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"], gst=c.use_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"], 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.get("inference_noise_scale", 0.33), 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)