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import path from 'path'
const name = 'samplernn'
const cwd = process.env.SAMPLERNN_CWD || path.join(process.env.HOME, 'code/' + name + '/')
const fetch = {
type: 'perl',
script: 'get.pl',
params: (task) => {
console.log(task)
return [ task.opt.url ]
},
listen: (task, line, i) => {
// here i need to bridge again... get the filename that comes back from youtube-dl
// and tell the cortex that URL -> fn and add the filename!
if ( line.match(/^youtube-dl got fn, /) ) {
let filename = line.split(' => ')[1].trim()
task.dataset = filename.split('.')[0]
task.opt.filename = filename
return { type: 'progress', action: 'resolve_dataset', task, }
}
return null
}
}
const train = {
type: 'pytorch',
script: 'train.py',
params: (task) => {
return [
'--exp', task.dataset.name,
'--dataset', task.dataset.name,
'--frame_sizes', '8', '2',
'--n_rnn', '2',
'--epoch_limit', task.epochs || 4,
'--sample_length', task.opt.sample_length || 44100 * 5,
'--n_samples', task.opt.n_samples || 6,
'--keep_old_checkpoints', task.opt.keep_old_checkpoints ? 'True' : 'False',
]
},
after: publish,
}
const generate = {
type: 'pytorch',
script: 'generate.py',
params: (task) => {
return [
'--exp', task.dataset,
'--dataset', task.dataset,
'--frame_sizes', '8', '2',
'--n_rnn', '2',
'--sample_length', task.opt.sample_length || 44100 * 5,
'--n_samples', task.opt.n_samples || 6,
'--keep_old_checkpoints', task.opt.keep_old_checkpoints ? 'True' : 'False',
]
},
after: publish,
}
const publish = {
type: 'perl',
script: 'latest.pl',
params: (task) => {
return ['-l', task.dataset]
}
}
const clear_cache = {
type: 'perl',
script: 'clear_cache.pl',
params: (task) => {
return ['-l', task.dataset]
}
}
const report = {
type: 'perl',
script: 'latest.pl',
params: ['-v'],
isScript: true,
}
export default {
name, cwd,
activities: {
fetch, train, generate, publish, clear_cache, report,
},
}
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