From c9e552707073658c3b9eba1b421121192c7627bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Eren=20G=C3=B6lge?= Date: Tue, 25 May 2021 14:38:54 +0200 Subject: [PATCH] remove `tts.generic_utils` as all the functions are moved to other files --- TTS/tts/utils/generic_utils.py | 278 --------------------------------- 1 file changed, 278 deletions(-) delete mode 100644 TTS/tts/utils/generic_utils.py diff --git a/TTS/tts/utils/generic_utils.py b/TTS/tts/utils/generic_utils.py deleted file mode 100644 index b0e53f33..00000000 --- a/TTS/tts/utils/generic_utils.py +++ /dev/null @@ -1,278 +0,0 @@ -import torch - -from TTS.utils.generic_utils import find_module - - -# from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1 -def sequence_mask(sequence_length, max_len=None): - if max_len is None: - max_len = sequence_length.data.max() - seq_range = torch.arange(max_len, dtype=sequence_length.dtype, device=sequence_length.device) - # B x T_max - return seq_range.unsqueeze(0) < sequence_length.unsqueeze(1) - - -def setup_model(num_chars, num_speakers, c, speaker_embedding_dim=None): - print(" > Using model: {}".format(c.model)) - MyModel = find_module("TTS.tts.models", c.model.lower()) - if c.model.lower() in "tacotron": - model = MyModel( - num_chars=num_chars + getattr(c, "add_blank", False), - num_speakers=num_speakers, - r=c.r, - postnet_output_dim=int(c.audio["fft_size"] / 2 + 1), - decoder_output_dim=c.audio["num_mels"], - use_gst=c.use_gst, - gst=c.gst, - memory_size=c.memory_size, - attn_type=c.attention_type, - attn_win=c.windowing, - attn_norm=c.attention_norm, - prenet_type=c.prenet_type, - prenet_dropout=c.prenet_dropout, - prenet_dropout_at_inference=c.prenet_dropout_at_inference, - forward_attn=c.use_forward_attn, - trans_agent=c.transition_agent, - forward_attn_mask=c.forward_attn_mask, - location_attn=c.location_attn, - attn_K=c.attention_heads, - separate_stopnet=c.separate_stopnet, - bidirectional_decoder=c.bidirectional_decoder, - double_decoder_consistency=c.double_decoder_consistency, - ddc_r=c.ddc_r, - speaker_embedding_dim=speaker_embedding_dim, - ) - elif c.model.lower() == "tacotron2": - model = MyModel( - num_chars=num_chars + getattr(c, "add_blank", False), - num_speakers=num_speakers, - r=c.r, - postnet_output_dim=c.audio["num_mels"], - decoder_output_dim=c.audio["num_mels"], - use_gst=c.use_gst, - gst=c.gst, - attn_type=c.attention_type, - attn_win=c.windowing, - attn_norm=c.attention_norm, - prenet_type=c.prenet_type, - prenet_dropout=c.prenet_dropout, - prenet_dropout_at_inference=c.prenet_dropout_at_inference, - forward_attn=c.use_forward_attn, - trans_agent=c.transition_agent, - forward_attn_mask=c.forward_attn_mask, - location_attn=c.location_attn, - attn_K=c.attention_heads, - separate_stopnet=c.separate_stopnet, - bidirectional_decoder=c.bidirectional_decoder, - double_decoder_consistency=c.double_decoder_consistency, - ddc_r=c.ddc_r, - speaker_embedding_dim=speaker_embedding_dim, - ) - elif c.model.lower() == "glow_tts": - model = MyModel( - num_chars=num_chars + getattr(c, "add_blank", False), - hidden_channels_enc=c["hidden_channels_encoder"], - hidden_channels_dec=c["hidden_channels_decoder"], - hidden_channels_dp=c["hidden_channels_duration_predictor"], - out_channels=c.audio["num_mels"], - encoder_type=c.encoder_type, - encoder_params=c.encoder_params, - use_encoder_prenet=c["use_encoder_prenet"], - inference_noise_scale=c.inference_noise_scale, - num_flow_blocks_dec=12, - kernel_size_dec=5, - dilation_rate=1, - num_block_layers=4, - dropout_p_dec=0.05, - num_speakers=num_speakers, - c_in_channels=0, - num_splits=4, - num_squeeze=2, - sigmoid_scale=False, - mean_only=True, - speaker_embedding_dim=speaker_embedding_dim, - ) - elif c.model.lower() == "speedy_speech": - model = MyModel( - num_chars=num_chars + getattr(c, "add_blank", False), - out_channels=c.audio["num_mels"], - hidden_channels=c["hidden_channels"], - positional_encoding=c["positional_encoding"], - encoder_type=c["encoder_type"], - encoder_params=c["encoder_params"], - decoder_type=c["decoder_type"], - decoder_params=c["decoder_params"], - c_in_channels=0, - ) - elif c.model.lower() == "align_tts": - model = MyModel( - num_chars=num_chars + getattr(c, "add_blank", False), - out_channels=c.audio["num_mels"], - hidden_channels=c["hidden_channels"], - hidden_channels_dp=c["hidden_channels_dp"], - encoder_type=c["encoder_type"], - encoder_params=c["encoder_params"], - decoder_type=c["decoder_type"], - decoder_params=c["decoder_params"], - c_in_channels=0, - ) - return model - - -def is_tacotron(c): - return "tacotron" in c["model"].lower() - - -# 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_num_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)