56 lines
1.5 KiB
Markdown
56 lines
1.5 KiB
Markdown
# AudioEnhancer
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Takes cruddy conference call audio and upsamples it to HD Audio
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To read more about the research, visit https://crimata.com/AIPost
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## Usage
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Repository Structure
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* `./data`: the data and the metadata (you should not need to touch this folder)
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* `./main`: the model and its respective utilites
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* `./samples`: audio files and spectrograms generated from the neural net
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```
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cd ./main
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python 3 main.py -h
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```
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It will then ask you if you'd like to train or evaluate. To get help for each respectivly, just type:
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```
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python 3 main.py train -h
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python 3 main.py eval -h
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```
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Argument Reference | train
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```
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Arguments:
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-h, --help
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-i MODEL_ID an index you can add to querey the model later
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-c FROM_CKPT bool, begin training session from a checkpoint
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-k NEW_DATA bool, make new set of data
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-d DIM_SIZE size of each audio sample
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-x NUM_FILES number of files to train
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-e EPOCHS
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-b BATCH_SIZE
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-o CYCLE_LENGTH length of cycle in epochs (only for SGDR)
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-m MAX_LR max learning rate (only for SGDR)
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-n MIN_LR min learning rate (only for SGDR)
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```
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Argument Reference | eval
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```
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Arguments:
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-h, --help
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-i MODEL_ID model ID to use
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-n NUM_EXAMPLES number of examples/files to run
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-w WAVFILE_LIST list to pull examples from
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-r SCALE level of upsampling
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-s SAMPLE_RATE target sample rate
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-a MAKE_AUDIO bool, make audio or just spectrograms
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-c FROM_CKPT bool, begin eval at last checkpoint
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```
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