def check_config_tts(c):
check_argument('model', c, enum_list=['tacotron', 'tacotron2', 'glow_tts', 'speedy_speech'], restricted=True, val_type=str)
check_argument('run_name', c, restricted=True, val_type=str)
check_argument('run_description', c, val_type=str)
# AUDIO
check_argument('audio', c, restricted=True, val_type=dict)
# audio processing parameters
check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056)
check_argument('fft_size', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000)
check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000, alternative='win_length')
check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000, alternative='hop_length')
check_argument('preemphasis', c['audio'], restricted=True, val_type=float, min_val=0, max_val=1)
check_argument('min_level_db', c['audio'], restricted=True, val_type=int, min_val=-1000, max_val=10)
check_argument('ref_level_db', c['audio'], restricted=True, val_type=int, min_val=0, max_val=1000)
check_argument('power', c['audio'], restricted=True, val_type=float, min_val=1, max_val=5)
check_argument('griffin_lim_iters', c['audio'], restricted=True, val_type=int, min_val=10, max_val=1000)
# vocabulary parameters
check_argument('characters', c, restricted=False, val_type=dict)
check_argument('pad', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
check_argument('eos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
check_argument('bos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
check_argument('characters', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
check_argument('phonemes', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
check_argument('punctuations', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
# normalization parameters
check_argument('signal_norm', c['audio'], restricted=True, val_type=bool)
check_argument('symmetric_norm', c['audio'], restricted=True, val_type=bool)
check_argument('max_norm', c['audio'], restricted=True, val_type=float, min_val=0.1, max_val=1000)
check_argument('clip_norm', c['audio'], restricted=True, val_type=bool)
check_argument('mel_fmin', c['audio'], restricted=True, val_type=float, min_val=0.0, max_val=1000)
check_argument('mel_fmax', c['audio'], restricted=True, val_type=float, min_val=500.0)
check_argument('spec_gain', c['audio'], restricted=True, val_type=[int, float], min_val=1, max_val=100)
check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
check_argument('trim_db', c['audio'], restricted=True, val_type=int)
# training parameters
check_argument('batch_size', c, restricted=True, val_type=int, min_val=1)
check_argument('eval_batch_size', c, restricted=True, val_type=int, min_val=1)
check_argument('r', c, restricted=True, val_type=int, min_val=1)
check_argument('gradual_training', c, restricted=False, val_type=list)
check_argument('mixed_precision', c, restricted=False, val_type=bool)
# check_argument('grad_accum', c, restricted=True, val_type=int, min_val=1, max_val=100)
# loss parameters
check_argument('loss_masking', c, restricted=True, val_type=bool)
if c['model'].lower() in ['tacotron', 'tacotron2']:
check_argument('decoder_loss_alpha', c, restricted=True, val_type=float, min_val=0)
check_argument('postnet_loss_alpha', c, restricted=True, val_type=float, min_val=0)
check_argument('postnet_diff_spec_alpha', c, restricted=True, val_type=float, min_val=0)
check_argument('decoder_diff_spec_alpha', c, restricted=True, val_type=float, min_val=0)
check_argument('decoder_ssim_alpha', c, restricted=True, val_type=float, min_val=0)
check_argument('postnet_ssim_alpha', c, restricted=True, val_type=float, min_val=0)
check_argument('ga_alpha', c, restricted=True, val_type=float, min_val=0)
if c['model'].lower == "speedy_speech":
check_argument('ssim_alpha', c, restricted=True, val_type=float, min_val=0)
check_argument('l1_alpha', c, restricted=True, val_type=float, min_val=0)
check_argument('huber_alpha', c, restricted=True, val_type=float, min_val=0)
# validation parameters
check_argument('run_eval', c, restricted=True, val_type=bool)
check_argument('test_delay_epochs', c, restricted=True, val_type=int, min_val=0)
check_argument('test_every_epochs', c, restricted=True, val_type=int, min_val=0)
check_argument('test_sentences_file', c, restricted=False, val_type=str)
# optimizer
check_argument('noam_schedule', c, restricted=False, val_type=bool)
check_argument('grad_clip', c, restricted=True, val_type=float, min_val=0.0)
check_argument('epochs', c, restricted=True, val_type=int, min_val=1)
check_argument('lr', c, restricted=True, val_type=float, min_val=0)
check_argument('wd', c, restricted=is_tacotron(c), val_type=float, min_val=0)
check_argument('warmup_steps', c, restricted=True, val_type=int, min_val=0)
check_argument('seq_len_norm', c, restricted=is_tacotron(c), val_type=bool)
# tacotron prenet
check_argument('memory_size', c, restricted=is_tacotron(c), val_type=int, min_val=-1)
check_argument('prenet_type', c, restricted=is_tacotron(c), val_type=str, enum_list=['original', 'bn'])
check_argument('prenet_dropout', c, restricted=is_tacotron(c), val_type=bool)
# attention
check_argument('attention_type', c, restricted=is_tacotron(c), val_type=str, enum_list=['graves', 'original', 'dynamic_convolution'])
check_argument('attention_heads', c, restricted=is_tacotron(c), val_type=int)
check_argument('attention_norm', c, restricted=is_tacotron(c), val_type=str, enum_list=['sigmoid', 'softmax'])
check_argument('windowing', c, restricted=is_tacotron(c), val_type=bool)
check_argument('use_forward_attn', c, restricted=is_tacotron(c), val_type=bool)
check_argument('forward_attn_mask', c, restricted=is_tacotron(c), val_type=bool)
check_argument('transition_agent', c, restricted=is_tacotron(c), val_type=bool)
check_argument('transition_agent', c, restricted=is_tacotron(c), val_type=bool)
check_argument('location_attn', c, restricted=is_tacotron(c), val_type=bool)
check_argument('bidirectional_decoder', c, restricted=is_tacotron(c), val_type=bool)
check_argument('double_decoder_consistency', c, restricted=is_tacotron(c), val_type=bool)
check_argument('ddc_r', c, restricted='double_decoder_consistency' in c.keys(), min_val=1, max_val=7, val_type=int)
if c['model'].lower() in ['tacotron', 'tacotron2']:
# stopnet
check_argument('stopnet', c, restricted=is_tacotron(c), val_type=bool)
check_argument('separate_stopnet', c, restricted=is_tacotron(c), val_type=bool)
# Model Parameters for non-tacotron models
if c['model'].lower == "speedy_speech":
check_argument('positional_encoding', c, restricted=True, val_type=type)
check_argument('encoder_type', c, restricted=True, val_type=str)
check_argument('encoder_params', c, restricted=True, val_type=dict)
check_argument('decoder_residual_conv_bn_params', c, restricted=True, val_type=dict)
# GlowTTS parameters
check_argument('encoder_type', c, restricted=not is_tacotron(c), val_type=str)
# tensorboard
check_argument('print_step', c, restricted=True, val_type=int, min_val=1)
check_argument('tb_plot_step', c, restricted=True, val_type=int, min_val=1)
check_argument('save_step', c, restricted=True, val_type=int, min_val=1)
check_argument('checkpoint', c, restricted=True, val_type=bool)
check_argument('tb_model_param_stats', c, restricted=True, val_type=bool)
# dataloading
# pylint: disable=import-outside-toplevel
from TTS.tts.utils.text import cleaners
check_argument('text_cleaner', c, restricted=True, val_type=str, enum_list=dir(cleaners))
check_argument('enable_eos_bos_chars', c, restricted=True, val_type=bool)
check_argument('num_loader_workers', c, restricted=True, val_type=int, min_val=0)
check_argument('num_val_loader_workers', c, restricted=True, val_type=int, min_val=0)
check_argument('batch_group_size', c, restricted=True, val_type=int, min_val=0)
check_argument('min_seq_len', c, restricted=True, val_type=int, min_val=0)
check_argument('max_seq_len', c, restricted=True, val_type=int, min_val=10)
check_argument('compute_input_seq_cache', c, restricted=True, val_type=bool)
# paths
check_argument('output_path', c, restricted=True, val_type=str)
# multi-speaker and gst
check_argument('use_speaker_embedding', c, restricted=True, val_type=bool)
check_argument('use_external_speaker_embedding_file', c, restricted=c['use_speaker_embedding'], val_type=bool)
check_argument('external_speaker_embedding_file', c, restricted=c['use_external_speaker_embedding_file'], val_type=str)
if c['model'].lower() in ['tacotron', 'tacotron2'] and c['use_gst']:
check_argument('use_gst', c, restricted=is_tacotron(c), val_type=bool)
check_argument('gst', c, restricted=is_tacotron(c), val_type=dict)
check_argument('gst_style_input', c['gst'], restricted=is_tacotron(c), val_type=[str, dict])
check_argument('gst_embedding_dim', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=0, max_val=1000)
check_argument('gst_use_speaker_embedding', c['gst'], restricted=is_tacotron(c), val_type=bool)
check_argument('gst_num_heads', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=2, max_val=10)
check_argument('gst_style_tokens', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=1, max_val=1000)
# datasets - checking only the first entry
check_argument('datasets', c, restricted=True, val_type=list)
for dataset_entry in c['datasets']:
check_argument('name', dataset_entry, restricted=True, val_type=str)
check_argument('path', dataset_entry, restricted=True, val_type=str)
check_argument('meta_file_train', dataset_entry, restricted=True, val_type=[str, list])
check_argument('meta_file_val', dataset_entry, restricted=True, val_type=str)
def check_config_tts(c):
check_argument(
"model",
c,
enum_list=[
"tacotron", "tacotron2", "glow_tts", "speedy_speech", "align_tts"
],
restricted=True,
val_type=str,
)
check_argument("run_name", c, restricted=True, val_type=str)
check_argument("run_description", c, val_type=str)
# AUDIO
check_argument("audio", c, restricted=True, val_type=dict)
# audio processing parameters
check_argument("num_mels",
c["audio"],
restricted=True,
val_type=int,
min_val=10,
max_val=2056)
check_argument("fft_size",
c["audio"],
restricted=True,
val_type=int,
min_val=128,
max_val=4058)
check_argument("sample_rate",
c["audio"],
restricted=True,
val_type=int,
min_val=512,
max_val=100000)
check_argument(
"frame_length_ms",
c["audio"],
restricted=True,
val_type=float,
min_val=10,
max_val=1000,
alternative="win_length",
)
check_argument("frame_shift_ms",
c["audio"],
restricted=True,
val_type=float,
min_val=1,
max_val=1000,
alternative="hop_length")
check_argument("preemphasis",
c["audio"],
restricted=True,
val_type=float,
min_val=0,
max_val=1)
check_argument("min_level_db",
c["audio"],
restricted=True,
val_type=int,
min_val=-1000,
max_val=10)
check_argument("ref_level_db",
c["audio"],
restricted=True,
val_type=int,
min_val=0,
max_val=1000)
check_argument("power",
c["audio"],
restricted=True,
val_type=float,
min_val=1,
max_val=5)
check_argument("griffin_lim_iters",
c["audio"],
restricted=True,
val_type=int,
min_val=10,
max_val=1000)
# vocabulary parameters
check_argument("characters", c, restricted=False, val_type=dict)
check_argument("pad",
c["characters"] if "characters" in c.keys() else {},
restricted="characters" in c.keys(),
val_type=str)
check_argument("eos",
c["characters"] if "characters" in c.keys() else {},
restricted="characters" in c.keys(),
val_type=str)
check_argument("bos",
c["characters"] if "characters" in c.keys() else {},
restricted="characters" in c.keys(),
val_type=str)
check_argument(
"characters",
c["characters"] if "characters" in c.keys() else {},
restricted="characters" in c.keys(),
val_type=str,
)
check_argument(
"phonemes",
c["characters"] if "characters" in c.keys() else {},
restricted="characters" in c.keys() and c["use_phonemes"],
val_type=str,
)
check_argument(
"punctuations",
c["characters"] if "characters" in c.keys() else {},
restricted="characters" in c.keys(),
val_type=str,
)
# normalization parameters
check_argument("signal_norm", c["audio"], restricted=True, val_type=bool)
check_argument("symmetric_norm",
c["audio"],
restricted=True,
val_type=bool)
check_argument("max_norm",
c["audio"],
restricted=True,
val_type=float,
min_val=0.1,
max_val=1000)
check_argument("clip_norm", c["audio"], restricted=True, val_type=bool)
check_argument("mel_fmin",
c["audio"],
restricted=True,
val_type=float,
min_val=0.0,
max_val=1000)
check_argument("mel_fmax",
c["audio"],
restricted=True,
val_type=float,
min_val=500.0)
check_argument("spec_gain",
c["audio"],
restricted=True,
val_type=[int, float],
min_val=1,
max_val=100)
check_argument("do_trim_silence",
c["audio"],
restricted=True,
val_type=bool)
check_argument("trim_db", c["audio"], restricted=True, val_type=int)
# training parameters
check_argument("batch_size", c, restricted=True, val_type=int, min_val=1)
check_argument("eval_batch_size",
c,
restricted=True,
val_type=int,
min_val=1)
check_argument("r", c, restricted=True, val_type=int, min_val=1)
check_argument("gradual_training", c, restricted=False, val_type=list)
check_argument("mixed_precision", c, restricted=False, val_type=bool)
# check_argument('grad_accum', c, restricted=True, val_type=int, min_val=1, max_val=100)
# loss parameters
check_argument("loss_masking", c, restricted=True, val_type=bool)
if c["model"].lower() in ["tacotron", "tacotron2"]:
check_argument("decoder_loss_alpha",
c,
restricted=True,
val_type=float,
min_val=0)
check_argument("postnet_loss_alpha",
c,
restricted=True,
val_type=float,
min_val=0)
check_argument("postnet_diff_spec_alpha",
c,
restricted=True,
val_type=float,
min_val=0)
check_argument("decoder_diff_spec_alpha",
c,
restricted=True,
val_type=float,
min_val=0)
check_argument("decoder_ssim_alpha",
c,
restricted=True,
val_type=float,
min_val=0)
check_argument("postnet_ssim_alpha",
c,
restricted=True,
val_type=float,
min_val=0)
check_argument("ga_alpha",
c,
restricted=True,
val_type=float,
min_val=0)
if c["model"].lower in ["speedy_speech", "align_tts"]:
check_argument("ssim_alpha",
c,
restricted=True,
val_type=float,
min_val=0)
check_argument("l1_alpha",
c,
restricted=True,
val_type=float,
min_val=0)
check_argument("huber_alpha",
c,
restricted=True,
val_type=float,
min_val=0)
# validation parameters
check_argument("run_eval", c, restricted=True, val_type=bool)
check_argument("test_delay_epochs",
c,
restricted=True,
val_type=int,
min_val=0)
check_argument("test_sentences_file", c, restricted=False, val_type=str)
# optimizer
check_argument("noam_schedule", c, restricted=False, val_type=bool)
check_argument("grad_clip",
c,
restricted=True,
val_type=float,
min_val=0.0)
check_argument("epochs", c, restricted=True, val_type=int, min_val=1)
check_argument("lr", c, restricted=True, val_type=float, min_val=0)
check_argument("wd",
c,
restricted=is_tacotron(c),
val_type=float,
min_val=0)
check_argument("warmup_steps", c, restricted=True, val_type=int, min_val=0)
check_argument("seq_len_norm", c, restricted=is_tacotron(c), val_type=bool)
# tacotron prenet
check_argument("memory_size",
c,
restricted=is_tacotron(c),
val_type=int,
min_val=-1)
check_argument("prenet_type",
c,
restricted=is_tacotron(c),
val_type=str,
enum_list=["original", "bn"])
check_argument("prenet_dropout",
c,
restricted=is_tacotron(c),
val_type=bool)
# attention
check_argument(
"attention_type",
c,
restricted=is_tacotron(c),
val_type=str,
enum_list=["graves", "original", "dynamic_convolution"],
)
check_argument("attention_heads",
c,
restricted=is_tacotron(c),
val_type=int)
check_argument("attention_norm",
c,
restricted=is_tacotron(c),
val_type=str,
enum_list=["sigmoid", "softmax"])
check_argument("windowing", c, restricted=is_tacotron(c), val_type=bool)
check_argument("use_forward_attn",
c,
restricted=is_tacotron(c),
val_type=bool)
check_argument("forward_attn_mask",
c,
restricted=is_tacotron(c),
val_type=bool)
check_argument("transition_agent",
c,
restricted=is_tacotron(c),
val_type=bool)
check_argument("transition_agent",
c,
restricted=is_tacotron(c),
val_type=bool)
check_argument("location_attn",
c,
restricted=is_tacotron(c),
val_type=bool)
check_argument("bidirectional_decoder",
c,
restricted=is_tacotron(c),
val_type=bool)
check_argument("double_decoder_consistency",
c,
restricted=is_tacotron(c),
val_type=bool)
check_argument("ddc_r",
c,
restricted="double_decoder_consistency" in c.keys(),
min_val=1,
max_val=7,
val_type=int)
if c["model"].lower() in ["tacotron", "tacotron2"]:
# stopnet
check_argument("stopnet", c, restricted=is_tacotron(c), val_type=bool)
check_argument("separate_stopnet",
c,
restricted=is_tacotron(c),
val_type=bool)
# Model Parameters for non-tacotron models
if c["model"].lower in ["speedy_speech", "align_tts"]:
check_argument("positional_encoding",
c,
restricted=True,
val_type=type)
check_argument("encoder_type", c, restricted=True, val_type=str)
check_argument("encoder_params", c, restricted=True, val_type=dict)
check_argument("decoder_residual_conv_bn_params",
c,
restricted=True,
val_type=dict)
# GlowTTS parameters
check_argument("encoder_type",
c,
restricted=not is_tacotron(c),
val_type=str)
# tensorboard
check_argument("print_step", c, restricted=True, val_type=int, min_val=1)
check_argument("tb_plot_step", c, restricted=True, val_type=int, min_val=1)
check_argument("save_step", c, restricted=True, val_type=int, min_val=1)
check_argument("checkpoint", c, restricted=True, val_type=bool)
check_argument("tb_model_param_stats", c, restricted=True, val_type=bool)
# dataloading
# pylint: disable=import-outside-toplevel
from TTS.tts.utils.text import cleaners
check_argument("text_cleaner",
c,
restricted=True,
val_type=str,
enum_list=dir(cleaners))
check_argument("enable_eos_bos_chars", c, restricted=True, val_type=bool)
check_argument("num_loader_workers",
c,
restricted=True,
val_type=int,
min_val=0)
check_argument("num_val_loader_workers",
c,
restricted=True,
val_type=int,
min_val=0)
check_argument("batch_group_size",
c,
restricted=True,
val_type=int,
min_val=0)
check_argument("min_seq_len", c, restricted=True, val_type=int, min_val=0)
check_argument("max_seq_len", c, restricted=True, val_type=int, min_val=10)
check_argument("compute_input_seq_cache",
c,
restricted=True,
val_type=bool)
# paths
check_argument("output_path", c, restricted=True, val_type=str)
# multi-speaker and gst
check_argument("use_speaker_embedding", c, restricted=True, val_type=bool)
check_argument("use_external_speaker_embedding_file",
c,
restricted=c["use_speaker_embedding"],
val_type=bool)
check_argument("external_speaker_embedding_file",
c,
restricted=c["use_external_speaker_embedding_file"],
val_type=str)
if c["model"].lower() in ["tacotron", "tacotron2"] and c["use_gst"]:
check_argument("use_gst", c, restricted=is_tacotron(c), val_type=bool)
check_argument("gst", c, restricted=is_tacotron(c), val_type=dict)
check_argument("gst_style_input",
c["gst"],
restricted=is_tacotron(c),
val_type=[str, dict])
check_argument("gst_embedding_dim",
c["gst"],
restricted=is_tacotron(c),
val_type=int,
min_val=0,
max_val=1000)
check_argument("gst_use_speaker_embedding",
c["gst"],
restricted=is_tacotron(c),
val_type=bool)
check_argument("gst_num_heads",
c["gst"],
restricted=is_tacotron(c),
val_type=int,
min_val=2,
max_val=10)
check_argument("gst_style_tokens",
c["gst"],
restricted=is_tacotron(c),
val_type=int,
min_val=1,
max_val=1000)
# datasets - checking only the first entry
check_argument("datasets", c, restricted=True, val_type=list)
for dataset_entry in c["datasets"]:
check_argument("name", dataset_entry, restricted=True, val_type=str)
check_argument("path", dataset_entry, restricted=True, val_type=str)
check_argument("meta_file_train",
dataset_entry,
restricted=True,
val_type=[str, list])
check_argument("meta_file_val",
dataset_entry,
restricted=True,
val_type=str)
def check_config_speaker_encoder(c):
"""Check the config.json file of the speaker encoder"""
check_argument("run_name", c, restricted=True, val_type=str)
check_argument("run_description", c, val_type=str)
# audio processing parameters
check_argument("audio", c, restricted=True, val_type=dict)
check_argument("num_mels",
c["audio"],
restricted=True,
val_type=int,
min_val=10,
max_val=2056)
check_argument("fft_size",
c["audio"],
restricted=True,
val_type=int,
min_val=128,
max_val=4058)
check_argument("sample_rate",
c["audio"],
restricted=True,
val_type=int,
min_val=512,
max_val=100000)
check_argument(
"frame_length_ms",
c["audio"],
restricted=True,
val_type=float,
min_val=10,
max_val=1000,
alternative="win_length",
)
check_argument("frame_shift_ms",
c["audio"],
restricted=True,
val_type=float,
min_val=1,
max_val=1000,
alternative="hop_length")
check_argument("preemphasis",
c["audio"],
restricted=True,
val_type=float,
min_val=0,
max_val=1)
check_argument("min_level_db",
c["audio"],
restricted=True,
val_type=int,
min_val=-1000,
max_val=10)
check_argument("ref_level_db",
c["audio"],
restricted=True,
val_type=int,
min_val=0,
max_val=1000)
check_argument("power",
c["audio"],
restricted=True,
val_type=float,
min_val=1,
max_val=5)
check_argument("griffin_lim_iters",
c["audio"],
restricted=True,
val_type=int,
min_val=10,
max_val=1000)
# training parameters
check_argument("loss",
c,
enum_list=["ge2e", "angleproto"],
restricted=True,
val_type=str)
check_argument("grad_clip", c, restricted=True, val_type=float)
check_argument("epochs", c, restricted=True, val_type=int, min_val=1)
check_argument("lr", c, restricted=True, val_type=float, min_val=0)
check_argument("lr_decay", c, restricted=True, val_type=bool)
check_argument("warmup_steps", c, restricted=True, val_type=int, min_val=0)
check_argument("tb_model_param_stats", c, restricted=True, val_type=bool)
check_argument("num_speakers_in_batch", c, restricted=True, val_type=int)
check_argument("num_loader_workers", c, restricted=True, val_type=int)
check_argument("wd",
c,
restricted=True,
val_type=float,
min_val=0.0,
max_val=1.0)
# checkpoint and output parameters
check_argument("steps_plot_stats", c, restricted=True, val_type=int)
check_argument("checkpoint", c, restricted=True, val_type=bool)
check_argument("save_step", c, restricted=True, val_type=int)
check_argument("print_step", c, restricted=True, val_type=int)
check_argument("output_path", c, restricted=True, val_type=str)
# model parameters
check_argument("model", c, restricted=True, val_type=dict)
check_argument("input_dim", c["model"], restricted=True, val_type=int)
check_argument("proj_dim", c["model"], restricted=True, val_type=int)
check_argument("lstm_dim", c["model"], restricted=True, val_type=int)
check_argument("num_lstm_layers",
c["model"],
restricted=True,
val_type=int)
check_argument("use_lstm_with_projection",
c["model"],
restricted=True,
val_type=bool)
# in-memory storage parameters
check_argument("storage", c, restricted=True, val_type=dict)
check_argument("sample_from_storage_p",
c["storage"],
restricted=True,
val_type=float,
min_val=0.0,
max_val=1.0)
check_argument("storage_size",
c["storage"],
restricted=True,
val_type=int,
min_val=1,
max_val=100)
check_argument("additive_noise",
c["storage"],
restricted=True,
val_type=float,
min_val=0.0,
max_val=1.0)
# datasets - checking only the first entry
check_argument("datasets", c, restricted=True, val_type=list)
for dataset_entry in c["datasets"]:
check_argument("name", dataset_entry, restricted=True, val_type=str)
check_argument("path", dataset_entry, restricted=True, val_type=str)
check_argument("meta_file_train",
dataset_entry,
restricted=True,
val_type=[str, list])
check_argument("meta_file_val",
dataset_entry,
restricted=True,
val_type=str)
def check_config_speaker_encoder(c):
"""Check the config.json file of the speaker encoder"""
check_argument('run_name', c, restricted=True, val_type=str)
check_argument('run_description', c, val_type=str)
# audio processing parameters
check_argument('audio', c, restricted=True, val_type=dict)
check_argument('num_mels',
c['audio'],
restricted=True,
val_type=int,
min_val=10,
max_val=2056)
check_argument('fft_size',
c['audio'],
restricted=True,
val_type=int,
min_val=128,
max_val=4058)
check_argument('sample_rate',
c['audio'],
restricted=True,
val_type=int,
min_val=512,
max_val=100000)
check_argument('frame_length_ms',
c['audio'],
restricted=True,
val_type=float,
min_val=10,
max_val=1000,
alternative='win_length')
check_argument('frame_shift_ms',
c['audio'],
restricted=True,
val_type=float,
min_val=1,
max_val=1000,
alternative='hop_length')
check_argument('preemphasis',
c['audio'],
restricted=True,
val_type=float,
min_val=0,
max_val=1)
check_argument('min_level_db',
c['audio'],
restricted=True,
val_type=int,
min_val=-1000,
max_val=10)
check_argument('ref_level_db',
c['audio'],
restricted=True,
val_type=int,
min_val=0,
max_val=1000)
check_argument('power',
c['audio'],
restricted=True,
val_type=float,
min_val=1,
max_val=5)
check_argument('griffin_lim_iters',
c['audio'],
restricted=True,
val_type=int,
min_val=10,
max_val=1000)
# training parameters
check_argument('loss',
c,
enum_list=['ge2e', 'angleproto'],
restricted=True,
val_type=str)
check_argument('grad_clip', c, restricted=True, val_type=float)
check_argument('epochs', c, restricted=True, val_type=int, min_val=1)
check_argument('lr', c, restricted=True, val_type=float, min_val=0)
check_argument('lr_decay', c, restricted=True, val_type=bool)
check_argument('warmup_steps', c, restricted=True, val_type=int, min_val=0)
check_argument('tb_model_param_stats', c, restricted=True, val_type=bool)
check_argument('num_speakers_in_batch', c, restricted=True, val_type=int)
check_argument('num_loader_workers', c, restricted=True, val_type=int)
check_argument('wd',
c,
restricted=True,
val_type=float,
min_val=0.0,
max_val=1.0)
# checkpoint and output parameters
check_argument('steps_plot_stats', c, restricted=True, val_type=int)
check_argument('checkpoint', c, restricted=True, val_type=bool)
check_argument('save_step', c, restricted=True, val_type=int)
check_argument('print_step', c, restricted=True, val_type=int)
check_argument('output_path', c, restricted=True, val_type=str)
# model parameters
check_argument('model', c, restricted=True, val_type=dict)
check_argument('input_dim', c['model'], restricted=True, val_type=int)
check_argument('proj_dim', c['model'], restricted=True, val_type=int)
check_argument('lstm_dim', c['model'], restricted=True, val_type=int)
check_argument('num_lstm_layers',
c['model'],
restricted=True,
val_type=int)
check_argument('use_lstm_with_projection',
c['model'],
restricted=True,
val_type=bool)
# in-memory storage parameters
check_argument('storage', c, restricted=True, val_type=dict)
check_argument('sample_from_storage_p',
c['storage'],
restricted=True,
val_type=float,
min_val=0.0,
max_val=1.0)
check_argument('storage_size',
c['storage'],
restricted=True,
val_type=int,
min_val=1,
max_val=100)
check_argument('additive_noise',
c['storage'],
restricted=True,
val_type=float,
min_val=0.0,
max_val=1.0)
# datasets - checking only the first entry
check_argument('datasets', c, restricted=True, val_type=list)
for dataset_entry in c['datasets']:
check_argument('name', dataset_entry, restricted=True, val_type=str)
check_argument('path', dataset_entry, restricted=True, val_type=str)
check_argument('meta_file_train',
dataset_entry,
restricted=True,
val_type=[str, list])
check_argument('meta_file_val',
dataset_entry,
restricted=True,
val_type=str)