavs/<jupyter_output><empty_output><jupyter_text>check sc09 file tree<jupyter_code>!ls -l ./wavs/sc09<jupyter_output><empty_output><jupyter_text>To monitor training via tensorboard<jupyter_code>from __future__ import absolute_import, division, print_function, unicode_literals

try:
  # %tensorflow_version only exists in Colab.
  %tensorflow_version 1.x
except Exception:
  pass<jupyter_output><empty_output><jupyter_text>re-start after train ends, when tensorboard doesn't work.<jupyter_code># Load the TensorBoard notebook extension
%load_ext tensorboard
%tensorboard --logdir=./logs<jupyter_output><empty_output><jupyter_text>**train**<jupyter_code>!python train.py --save_path ./logs --data_path ./ --n_test_samples 10<jupyter_output><empty_output><jupyter_text>requirements<jupyter_code>!pip install pyyaml==5.1<jupyter_output><empty_output><jupyter_text>**Generate**MelVocoder using best_netG.ptusing valid_files.txt under --data_path<jupyter_code>!python generate_from_folder2.py --load_path ./logs --save_path ./wavout --data_path ./  --folder none<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12521638776687824, 'fraction_numerical': 0.01154068090017311, 'mean_word_length': 7.984455958549223, 'pattern_counts': {'":': 0, '<': 41, '<?xml version=': 0, '>': 41, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '10', 'dir': 'jupyter-structured-clean-dedup', 'id': '641059', 'license': 'MIT', 'n_tokens_mistral': 653, 'n_tokens_neox': 632, 'n_words': 105, 'path': 'MelGAN_colab_practice1_sc09.ipynb', 'repo_name': 'shun60s/Mel-GAN-clone', 'size': '1733'}