hparams.py

# -*- coding: utf-8 -*-

import tensorflow as tf
import numpy as np

hparams = tf.contrib.training.HParams(
    name = "Tacotron-2",
    
    # tacotron hyper parameter
    
    cleaners = 'korean_cleaners',  # 'korean_cleaners'   or 'english_cleaners'
    
    
    skip_path_filter = False, # npz파일에서 불필요한 것을 거르는 작업을 할지 말지 결정. receptive_field 보다 짧은 data를 걸러야 하기 때문에 해 줘야 한다.
    use_lws = False,
    
    # Audio
    sample_rate = 24000,  # 
    
    # shift can be specified by either hop_size(우선) or frame_shift_ms
    hop_size = 300,             # frame_shift_ms = 12.5ms
    fft_size=2048,   # n_fft. 주로 1024로 되어있는데, tacotron에서 2048사용
    win_size = 1200,   # 50ms
    num_mels=80,

    #Spectrogram Pre-Emphasis (Lfilter: Reduce spectrogram noise and helps model certitude levels. Also allows for better G&L phase reconstruction)
    preemphasize = True, #whether to apply filter
    preemphasis = 0.97,
    min_level_db = -100,
    ref_level_db = 20,
    signal_normalization = True, #Whether to normalize mel spectrograms to some predefined range (following below parameters)
    allow_clipping_in_normalization = True, #Only relevant if mel_normalization = True
    symmetric_mels = True, #Whether to scale the data to be symmetric around 0. (Also multiplies the output range by 2, faster and cleaner convergence)
    max_abs_value = 4., #max absolute value of data. If symmetric, data will be [-max, max] else [0, max] (Must not be too big to avoid gradient explosion, not too small for fast convergence)
    
        
    rescaling=True,
    rescaling_max=0.999, 
    
    trim_silence = True, #Whether to clip silence in Audio (at beginning and end of audio only, not the middle)
    #M-AILABS (and other datasets) trim params (there parameters are usually correct for any data, but definitely must be tuned for specific speakers)
    trim_fft_size = 512, 
    trim_hop_size = 128,
    trim_top_db = 23,
    
    
    
    
    clip_mels_length = True, #For cases of OOM (Not really recommended, only use if facing unsolvable OOM errors, also consider clipping your samples to smaller chunks)   
    max_mel_frames = 1000,  #Only relevant when clip_mels_length = True, please only use after trying output_per_steps=3 and still getting OOM errors.
    
    

    l2_regularization_strength = 0,  # Coefficient in the L2 regularization.
    sample_size = 9000,              # Concatenate and cut audio samples to this many samples
    silence_threshold = 0,             # Volume threshold below which to trim the start and the end from the training set samples. e.g. 2

    
    filter_width = 3,
    gc_channels = 32,                  # global_condition_vector의 차원. 이것 지정함으로써, global conditioning을 모델에 반영하라는 의미가 된다.
    
    input_type="raw",    # 'mulaw-quantize', 'mulaw', 'raw',   mulaw, raw 2가지는 scalar input
    scalar_input = True,   # input_type과 맞아야 함.
    
    
    dilations = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512,
                  1, 2, 4, 8, 16, 32, 64, 128, 256, 512],
    residual_channels = 128,
    dilation_channels = 256,
    quantization_channels = 256,
    out_channels = 30,  # discretized_mix_logistic_loss를 적용하기 때문에, 3의 배수
    skip_channels = 128,
    use_biases = True,
    upsample_type = 'SubPixel',  # 'SubPixel', None   
    upsample_factor=[12,25],   # np.prod(upsample_factor) must equal to hop_size



    # wavenet training hp
    wavenet_batch_size = 2,            # 16--> OOM. wavenet은 batch_size가 고정되어야 한다.
    store_metadata = False,
    num_steps = 1000000,                # Number of training steps

    #Learning rate schedule
    wavenet_learning_rate = 1e-3, #wavenet initial learning rate
    wavenet_decay_rate = 0.5, #Only used with 'exponential' scheme. Defines the decay rate.
    wavenet_decay_steps = 300000, #Only used with 'exponential' scheme. Defines the decay steps.

    #Regularization parameters
    wavenet_clip_gradients = True, #Whether the clip the gradients during wavenet training.

    # residual 결과를 sum할 때, 
    legacy = True, #Whether to use legacy mode: Multiply all skip outputs but the first one with sqrt(0.5) (True for more early training stability, especially for large models)
    
    # residual block내에서  x = (x + residual) * np.sqrt(0.5)
    residual_legacy = True, #Whether to scale residual blocks outputs by a factor of sqrt(0.5) (True for input variance preservation early in training and better overall stability)


    wavenet_dropout = 0.05,
    
    optimizer = 'adam',
    momentum = 0.9,                   # 'Specify the momentum to be used by sgd or rmsprop optimizer. Ignored by the adam optimizer.
    max_checkpoints = 3,             # 'Maximum amount of checkpoints that will be kept alive. Default: '    


    ####################################
    ####################################
    ####################################
    # TACOTRON HYPERPARAMETERS
    
    # Training
    adam_beta1 = 0.9,
    adam_beta2 = 0.999,
    
    #Learning rate schedule
    tacotron_decay_learning_rate = True, #boolean, determines if the learning rate will follow an exponential decay
    tacotron_start_decay = 40000, #Step at which learning decay starts
    tacotron_decay_steps = 18000, #Determines the learning rate decay slope (UNDER TEST)
    tacotron_decay_rate = 0.5, #learning rate decay rate (UNDER TEST)
    tacotron_initial_learning_rate = 1e-3, #starting learning rate
    tacotron_final_learning_rate = 1e-4, #minimal learning rate
    
    
    
    
    initial_data_greedy = True,
    initial_phase_step = 8000,   # 여기서 지정한 step 이전에는 data_dirs의 각각의 디렉토리에 대하여 같은 수의 example을 만들고, 이후, weght 비듈에 따라 ... 즉, 아래의 'main_data_greedy_factor'의 영향을 받는다.
    main_data_greedy_factor = 0,
    main_data = [''],    # 이곳에 있는 directory 속에 있는 data는 가중치를 'main_data_greedy_factor' 만큼 더 준다.
    prioritize_loss = False,    
    

    # Model
    model_type = 'multi-speaker', # [single, multi-speaker]
    speaker_embedding_size  = 16, 

    embedding_size = 512,    # 'ᄀ', 'ᄂ', 'ᅡ' 에 대한 embedding dim
    dropout_prob = 0.5,

    reduction_factor = 2,  # reduction_factor가 적으면 더 많은 iteration이 필요하므로, 더 많은 메모리가 필요하다.
    
    # Encoder
    enc_conv_num_layers = 3,
    enc_conv_kernel_size = 5,
    enc_conv_channels = 512,
    tacotron_zoneout_rate = 0.1,
    encoder_lstm_units = 256,


    attention_type = 'bah_mon_norm',    # 'loc_sen', 'bah_mon_norm'
    attention_size = 128,

    #Attention mechanism
    smoothing = False, #Whether to smooth the attention normalization function
    attention_dim = 128, #dimension of attention space
    attention_filters = 32, #number of attention convolution filters
    attention_kernel = (31, ), #kernel size of attention convolution
    cumulative_weights = True, #Whether to cumulate (sum) all previous attention weights or simply feed previous weights (Recommended: True)

    #Attention synthesis constraints
    #"Monotonic" constraint forces the model to only look at the forwards attention_win_size steps.
    #"Window" allows the model to look at attention_win_size neighbors, both forward and backward steps.
    synthesis_constraint = False,  #Whether to use attention windows constraints in synthesis only (Useful for long utterances synthesis)
    synthesis_constraint_type = 'window', #can be in ('window', 'monotonic'). 
    attention_win_size = 7, #Side of the window. Current step does not count. If mode is window and attention_win_size is not pair, the 1 extra is provided to backward part of the window.

    #Loss params
    mask_encoder = True, #whether to mask encoder padding while computing location sensitive attention. Set to True for better prosody but slower convergence.


    #Decoder
    prenet_layers = [256, 256], #number of layers and number of units of prenet
    decoder_layers = 2, #number of decoder lstm layers
    decoder_lstm_units = 1024, #number of decoder lstm units on each layer

    dec_prenet_sizes = [256, 256], #number of layers and number of units of prenet



    #Residual postnet
    postnet_num_layers = 5, #number of postnet convolutional layers
    postnet_kernel_size = (5, ), #size of postnet convolution filters for each layer
    postnet_channels = 512, #number of postnet convolution filters for each layer



    # for linear mel spectrogrma
    post_bank_size = 8,
    post_bank_channel_size = 128,
    post_maxpool_width = 2,
    post_highway_depth = 4,
    post_rnn_size = 128,
    post_proj_sizes = [256, 80], # num_mels=80
    post_proj_width = 3,



    tacotron_reg_weight = 1e-6, #regularization weight (for L2 regularization)
    inference_prenet_dropout = True,


    # Eval
    min_tokens = 30,  #originally 50, 30 is good for korean,  text를 token으로 쪼갰을 때, 최소 길이 이상되어야 train에 사용
    min_n_frame = 30*5,  # min_n_frame = reduction_factor * min_iters, reduction_factor와 곱해서 min_n_frame을 설정한다.
    max_n_frame = 200*5,
    skip_inadequate = False,
 
    griffin_lim_iters = 60,
    power = 1.5, 
 
)

if hparams.use_lws:
    # Does not work if fft_size is not multiple of hop_size!!
    # sample size = 20480, hop_size=256=12.5ms. fft_size는 window_size를 결정하는데, 2048을 시간으로 환산하면 2048/20480 = 0.1초=100ms
    hparams.sample_rate = 20480  # 
    
    # shift can be specified by either hop_size(우선) or frame_shift_ms
    hparams.hop_size = 256             # frame_shift_ms = 12.5ms
    hparams.frame_shift_ms=None      # hop_size=  sample_rate *  frame_shift_ms / 1000
    hparams.fft_size=2048   # 주로 1024로 되어있는데, tacotron에서 2048사용==> output size = 1025
    hparams.win_size = None # 256x4 --> 50ms
    
  
    
else:
    # 미리 정의되 parameter들로 부터 consistant하게 정의해 준다.
    hparams.num_freq = int(hparams.fft_size/2 + 1)
    hparams.frame_shift_ms = hparams.hop_size * 1000.0/ hparams.sample_rate      # hop_size=  sample_rate *  frame_shift_ms / 1000
    hparams.frame_length_ms = hparams.win_size * 1000.0/ hparams.sample_rate 


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