mirror of https://github.com/coqui-ai/TTS.git
348 lines
16 KiB
Python
348 lines
16 KiB
Python
import os
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import glob
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import torch
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import shutil
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import datetime
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import subprocess
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import importlib
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import numpy as np
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def get_git_branch():
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try:
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out = subprocess.check_output(["git", "branch"]).decode("utf8")
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current = next(line for line in out.split("\n")
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if line.startswith("*"))
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current.replace("* ", "")
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except subprocess.CalledProcessError:
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current = "inside_docker"
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return current
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def get_commit_hash():
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"""https://stackoverflow.com/questions/14989858/get-the-current-git-hash-in-a-python-script"""
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# try:
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# subprocess.check_output(['git', 'diff-index', '--quiet',
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# 'HEAD']) # Verify client is clean
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# except:
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# raise RuntimeError(
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# " !! Commit before training to get the commit hash.")
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try:
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commit = subprocess.check_output(
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['git', 'rev-parse', '--short', 'HEAD']).decode().strip()
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# Not copying .git folder into docker container
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except subprocess.CalledProcessError:
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commit = "0000000"
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print(' > Git Hash: {}'.format(commit))
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return commit
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def create_experiment_folder(root_path, model_name, debug):
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""" Create a folder with the current date and time """
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date_str = datetime.datetime.now().strftime("%B-%d-%Y_%I+%M%p")
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# if debug:
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# commit_hash = 'debug'
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# else:
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commit_hash = get_commit_hash()
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output_folder = os.path.join(
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root_path, model_name + '-' + date_str + '-' + commit_hash)
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os.makedirs(output_folder, exist_ok=True)
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print(" > Experiment folder: {}".format(output_folder))
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return output_folder
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def remove_experiment_folder(experiment_path):
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"""Check folder if there is a checkpoint, otherwise remove the folder"""
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checkpoint_files = glob.glob(experiment_path + "/*.pth.tar")
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if not checkpoint_files:
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if os.path.exists(experiment_path):
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shutil.rmtree(experiment_path, ignore_errors=True)
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print(" ! Run is removed from {}".format(experiment_path))
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else:
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print(" ! Run is kept in {}".format(experiment_path))
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def count_parameters(model):
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r"""Count number of trainable parameters in a network"""
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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def split_dataset(items):
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is_multi_speaker = False
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speakers = [item[-1] for item in items]
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is_multi_speaker = len(set(speakers)) > 1
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eval_split_size = 500 if len(items) * 0.01 > 500 else int(
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len(items) * 0.01)
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np.random.seed(0)
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np.random.shuffle(items)
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if is_multi_speaker:
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items_eval = []
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# most stupid code ever -- Fix it !
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while len(items_eval) < eval_split_size:
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speakers = [item[-1] for item in items]
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speaker_counter = Counter(speakers)
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item_idx = np.random.randint(0, len(items))
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if speaker_counter[items[item_idx][-1]] > 1:
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items_eval.append(items[item_idx])
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del items[item_idx]
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return items_eval, items
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else:
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return items[:eval_split_size], items[eval_split_size:]
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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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batch_size = sequence_length.size(0)
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seq_range = torch.arange(0, max_len).long()
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seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
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if sequence_length.is_cuda:
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seq_range_expand = seq_range_expand.cuda()
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seq_length_expand = (
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sequence_length.unsqueeze(1).expand_as(seq_range_expand))
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# B x T_max
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return seq_range_expand < seq_length_expand
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def set_init_dict(model_dict, checkpoint, c):
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# Partial initialization: if there is a mismatch with new and old layer, it is skipped.
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for k, v in checkpoint['model'].items():
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if k not in model_dict:
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print(" | > Layer missing in the model definition: {}".format(k))
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# 1. filter out unnecessary keys
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pretrained_dict = {
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k: v
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for k, v in checkpoint['model'].items() if k in model_dict
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}
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# 2. filter out different size layers
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pretrained_dict = {
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k: v
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for k, v in pretrained_dict.items()
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if v.numel() == model_dict[k].numel()
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}
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# 3. skip reinit layers
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if c.reinit_layers is not None:
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for reinit_layer_name in c.reinit_layers:
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pretrained_dict = {
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k: v
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for k, v in pretrained_dict.items()
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if reinit_layer_name not in k
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}
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# 4. overwrite entries in the existing state dict
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model_dict.update(pretrained_dict)
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print(" | > {} / {} layers are restored.".format(len(pretrained_dict),
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len(model_dict)))
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return model_dict
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def setup_model(num_chars, num_speakers, c):
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print(" > Using model: {}".format(c.model))
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MyModel = importlib.import_module('TTS.models.' + c.model.lower())
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MyModel = getattr(MyModel, c.model)
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if c.model.lower() in "tacotron":
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model = MyModel(num_chars=num_chars,
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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_freq'],
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decoder_output_dim=c.audio['num_mels'],
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gst=c.use_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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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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elif c.model.lower() == "tacotron2":
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model = MyModel(num_chars=num_chars,
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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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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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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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return model
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class KeepAverage():
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def __init__(self):
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self.avg_values = {}
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self.iters = {}
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def __getitem__(self, key):
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return self.avg_values[key]
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def items(self):
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return self.avg_values.items()
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def add_value(self, name, init_val=0, init_iter=0):
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self.avg_values[name] = init_val
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self.iters[name] = init_iter
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def update_value(self, name, value, weighted_avg=False):
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if weighted_avg:
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self.avg_values[name] = 0.99 * self.avg_values[name] + 0.01 * value
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self.iters[name] += 1
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else:
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self.avg_values[name] = self.avg_values[name] * \
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self.iters[name] + value
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self.iters[name] += 1
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self.avg_values[name] /= self.iters[name]
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def add_values(self, name_dict):
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for key, value in name_dict.items():
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self.add_value(key, init_val=value)
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def update_values(self, value_dict):
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for key, value in value_dict.items():
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self.update_value(key, value)
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def _check_argument(name, c, enum_list=None, max_val=None, min_val=None, restricted=False, val_type=None, alternative=None):
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if alternative in c.keys() and c[alternative] is not None:
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return
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if restricted:
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assert name in c.keys(), f' [!] {name} not defined in config.json'
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if name in c.keys():
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if max_val:
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assert c[name] <= max_val, f' [!] {name} is larger than max value {max_val}'
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if min_val:
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assert c[name] >= min_val, f' [!] {name} is smaller than min value {min_val}'
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if enum_list:
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assert c[name].lower() in enum_list, f' [!] {name} is not a valid value'
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if val_type:
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assert isinstance(c[name], val_type) or c[name] is None, f' [!] {name} has wrong type - {type(c[name])} vs {val_type}'
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def check_config(c):
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_check_argument('model', c, enum_list=['tacotron', 'tacotron2'], 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('num_freq', 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(), 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('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('loss_masking', c, restricted=True, 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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# 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=True, 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=True, val_type=bool)
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# tacotron prenet
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_check_argument('memory_size', c, restricted=True, val_type=int, min_val=-1)
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_check_argument('prenet_type', c, restricted=True, val_type=str, enum_list=['original', 'bn'])
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_check_argument('prenet_dropout', c, restricted=True, val_type=bool)
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# attention
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_check_argument('attention_type', c, restricted=True, val_type=str, enum_list=['graves', 'original'])
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_check_argument('attention_heads', c, restricted=True, val_type=int)
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_check_argument('attention_norm', c, restricted=True, val_type=str, enum_list=['sigmoid', 'softmax'])
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_check_argument('windowing', c, restricted=True, val_type=bool)
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_check_argument('use_forward_attn', c, restricted=True, val_type=bool)
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_check_argument('forward_attn_mask', c, restricted=True, val_type=bool)
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_check_argument('transition_agent', c, restricted=True, val_type=bool)
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_check_argument('transition_agent', c, restricted=True, val_type=bool)
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_check_argument('location_attn', c, restricted=True, val_type=bool)
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_check_argument('bidirectional_decoder', c, restricted=True, val_type=bool)
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# stopnet
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_check_argument('stopnet', c, restricted=True, val_type=bool)
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_check_argument('separate_stopnet', c, restricted=True, val_type=bool)
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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('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.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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# paths
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_check_argument('output_path', c, restricted=True, val_type=str)
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# multi-speaker gst
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_check_argument('use_speaker_embedding', c, restricted=True, val_type=bool)
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_check_argument('style_wav_for_test', c, restricted=True, val_type=str)
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_check_argument('use_gst', c, restricted=True, val_type=bool)
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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)
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_check_argument('meta_file_val', dataset_entry, restricted=True, val_type=str)
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