Metadata-Version: 2.1
Name: libyata
Version: 0.6.3
Summary: Yet Another Tools for Audio deep learning
Home-page: http://github.com/HudsonHuang/yata
Author: HudsonHuang
Author-email: 790209714@qq.com
License: MIT
Description: # yata[WIP]
        Yet Another Tools for Audio deep learning(for myself).
        ```
        pip install libyata
        ```
        ## Usage
        ### ```(NEW!)```As command line batch running tools
        - usage
          ```
          yata glob_pattern --command command_pattern --replace_from foo --replace_to bar --j NUM_PROCESSES --dry_run True/False
          ```
          args:
          - --command: set command pattern.  
            with placeholder of {i} as input file and {o} as output file
          - --j number of process to run
          - --dry_run: only show command ```to be run``` but not actual running it.  
            for example: "mv 1.wav 1_backup.wav"
        - examples
          - batch rename example:  
            On terminal, just type this to see guide:
            ```
            yata_guide rename
            ```
            you will get a guide:
            ```
            yata './**/*' --command 'mv {i} {o}' --replace_from .aac --replace_to .wav --j 4 --dry_run True
            ```
            You can run it directly and will show you the dry running command ```to be run```:
            ```
            yata date:20210418_161624, total_files:56, dry_run=True
            mv ./testdir/1.wav ./testdir/1.aac
            mv ./testdir/2i/2.wav ./testdir/2i/2.aac
            mv ./testdir_16k/1.wav ./testdir_16k/1.aac
            mv ./testdir_16k/2i/2.wav ./testdir_16k/2i/2.aac
            mv ./yata/61-70968-0002.wav ./yata/61-70968-0002.aac
            100%|██████████████████████████████████| 55/55 [00:00<00:00, 37724.73it/s]
            yata done:20210418_161705, total_files:56, dry_run=True
            ```
            You can modify it as your will and run it, remember to remove "--dry_run True":
            ```
            yata './**/*' --command   'mv {i} {o}' --replace_from .wav --replace_to _backup.wav --j 4
            ```
        
          - batch resample example:  
            Type
            ```
            yata_guide resample
            ```
            to get this guide:
            ```
            yata './**/*' --command 'ffmpeg -hide_banner -loglevel quiet  -i {i} -f wav -ar 16000 -acodec pcm_s16le -ac 1 {o}  -y' --replace_from .aac --replace_to .wav --j 10 --dry_run True
            ```
        - with pythonic support
          - tqdm
          - multiprocessing
          - auto new_dir
        ### As python package
        ```
        import yata
        ```
        - handy tools
            - yata.utils.run():  
              No more ArgumentParser!!   
              you can pass and update any parameter with:
              ```
              python test.py --a 2 --lr 0.01
              ```
              with code like:
              ```
              import yata
              
              default_hp = {"a":1,"b":2}
              args = yata.util.run(default_hp)
              ```
              you can acess params like HParams:
              ```
              print(args.a, args.b) # acess default params
              print(args.lr) # acess newly add params from CLI
              ```
            - yata.utils.new_dir:   
                Make directory like this `./file_a/tag/1/` with:
                ```
                new_dir("file_a", "tag", 1)
                ```
            - yata.utils.backup_code:  
                Backup all your \*.py(optional) to a zip file, eg. backup code for every experiments before running.
            - yata.utils.get_current_date: Get date as string
            
        
        - Tensorflow alternatives
            - yata.utils.HParams:   
              An alternative to tf.contrib.training.HParams without Tensorflow dependency
            - yata.utils.to_categorical:   
              An alternative to tf.keras.utils.to_categorical without Tensorflow & keras dependency
        - data augmentation
          - [x] mixup: [paper](https://arxiv.org/abs/1710.09412) [code](https://github.com/hongyi-zhang/mixup)
          - [x] SpecAugment: [paper](https://arxiv.org/abs/1904.08779) [code](https://github.com/DemisEom/SpecAugment)
          - [ ] mp3 as augumentation，用MP3编码后去掉的不可听噪声，把这种生成不可听噪声作为数据增强的手段（做法：加高斯，把MP3当成一个mask去编码，把MP3mask挖掉的区域的高斯留下来加到频谱上面，形成不可听噪声）
          - [ ] phase putertubation
        - feature extraction
          - PASE: [paper](https://arxiv.org/abs/2001.09239) [code](https://github.com/santi-pdp/pase)
          - Multi scale MelSpectrogram
        
Keywords: deep learning,audio processing,machine learning
Platform: UNKNOWN
Classifier: License :: OSI Approved :: ISC License (ISCL)
Classifier: Programming Language :: Python
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Multimedia :: Sound/Audio :: Analysis
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
