wavernn_hparams.py


# CONFIG -----------------------------------------------------------------------------------------------------------#

# Here are the input and output data paths (Note: you can override wav_path in preprocess.py)
feature_path = './wavernn_training_data.txt'

# model ids are separate - that way you can use a new tts with an old wavernn and vice versa
# NB: expect undefined behaviour if models were trained on different DSP settings
voc_model_id = 'wavernn'

# set this to True if you are only interested in WaveRNN
ignore_tts = True 


# DSP --------------------------------------------------------------------------------------------------------------#

# Settings for all models
sample_rate = 22050
n_fft = 2048
fft_bins = n_fft // 2 + 1
num_mels = 80
hop_length = 275                 # 12.5ms - in line with Tacotron 2 paper
win_length = 1100                   # 50ms - same reason as above
fmin = 95
min_level_db = -100
ref_level_db = 20
bits = 10                            # bit depth of signal
mu_law = True                       # Recommended to suppress noise if using raw bits in hp.voc_mode below
peak_norm = True                    # Normalise to the peak of each wav file


# WAVERNN / VOCODER ------------------------------------------------------------------------------------------------#

# Model Hparams
voc_mode = 'RAW'                    # either 'RAW' (softmax on raw bits) or 'MOL' (sample from mixture of logistics)
voc_upsample_factors = (5, 5, 11)   # 5 * 5 * 11 = 275,  NB - this needs to correctly factorise hop_length
voc_rnn_dims = 512
voc_fc_dims = 512
voc_compute_dims = 128
voc_res_out_dims = 128
voc_res_blocks = 10

# Training
voc_batch_size = 32
voc_lr = 1e-4
voc_checkpoint_every = 1000
voc_gen_at_checkpoint = 5           # number of samples to generate at each checkpoint
voc_total_steps = 500_000         # Total number of training steps
voc_test_samples = 50               # How many unseen samples to put aside for testing
voc_pad = 2                         # this will pad the input so that the resnet can 'see' wider than input length
voc_seq_len = hop_length * 5        # must be a multiple of hop_length
voc_clip_grad_norm = 4              # set to None if no gradient clipping needed

# Generating / Synthesizing
voc_gen_batched = False             # very fast (realtime+) single utterance batched generation
voc_target = 11_000                 # target number of samples to be generated in each batch entry
voc_overlap = 550                   # number of samples for crossfading between batches