src/wavegrad/params.py

# 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 None:
      pass
    else:
      raise NotImplementedError
    return self


params = AttrDict(
    # Training params
    batch_size=32,
    learning_rate=2e-4,
    max_grad_norm=1.0,

    # Data params
    sample_rate=22050,
    hop_samples=300,  # Don't change this. Really.
    crop_mel_frames=24,

    # Model params
    noise_schedule=np.linspace(1e-6, 0.01, 1000).tolist(),
)