/
params.py
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/
params.py
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# Copyright 2020 LMNT, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import numpy as np
class AttrDict(dict):
def __init__(self, *args, **kwargs):
super(AttrDict, self).__init__(*args, **kwargs)
self.__dict__ = self
def override(self, attrs):
if isinstance(attrs, dict):
self.__dict__.update(**attrs)
elif isinstance(attrs, (list, tuple, set)):
for attr in attrs:
self.override(attr)
elif attrs is not None:
raise NotImplementedError
return self
params = AttrDict(
# Training params
batch_size=16,
learning_rate=2e-4,
max_grad_norm=None,
# Data params
sample_rate=22050,
n_mels=80,
n_fft=1024,
hop_samples=256,
crop_mel_frames=62, # Probably an error in paper.
# Model params
residual_layers=30,
residual_channels=64,
dilation_cycle_length=10,
unconditional = False,
noise_schedule=np.linspace(1e-4, 0.05, 50).tolist(),
inference_noise_schedule=[0.0001, 0.001, 0.01, 0.05, 0.2, 0.5],
# unconditional sample len
audio_len = 22050*5, # unconditional_synthesis_samples
)