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# MIT License
# Copyright (c) 2021 Pranay Manocha
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# code adapated from https://github.com/pranaymanocha/PerceptualAudio
import cdpam
import torch
class CDPAMEncoder(torch.nn.Module):
def __init__(self, cdpam_ckpt: str):
super().__init__()
# pre-trained model parameterss
encoder_layers = 16
encoder_filters = 64
input_size = 512
proj_ndim = [512, 256]
ndim = [16, 6]
classif_BN = 0
classif_act = "no"
proj_dp = 0.1
proj_BN = 1
classif_dp = 0.05
model = cdpam.models.FINnet(
encoder_layers=encoder_layers,
encoder_filters=encoder_filters,
ndim=ndim,
classif_dp=classif_dp,
classif_BN=classif_BN,
classif_act=classif_act,
input_size=input_size,
)
state = torch.load(cdpam_ckpt, map_location="cpu")["state"]
model.load_state_dict(state)
model.eval()
self.model = model
self.embed_dim = 512
def forward(self, x):
with torch.no_grad():
_, a1, c1 = self.model.base_encoder.forward(x)
a1 = torch.nn.functional.normalize(a1, dim=1)
return a1
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