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Sam Khoze
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import os
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import sys
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from collections import Counter
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from pathlib import Path
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from typing import Callable, Dict, List, Tuple, Union
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import numpy as np
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from TTS.tts.datasets.dataset import *
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from TTS.tts.datasets.formatters import *
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def split_dataset(items, eval_split_max_size=None, eval_split_size=0.01):
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"""Split a dataset into train and eval. Consider speaker distribution in multi-speaker training.
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Args:
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items (List[List]):
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A list of samples. Each sample is a list of `[audio_path, text, speaker_id]`.
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eval_split_max_size (int):
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Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
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eval_split_size (float):
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If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
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If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
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"""
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speakers = [item["speaker_name"] for item in items]
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is_multi_speaker = len(set(speakers)) > 1
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if eval_split_size > 1:
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eval_split_size = int(eval_split_size)
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else:
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if eval_split_max_size:
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eval_split_size = min(eval_split_max_size, int(len(items) * eval_split_size))
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else:
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eval_split_size = int(len(items) * eval_split_size)
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assert (
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eval_split_size > 0
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), " [!] You do not have enough samples for the evaluation set. You can work around this setting the 'eval_split_size' parameter to a minimum of {}".format(
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1 / len(items)
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)
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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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speakers = [item["speaker_name"] for item in items]
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speaker_counter = Counter(speakers)
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while len(items_eval) < eval_split_size:
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item_idx = np.random.randint(0, len(items))
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speaker_to_be_removed = items[item_idx]["speaker_name"]
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if speaker_counter[speaker_to_be_removed] > 1:
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items_eval.append(items[item_idx])
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speaker_counter[speaker_to_be_removed] -= 1
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del items[item_idx]
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return items_eval, items
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return items[:eval_split_size], items[eval_split_size:]
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def add_extra_keys(metadata, language, dataset_name):
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for item in metadata:
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# add language name
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item["language"] = language
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# add unique audio name
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relfilepath = os.path.splitext(os.path.relpath(item["audio_file"], item["root_path"]))[0]
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audio_unique_name = f"{dataset_name}#{relfilepath}"
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item["audio_unique_name"] = audio_unique_name
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return metadata
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def load_tts_samples(
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datasets: Union[List[Dict], Dict],
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eval_split=True,
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formatter: Callable = None,
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eval_split_max_size=None,
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eval_split_size=0.01,
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) -> Tuple[List[List], List[List]]:
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"""Parse the dataset from the datasets config, load the samples as a List and load the attention alignments if provided.
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If `formatter` is not None, apply the formatter to the samples else pick the formatter from the available ones based
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on the dataset name.
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Args:
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datasets (List[Dict], Dict): A list of datasets or a single dataset dictionary. If multiple datasets are
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in the list, they are all merged.
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eval_split (bool, optional): If true, create a evaluation split. If an eval split provided explicitly, generate
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an eval split automatically. Defaults to True.
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formatter (Callable, optional): The preprocessing function to be applied to create the list of samples. It
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must take the root_path and the meta_file name and return a list of samples in the format of
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`[[text, audio_path, speaker_id], ...]]`. See the available formatters in `TTS.tts.dataset.formatter` as
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example. Defaults to None.
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eval_split_max_size (int):
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Number maximum of samples to be used for evaluation in proportion split. Defaults to None (Disabled).
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eval_split_size (float):
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If between 0.0 and 1.0 represents the proportion of the dataset to include in the evaluation set.
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If > 1, represents the absolute number of evaluation samples. Defaults to 0.01 (1%).
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Returns:
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Tuple[List[List], List[List]: training and evaluation splits of the dataset.
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"""
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meta_data_train_all = []
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meta_data_eval_all = [] if eval_split else None
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if not isinstance(datasets, list):
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datasets = [datasets]
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for dataset in datasets:
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formatter_name = dataset["formatter"]
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dataset_name = dataset["dataset_name"]
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root_path = dataset["path"]
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meta_file_train = dataset["meta_file_train"]
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meta_file_val = dataset["meta_file_val"]
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ignored_speakers = dataset["ignored_speakers"]
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language = dataset["language"]
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# setup the right data processor
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if formatter is None:
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formatter = _get_formatter_by_name(formatter_name)
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# load train set
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meta_data_train = formatter(root_path, meta_file_train, ignored_speakers=ignored_speakers)
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assert len(meta_data_train) > 0, f" [!] No training samples found in {root_path}/{meta_file_train}"
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meta_data_train = add_extra_keys(meta_data_train, language, dataset_name)
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print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}")
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# load evaluation split if set
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if eval_split:
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if meta_file_val:
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meta_data_eval = formatter(root_path, meta_file_val, ignored_speakers=ignored_speakers)
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meta_data_eval = add_extra_keys(meta_data_eval, language, dataset_name)
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else:
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eval_size_per_dataset = eval_split_max_size // len(datasets) if eval_split_max_size else None
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meta_data_eval, meta_data_train = split_dataset(meta_data_train, eval_size_per_dataset, eval_split_size)
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meta_data_eval_all += meta_data_eval
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meta_data_train_all += meta_data_train
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# load attention masks for the duration predictor training
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if dataset.meta_file_attn_mask:
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meta_data = dict(load_attention_mask_meta_data(dataset["meta_file_attn_mask"]))
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for idx, ins in enumerate(meta_data_train_all):
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attn_file = meta_data[ins["audio_file"]].strip()
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meta_data_train_all[idx].update({"alignment_file": attn_file})
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if meta_data_eval_all:
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for idx, ins in enumerate(meta_data_eval_all):
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attn_file = meta_data[ins["audio_file"]].strip()
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meta_data_eval_all[idx].update({"alignment_file": attn_file})
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# set none for the next iter
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formatter = None
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return meta_data_train_all, meta_data_eval_all
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def load_attention_mask_meta_data(metafile_path):
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"""Load meta data file created by compute_attention_masks.py"""
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with open(metafile_path, "r", encoding="utf-8") as f:
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lines = f.readlines()
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meta_data = []
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for line in lines:
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wav_file, attn_file = line.split("|")
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meta_data.append([wav_file, attn_file])
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return meta_data
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def _get_formatter_by_name(name):
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"""Returns the respective preprocessing function."""
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thismodule = sys.modules[__name__]
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return getattr(thismodule, name.lower())
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def find_unique_chars(data_samples, verbose=True):
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texts = "".join(item[0] for item in data_samples)
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chars = set(texts)
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lower_chars = filter(lambda c: c.islower(), chars)
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chars_force_lower = [c.lower() for c in chars]
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chars_force_lower = set(chars_force_lower)
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if verbose:
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print(f" > Number of unique characters: {len(chars)}")
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print(f" > Unique characters: {''.join(sorted(chars))}")
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print(f" > Unique lower characters: {''.join(sorted(lower_chars))}")
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print(f" > Unique all forced to lower characters: {''.join(sorted(chars_force_lower))}")
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return chars_force_lower
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