hparams.py

import tensorflow as tf
import numpy as np

# NOTE: If you want full control for model architecture. please take a look
# at the code and change whatever you want. Some hyper parameters are hardcoded.

# Default hyperparameters:
hparams = tf.contrib.training.HParams(
    name="wavenet_vocoder",

    # Convenient model builder
    builder="wavenet",

    # Input type:
    # 1. raw [-1, 1]
    # 2. mulaw [-1, 1]
    # 3. mulaw-quantize [0, mu]
    # If input_type is raw or mulaw, network assumes scalar input and
    # discretized mixture of logistic distributions output, otherwise one-hot
    # input and softmax output are assumed.
    # **NOTE**: if you change the one of the two parameters below, you need to
    # re-run preprocessing before training.
    # **NOTE**: scaler input (raw or mulaw) is experimental. Use it your own risk.
    input_type="raw",
    output_type="Gaussian",#['Gaussian','MOG','MOL','softmax']
    quantize_channels=65536,  # 65536 or 256

    # Audio:
    sample_rate=22050,
    # this is only valid for mulaw is True
    silence_threshold=2,
    num_mels=80,
    fmin=125,
    fmax=7600,
    fft_size=1024,
    # shift can be specified by either hop_size or frame_shift_ms
    hop_size=256,
    frame_shift_ms=None,
    min_level_db=-100,
    ref_level_db=20,
    # whether to rescale waveform or not.
    # Let x is an input waveform, rescaled waveform y is given by:
    # y = x / np.abs(x).max() * rescaling_max
    rescaling=True,
    rescaling_max=0.999,
    # mel-spectrogram is normalized to [0, 1] for each utterance and clipping may
    # happen depends on min_level_db and ref_level_db, causing clipping noise.
    # If False, assertion is added to ensure no clipping happens.o0
    allow_clipping_in_normalization=True,

    # Mixture of logistic distributions:
    log_scale_min=float(np.log(1e-14)),

    # Model:
    # This should equal to `quantize_channels` if mu-law quantize enabled
    # otherwise num_mixture * 3 (pi, mean, log_scale)
    out_channels=2,
    use_skip=True,
    layers=24,
    stacks=4,
    residual_channels=512,
    gate_channels=512,  # split into 2 gropus internally for gated activation
    skip_out_channels=256,
    dropout=1 - 0.95,
    kernel_size=3,
    # If True, apply weight normalization as same as DeepVoice3
    weight_normalization=True,

    # Local conditioning (set negative value to disable))
    cin_channels=80,
    # If True, use transposed convolutions to upsample conditional features,
    # otherwise repeat features to adjust time resolution
    upsample_conditional_features=True,
    # should np.prod(upsample_scales) == hop_size
    upsample_scales=[4, 4, 4, 4],
    upsample_size=[[30,3],[40,3]],
    # Freq axis kernel size for upsampling network
    freq_axis_kernel_size=3,

    # Global conditioning (set negative value to disable)
    # currently limited for speaker embedding
    # this should only be enabled for multi-speaker dataset
    gin_channels=-1,  # i.e., speaker embedding dim
    n_speakers=7,  # 7 for CMU ARCTIC

    # Data loader
    pin_memory=True,
    num_workers=2,

    # train/test
    # test size can be specified as portion or num samples
    test_size=0.0441,  # 50 for CMU ARCTIC single speaker
    test_num_samples=None,
    random_state=1234,

    # Loss

    # Training:
    batch_size=2,
    adam_beta1=0.9,
    adam_beta2=0.999,
    adam_eps=1e-8,
    initial_learning_rate=1e-3,
    # see lrschedule.py for available lr_schedule
    lr_schedule="noam_learning_rate_decay",
    lr_schedule_kwargs={},  # {"anneal_rate": 0.5, "anneal_interval": 50000},
    nepochs=2000,
    weight_decay=0.0,
    clip_thresh=-1,
    # max time steps can either be specified as sec or steps
    # This is needed for those who don't have huge GPU memory...
    # if both are None, then full audio samples are used
    max_time_sec=None,
    max_time_steps=8000,
    # Hold moving averaged parameters and use them for evaluation
    exponential_moving_average=True,
    # averaged = decay * averaged + (1 - decay) * x
    ema_decay=0.9999,

    # Save
    # per-step intervals
    checkpoint_interval=10000,
    train_eval_interval=10000,
    # per-epoch interval
    test_eval_epoch_interval=5,
    save_optimizer_state=True,

    # Eval:

    # Student Model
    student_out_channels=2,
    student_layers=60,
    student_stacks=6,
    student_residual_channels=128,
    student_skip_channels=128,
    iaf_layer_sizes=[10, 10, 10, 10,10,10],
    student_gate_channels=128,
    use_scale=False,
    iaf_shift=False,
    share_condition_net=True
)


def hparams_debug_string():
    values = hparams.values()
    hp = ['  %s: %s' % (name, values[name]) for name in sorted(values)]
    return 'Hyperparameters:\n' + '\n'.join(hp)