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hyperparams.py
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hyperparams.py
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from utils.hparams import HParams
hparams = HParams(
num_mels=80,
frame_length_ms=50,
frame_shift_ms=12.5,
hop_length=int(16000 * 0.0125), # samples.
win_length=int(16000 * 0.05), # samples.
max_db=100,
ref_db=20,
preemphasis=0.97,
max_abs_value=4.0,
symmetric_mel=True,
sr=16000,
n_fft=2048,
n_iter=60,
power=1.5,
max_generation_frames=1100,
max_eval_batches=20,
max_eval_sample_length=1000,
eval_sample_per_speaker=4,
vocab_size=6000,
embed_size=512,
encoder_hidden=512,
decoder_hidden=768,
n_encoder_layer=6,
n_decoder_layer=6,
n_attention_head=8,
transformer_dropout_rate=0.1,
decoder_dropout_rate=0.5,
prenet_hidden=256,
postnet_hidden=512,
n_postnet_layer=5,
data_format="nlti",
use_sos=True,
bucket_size=512,
shuffle_training_data=True,
batch_frame_limit=8000,
batch_frame_quad_limit=7000000,
balanced_training=True,
lg_prob_scale=0.2,
adapt_start_step=30000,
adapt_end_step=30000,
final_adapt_rate=0.25,
data_warmup_steps=30000,
target_length_lower_bound=240,
target_length_upper_bound=800,
reg_weight=5e-9,
multi_speaker=True,
max_num_speaker=1000,
speaker_embedding_size=128,
multi_lingual=True,
max_num_language=100,
language_net_hidden=128,
language_embedding_size=128,
warmup_steps=50000,
max_lr=1e-3,
min_lr=1e-5,
lr_decay_step=550000,
lr_decay_rate=1e-2,
adam_eps=5e-8,
external_embed_dim=1024,
use_external_embed=False,
)