mirror of https://github.com/coqui-ai/TTS.git
279 lines
16 KiB
Python
279 lines
16 KiB
Python
import torch
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from TTS.utils.generic_utils import find_module
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# from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1
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def sequence_mask(sequence_length, max_len=None):
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if max_len is None:
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max_len = sequence_length.data.max()
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seq_range = torch.arange(max_len, dtype=sequence_length.dtype, device=sequence_length.device)
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# B x T_max
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return seq_range.unsqueeze(0) < sequence_length.unsqueeze(1)
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def setup_model(num_chars, num_speakers, c, speaker_embedding_dim=None):
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print(" > Using model: {}".format(c.model))
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MyModel = find_module("TTS.tts.models", c.model.lower())
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if c.model.lower() in "tacotron":
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model = MyModel(
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num_chars=num_chars + getattr(c, "add_blank", False),
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num_speakers=num_speakers,
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r=c.r,
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postnet_output_dim=int(c.audio["fft_size"] / 2 + 1),
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decoder_output_dim=c.audio["num_mels"],
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use_gst=c.use_gst,
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gst=c.gst,
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memory_size=c.memory_size,
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attn_type=c.attention_type,
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attn_win=c.windowing,
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attn_norm=c.attention_norm,
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prenet_type=c.prenet_type,
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prenet_dropout=c.prenet_dropout,
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prenet_dropout_at_inference=c.prenet_dropout_at_inference,
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forward_attn=c.use_forward_attn,
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trans_agent=c.transition_agent,
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forward_attn_mask=c.forward_attn_mask,
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location_attn=c.location_attn,
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attn_K=c.attention_heads,
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separate_stopnet=c.separate_stopnet,
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bidirectional_decoder=c.bidirectional_decoder,
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double_decoder_consistency=c.double_decoder_consistency,
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ddc_r=c.ddc_r,
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speaker_embedding_dim=speaker_embedding_dim,
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)
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elif c.model.lower() == "tacotron2":
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model = MyModel(
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num_chars=num_chars + getattr(c, "add_blank", False),
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num_speakers=num_speakers,
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r=c.r,
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postnet_output_dim=c.audio["num_mels"],
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decoder_output_dim=c.audio["num_mels"],
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use_gst=c.use_gst,
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gst=c.gst,
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attn_type=c.attention_type,
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attn_win=c.windowing,
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attn_norm=c.attention_norm,
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prenet_type=c.prenet_type,
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prenet_dropout=c.prenet_dropout,
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prenet_dropout_at_inference=c.prenet_dropout_at_inference,
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forward_attn=c.use_forward_attn,
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trans_agent=c.transition_agent,
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forward_attn_mask=c.forward_attn_mask,
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location_attn=c.location_attn,
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attn_K=c.attention_heads,
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separate_stopnet=c.separate_stopnet,
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bidirectional_decoder=c.bidirectional_decoder,
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double_decoder_consistency=c.double_decoder_consistency,
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ddc_r=c.ddc_r,
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speaker_embedding_dim=speaker_embedding_dim,
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)
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elif c.model.lower() == "glow_tts":
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model = MyModel(
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num_chars=num_chars + getattr(c, "add_blank", False),
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hidden_channels_enc=c["hidden_channels_encoder"],
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hidden_channels_dec=c["hidden_channels_decoder"],
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hidden_channels_dp=c["hidden_channels_duration_predictor"],
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out_channels=c.audio["num_mels"],
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encoder_type=c.encoder_type,
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encoder_params=c.encoder_params,
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use_encoder_prenet=c["use_encoder_prenet"],
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inference_noise_scale=c.inference_noise_scale,
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num_flow_blocks_dec=12,
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kernel_size_dec=5,
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dilation_rate=1,
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num_block_layers=4,
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dropout_p_dec=0.05,
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num_speakers=num_speakers,
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c_in_channels=0,
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num_splits=4,
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num_squeeze=2,
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sigmoid_scale=False,
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mean_only=True,
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speaker_embedding_dim=speaker_embedding_dim,
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)
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elif c.model.lower() == "speedy_speech":
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model = MyModel(
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num_chars=num_chars + getattr(c, "add_blank", False),
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out_channels=c.audio["num_mels"],
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hidden_channels=c["hidden_channels"],
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positional_encoding=c["positional_encoding"],
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encoder_type=c["encoder_type"],
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encoder_params=c["encoder_params"],
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decoder_type=c["decoder_type"],
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decoder_params=c["decoder_params"],
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c_in_channels=0,
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)
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elif c.model.lower() == "align_tts":
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model = MyModel(
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num_chars=num_chars + getattr(c, "add_blank", False),
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out_channels=c.audio["num_mels"],
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hidden_channels=c["hidden_channels"],
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hidden_channels_dp=c["hidden_channels_dp"],
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encoder_type=c["encoder_type"],
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encoder_params=c["encoder_params"],
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decoder_type=c["decoder_type"],
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decoder_params=c["decoder_params"],
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c_in_channels=0,
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)
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return model
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def is_tacotron(c):
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return "tacotron" in c["model"].lower()
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# def check_config_tts(c):
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# check_argument('model', c, enum_list=['tacotron', 'tacotron2', 'glow_tts', 'speedy_speech', 'align_tts'], restricted=True, val_type=str)
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# check_argument('run_name', c, restricted=True, val_type=str)
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# check_argument('run_description', c, val_type=str)
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# # AUDIO
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# # check_argument('audio', c, restricted=True, val_type=dict)
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# # audio processing parameters
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# # check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056)
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# # check_argument('fft_size', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
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# # check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000)
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# # check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000, alternative='win_length')
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# # check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000, alternative='hop_length')
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# # check_argument('preemphasis', c['audio'], restricted=True, val_type=float, min_val=0, max_val=1)
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# # check_argument('min_level_db', c['audio'], restricted=True, val_type=int, min_val=-1000, max_val=10)
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# # check_argument('ref_level_db', c['audio'], restricted=True, val_type=int, min_val=0, max_val=1000)
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# # check_argument('power', c['audio'], restricted=True, val_type=float, min_val=1, max_val=5)
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# # check_argument('griffin_lim_iters', c['audio'], restricted=True, val_type=int, min_val=10, max_val=1000)
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# # vocabulary parameters
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# check_argument('characters', c, restricted=False, val_type=dict)
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# check_argument('pad', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
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# check_argument('eos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
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# check_argument('bos', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
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# check_argument('characters', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
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# check_argument('phonemes', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys() and c['use_phonemes'], val_type=str)
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# check_argument('punctuations', c['characters'] if 'characters' in c.keys() else {}, restricted='characters' in c.keys(), val_type=str)
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# # normalization parameters
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# # check_argument('signal_norm', c['audio'], restricted=True, val_type=bool)
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# # check_argument('symmetric_norm', c['audio'], restricted=True, val_type=bool)
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# # check_argument('max_norm', c['audio'], restricted=True, val_type=float, min_val=0.1, max_val=1000)
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# # check_argument('clip_norm', c['audio'], restricted=True, val_type=bool)
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# # check_argument('mel_fmin', c['audio'], restricted=True, val_type=float, min_val=0.0, max_val=1000)
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# # check_argument('mel_fmax', c['audio'], restricted=True, val_type=float, min_val=500.0)
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# # check_argument('spec_gain', c['audio'], restricted=True, val_type=[int, float], min_val=1, max_val=100)
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# # check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
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# # check_argument('trim_db', c['audio'], restricted=True, val_type=int)
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# # training parameters
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# # check_argument('batch_size', c, restricted=True, val_type=int, min_val=1)
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# # check_argument('eval_batch_size', c, restricted=True, val_type=int, min_val=1)
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# # check_argument('r', c, restricted=True, val_type=int, min_val=1)
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# # check_argument('gradual_training', c, restricted=False, val_type=list)
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# # check_argument('mixed_precision', c, restricted=False, val_type=bool)
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# # check_argument('grad_accum', c, restricted=True, val_type=int, min_val=1, max_val=100)
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# # loss parameters
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# # check_argument('loss_masking', c, restricted=True, val_type=bool)
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# # if c['model'].lower() in ['tacotron', 'tacotron2']:
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# # check_argument('decoder_loss_alpha', c, restricted=True, val_type=float, min_val=0)
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# # check_argument('postnet_loss_alpha', c, restricted=True, val_type=float, min_val=0)
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# # check_argument('postnet_diff_spec_alpha', c, restricted=True, val_type=float, min_val=0)
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# # check_argument('decoder_diff_spec_alpha', c, restricted=True, val_type=float, min_val=0)
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# # check_argument('decoder_ssim_alpha', c, restricted=True, val_type=float, min_val=0)
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# # check_argument('postnet_ssim_alpha', c, restricted=True, val_type=float, min_val=0)
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# # check_argument('ga_alpha', c, restricted=True, val_type=float, min_val=0)
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# if c['model'].lower in ["speedy_speech", "align_tts"]:
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# check_argument('ssim_alpha', c, restricted=True, val_type=float, min_val=0)
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# check_argument('l1_alpha', c, restricted=True, val_type=float, min_val=0)
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# check_argument('huber_alpha', c, restricted=True, val_type=float, min_val=0)
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# # validation parameters
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# # check_argument('run_eval', c, restricted=True, val_type=bool)
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# # check_argument('test_delay_epochs', c, restricted=True, val_type=int, min_val=0)
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# # check_argument('test_sentences_file', c, restricted=False, val_type=str)
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# # optimizer
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# check_argument('noam_schedule', c, restricted=False, val_type=bool)
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# check_argument('grad_clip', c, restricted=True, val_type=float, min_val=0.0)
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# check_argument('epochs', c, restricted=True, val_type=int, min_val=1)
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# check_argument('lr', c, restricted=True, val_type=float, min_val=0)
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# check_argument('wd', c, restricted=is_tacotron(c), val_type=float, min_val=0)
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# check_argument('warmup_steps', c, restricted=True, val_type=int, min_val=0)
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# check_argument('seq_len_norm', c, restricted=is_tacotron(c), val_type=bool)
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# # tacotron prenet
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# # check_argument('memory_size', c, restricted=is_tacotron(c), val_type=int, min_val=-1)
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# # check_argument('prenet_type', c, restricted=is_tacotron(c), val_type=str, enum_list=['original', 'bn'])
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# # check_argument('prenet_dropout', c, restricted=is_tacotron(c), val_type=bool)
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# # attention
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# check_argument('attention_type', c, restricted=is_tacotron(c), val_type=str, enum_list=['graves', 'original', 'dynamic_convolution'])
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# check_argument('attention_heads', c, restricted=is_tacotron(c), val_type=int)
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# check_argument('attention_norm', c, restricted=is_tacotron(c), val_type=str, enum_list=['sigmoid', 'softmax'])
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# check_argument('windowing', c, restricted=is_tacotron(c), val_type=bool)
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# check_argument('use_forward_attn', c, restricted=is_tacotron(c), val_type=bool)
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# check_argument('forward_attn_mask', c, restricted=is_tacotron(c), val_type=bool)
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# check_argument('transition_agent', c, restricted=is_tacotron(c), val_type=bool)
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# check_argument('transition_agent', c, restricted=is_tacotron(c), val_type=bool)
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# check_argument('location_attn', c, restricted=is_tacotron(c), val_type=bool)
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# check_argument('bidirectional_decoder', c, restricted=is_tacotron(c), val_type=bool)
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# check_argument('double_decoder_consistency', c, restricted=is_tacotron(c), val_type=bool)
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# check_argument('ddc_r', c, restricted='double_decoder_consistency' in c.keys(), min_val=1, max_val=7, val_type=int)
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# if c['model'].lower() in ['tacotron', 'tacotron2']:
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# # stopnet
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# # check_argument('stopnet', c, restricted=is_tacotron(c), val_type=bool)
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# # check_argument('separate_stopnet', c, restricted=is_tacotron(c), val_type=bool)
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# # Model Parameters for non-tacotron models
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# if c['model'].lower in ["speedy_speech", "align_tts"]:
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# check_argument('positional_encoding', c, restricted=True, val_type=type)
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# check_argument('encoder_type', c, restricted=True, val_type=str)
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# check_argument('encoder_params', c, restricted=True, val_type=dict)
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# check_argument('decoder_residual_conv_bn_params', c, restricted=True, val_type=dict)
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# # GlowTTS parameters
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# check_argument('encoder_type', c, restricted=not is_tacotron(c), val_type=str)
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# # tensorboard
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# # check_argument('print_step', c, restricted=True, val_type=int, min_val=1)
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# # check_argument('tb_plot_step', c, restricted=True, val_type=int, min_val=1)
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# # check_argument('save_step', c, restricted=True, val_type=int, min_val=1)
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# # check_argument('checkpoint', c, restricted=True, val_type=bool)
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# # check_argument('tb_model_param_stats', c, restricted=True, val_type=bool)
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# # dataloading
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# # pylint: disable=import-outside-toplevel
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# from TTS.tts.utils.text import cleaners
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# # check_argument('text_cleaner', c, restricted=True, val_type=str, enum_list=dir(cleaners))
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# # check_argument('enable_eos_bos_chars', c, restricted=True, val_type=bool)
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# # check_argument('num_loader_workers', c, restricted=True, val_type=int, min_val=0)
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# # check_argument('num_val_loader_workers', c, restricted=True, val_type=int, min_val=0)
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# # check_argument('batch_group_size', c, restricted=True, val_type=int, min_val=0)
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# # check_argument('min_seq_len', c, restricted=True, val_type=int, min_val=0)
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# # check_argument('max_seq_len', c, restricted=True, val_type=int, min_val=10)
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# # check_argument('compute_input_seq_cache', c, restricted=True, val_type=bool)
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# # paths
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# # check_argument('output_path', c, restricted=True, val_type=str)
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# # multi-speaker and gst
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# # check_argument('use_speaker_embedding', c, restricted=True, val_type=bool)
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# # check_argument('use_external_speaker_embedding_file', c, restricted=c['use_speaker_embedding'], val_type=bool)
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# # check_argument('external_speaker_embedding_file', c, restricted=c['use_external_speaker_embedding_file'], val_type=str)
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# if c['model'].lower() in ['tacotron', 'tacotron2'] and c['use_gst']:
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# # check_argument('use_gst', c, restricted=is_tacotron(c), val_type=bool)
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# # check_argument('gst', c, restricted=is_tacotron(c), val_type=dict)
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# # check_argument('gst_style_input', c['gst'], restricted=is_tacotron(c), val_type=[str, dict])
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# # check_argument('gst_embedding_dim', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=0, max_val=1000)
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# # check_argument('gst_use_speaker_embedding', c['gst'], restricted=is_tacotron(c), val_type=bool)
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# # check_argument('gst_num_heads', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=2, max_val=10)
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# # check_argument('gst_num_style_tokens', c['gst'], restricted=is_tacotron(c), val_type=int, min_val=1, max_val=1000)
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# # datasets - checking only the first entry
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# # check_argument('datasets', c, restricted=True, val_type=list)
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# # for dataset_entry in c['datasets']:
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# # check_argument('name', dataset_entry, restricted=True, val_type=str)
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# # check_argument('path', dataset_entry, restricted=True, val_type=str)
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# # check_argument('meta_file_train', dataset_entry, restricted=True, val_type=[str, list])
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# # check_argument('meta_file_val', dataset_entry, restricted=True, val_type=str)
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