文件列表:

audio_fidelity_test.py (541, 2022-07-05)
data/ (0, 2022-07-05)
data/feather_dot.wav (7735340, 2022-07-05)
dataset.py (1032, 2022-07-05)
generate.py (3180, 2022-07-05)
params.py (666, 2022-07-05)
preprocess.py (4740, 2022-07-05)
test_util.py (1479, 2022-07-05)
train.py (5516, 2022-07-05)
util.py (705, 2022-07-05)
wavenet/ (0, 2022-07-05)
wavenet/__init__.py (0, 2022-07-05)
wavenet/model.py (4255, 2022-07-05)
wavenet/model_test.py (4052, 2022-07-05)

# WaveNet for Music Generation. ## Preprocesing data. preprocess.py generates tfrecord from audio files. First create a directory and put audio files to generate training data from. Then pass that directory as the first argument to the script. Supported audio formats are "wav" and "mp3". ``` # Run python3 preprocess.py --help to see all options. # This creates tfrecord files in . python3 preprocess.py [options] ``` ## Training the model. train.py trains WaveNet using tfrecord data created by preprocess.py and saves the trained weights to disk. ``` # Run python3 train.py --help to see all the options. python3 train.py ``` ## Generating audio. generate.py generates audio iteratively by making the model predict the value of the next timestamp. It starts with an arbitrary timestamp 1 and make the model predict the next timestamp. Then it appends the output of the model to the input makind the model generate one timestamp at a time. By default the script generates an audio clip of 5 seconds. ``` # Run python3 train.py --help to see all the options. python3 generate.py ``` ## Running unit tests. Make sure these tests pass after modifying model.py or util.py. ``` pytest test_util.py python3 wavenet/model_test.py ```

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