readme.py

import torch
import torch.nn as nn

class MyVolumeModel(nn.Module):

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        # do the neural net magic!
        x = x * 2

        return x

from audacitorch import WaveformToWaveformBase

class MyVolumeModelWrapper(WaveformToWaveformBase):
    
    def do_forward_pass(self, x: torch.Tensor) -> torch.Tensor:
        
        # do any preprocessing here! 
        # expect x to be a waveform tensor with shape (n_channels, n_samples)

        output = self.model(x)

        # do any postprocessing here!
        # the return value should be a multichannel waveform tensor with shape (n_channels, n_samples)
    
        return output

metadata = {
    'sample_rate': 48000, 
    'domain_tags': ['music', 'speech', 'environmental'],
    'short_description': 'Use me to boost volume by 3dB :).',
    'long_description':  'This description can be a max of 280 characters aaaaaaaaaaaaaaaaaaaa.',
    'tags': ['volume boost'],
    'labels': ['boosted'],
    'effect_type': 'waveform-to-waveform',
    'multichannel': False,
}

from pathlib import Path
from audacitorch.utils import save_model, validate_metadata, \
                              get_example_inputs, test_run

# create a root dir for our model
root = Path('booster-net')
root.mkdir(exist_ok=True, parents=True)

# get our model
model = MyVolumeModel()

# wrap it
wrapper = MyVolumeModelWrapper(model)

# serialize it using torch.jit.script, torch.jit.trace,
# or a combination of both. 

# option 1: torch.jit.script 
# using torch.jit.script is preferred for most cases, 
# but may require changing a lot of source code
serialized_model = torch.jit.script(wrapper)

# option 2: torch.jit.trace
# using torch.jit.trace is typically easier, but you
# need to be extra careful that your serialized model behaves 
# properly after tracing
example_inputs = get_example_inputs()
serialized_model = torch.jit.trace(wrapper, example_inputs[0], 
                                    check_inputs=example_inputs)

# take your model for a test run!
test_run(serialized_model)

# check that we created our metadata correctly
success, msg = validate_metadata(metadata)
assert success

# save!
save_model(serialized_model, metadata, root)