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274
mimic3-tts/mimic3_tts/config.py
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274
mimic3-tts/mimic3_tts/config.py
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"""Configuration classes"""
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# Copyright 2021 Mycroft AI Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import collections
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import json
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import typing
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from dataclasses import dataclass, field
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from enum import Enum
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from pathlib import Path
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from dataclasses_json import DataClassJsonMixin
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from gruut_ipa import IPA
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from phonemes2ids import BlankBetween
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@dataclass
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class AudioConfig(DataClassJsonMixin):
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filter_length: int = 1024
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hop_length: int = 256
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win_length: int = 1024
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mel_channels: int = 80
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sample_rate: int = 22050
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sample_bytes: int = 2
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channels: int = 1
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mel_fmin: float = 0.0
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mel_fmax: typing.Optional[float] = None
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ref_level_db: float = 20.0
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spec_gain: float = 1.0
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# Normalization
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signal_norm: bool = True
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min_level_db: float = -100.0
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max_norm: float = 1.0
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clip_norm: bool = True
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symmetric_norm: bool = True
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do_dynamic_range_compression: bool = True
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convert_db_to_amp: bool = True
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do_trim_silence: bool = False
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trim_silence_db: float = 40.0
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trim_margin_sec: float = 0.01
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trim_keep_sec: float = 0.25
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scale_mels: bool = False
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def __post_init__(self):
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if self.mel_fmax is not None:
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assert self.mel_fmax <= self.sample_rate // 2
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@dataclass
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class ModelConfig(DataClassJsonMixin):
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num_symbols: int = 0
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n_speakers: int = 1
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inter_channels: int = 192
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hidden_channels: int = 192
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filter_channels: int = 768
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n_heads: int = 2
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n_layers: int = 6
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kernel_size: int = 3
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p_dropout: float = 0.1
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resblock: str = "1"
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resblock_kernel_sizes: typing.Tuple[int, ...] = (3, 7, 11)
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resblock_dilation_sizes: typing.Tuple[typing.Tuple[int, ...], ...] = (
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(1, 3, 5),
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(1, 3, 5),
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(1, 3, 5),
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)
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upsample_rates: typing.Tuple[int, ...] = (8, 8, 2, 2)
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upsample_initial_channel: int = 512
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upsample_kernel_sizes: typing.Tuple[int, ...] = (16, 16, 4, 4)
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n_layers_q: int = 3
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use_spectral_norm: bool = False
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gin_channels: int = 256
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use_sdp: bool = True # StochasticDurationPredictor
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@property
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def is_multispeaker(self) -> bool:
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return self.n_speakers > 1
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@dataclass
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class PhonemesConfig(DataClassJsonMixin):
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phoneme_separator: str = " "
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"""Separator between individual phonemes in CSV input"""
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word_separator: str = "#"
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"""Separator between word phonemes in CSV input (must not match phoneme_separator)"""
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phoneme_to_id: typing.Optional[typing.Mapping[str, int]] = None
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pad: typing.Optional[str] = "_"
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bos: typing.Optional[str] = None
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eos: typing.Optional[str] = None
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blank: typing.Optional[str] = "#"
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blank_word: typing.Optional[str] = None
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blank_between: typing.Union[str, BlankBetween] = BlankBetween.WORDS
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blank_at_start: bool = True
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blank_at_end: bool = True
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simple_punctuation: bool = True
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punctuation_map: typing.Optional[typing.Mapping[str, str]] = None
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separate: typing.Optional[typing.List[str]] = None
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separate_graphemes: bool = False
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separate_tones: bool = False
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tone_before: bool = False
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phoneme_map: typing.Optional[typing.Mapping[str, str]] = None
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auto_bos_eos: bool = False
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minor_break: typing.Optional[str] = IPA.BREAK_MINOR.value
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major_break: typing.Optional[str] = IPA.BREAK_MAJOR.value
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def split_word_phonemes(self, phonemes_str: str) -> typing.List[typing.List[str]]:
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"""Split phonemes string into a list of lists (outer is words, inner is individual phonemes in each word)"""
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return [
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word_phonemes_str.split(self.phoneme_separator)
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for word_phonemes_str in phonemes_str.split(self.word_separator)
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]
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def join_word_phonemes(self, word_phonemes: typing.List[typing.List[str]]) -> str:
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"""Split phonemes string into a list of lists (outer is words, inner is individual phonemes in each word)"""
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return self.word_separator.join(
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self.phoneme_separator.join(wp) for wp in word_phonemes
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)
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class Phonemizer(str, Enum):
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SYMBOLS = "symbols"
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GRUUT = "gruut"
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ESPEAK = "espeak"
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class Aligner(str, Enum):
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KALDI_ALIGN = "kaldi_align"
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class TextCasing(str, Enum):
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LOWER = "lower"
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UPPER = "upper"
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class MetadataFormat(str, Enum):
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TEXT = "text"
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PHONEMES = "phonemes"
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PHONEME_IDS = "ids"
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@dataclass
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class DatasetConfig:
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name: str
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metadata_path: typing.Optional[typing.Union[str, Path]] = None
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train_path: typing.Optional[typing.Union[str, Path]] = None
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multispeaker: bool = False
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text_language: typing.Optional[str] = None
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audio_dir: typing.Optional[typing.Union[str, Path]] = None
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cache_dir: typing.Optional[typing.Union[str, Path]] = None
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def get_cache_dir(self, output_dir: typing.Union[str, Path]) -> Path:
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if self.cache_dir is not None:
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cache_dir = Path(self.cache_dir)
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else:
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cache_dir = Path("cache") / self.name
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if not cache_dir.is_absolute():
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cache_dir = Path(output_dir) / str(cache_dir)
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return cache_dir
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@dataclass
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class AlignerConfig:
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aligner: typing.Optional[Aligner] = None
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casing: typing.Optional[TextCasing] = None
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@dataclass
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class TrainingConfig(DataClassJsonMixin):
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seed: int = 1234
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epochs: int = 10000
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learning_rate: float = 2e-4
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betas: typing.Tuple[float, float] = field(default=(0.8, 0.99))
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eps: float = 1e-9
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batch_size: int = 32
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fp16_run: bool = False
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lr_decay: float = 0.999875
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segment_size: int = 8192
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init_lr_ratio: float = 1.0
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warmup_epochs: int = 0
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c_mel: int = 45
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c_kl: float = 1.0
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grad_clip: typing.Optional[float] = None
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min_seq_length: typing.Optional[int] = None
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max_seq_length: typing.Optional[int] = None
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min_spec_length: typing.Optional[int] = None
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max_spec_length: typing.Optional[int] = None
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last_epoch: int = 1
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global_step: int = 1
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best_loss: typing.Optional[float] = None
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audio: AudioConfig = field(default_factory=AudioConfig)
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model: ModelConfig = field(default_factory=ModelConfig)
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phonemes: PhonemesConfig = field(default_factory=PhonemesConfig)
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text_aligner: AlignerConfig = field(default_factory=AlignerConfig)
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text_language: typing.Optional[str] = None
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phonemizer: typing.Optional[Phonemizer] = None
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datasets: typing.List[DatasetConfig] = field(default_factory=list)
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dataset_format: MetadataFormat = MetadataFormat.TEXT
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version: int = 1
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git_commit: str = ""
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@property
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def is_multispeaker(self):
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return (
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self.model.is_multispeaker
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or any(d.multispeaker for d in self.datasets)
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)
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def save(self, config_file: typing.TextIO):
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"""Save config as JSON to a file"""
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json.dump(self.to_dict(), config_file, indent=4)
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@staticmethod
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def load(config_file: typing.TextIO) -> "TrainingConfig":
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"""Load config from a JSON file"""
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return TrainingConfig.from_json(config_file.read())
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@staticmethod
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def load_and_merge(
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config: "TrainingConfig",
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config_files: typing.Iterable[typing.Union[str, Path, typing.TextIO]],
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) -> "TrainingConfig":
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"""Loads one or more JSON configuration files and overlays them on top of an existing config"""
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base_dict = config.to_dict()
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for maybe_config_file in config_files:
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if isinstance(maybe_config_file, (str, Path)):
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# File path
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config_file = open(maybe_config_file, "r", encoding="utf-8")
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else:
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# File object
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config_file = maybe_config_file
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with config_file:
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# Load new config and overlay on existing config
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new_dict = json.load(config_file)
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TrainingConfig.recursive_update(base_dict, new_dict)
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return TrainingConfig.from_dict(base_dict)
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@staticmethod
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def recursive_update(
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base_dict: typing.Dict[typing.Any, typing.Any],
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new_dict: typing.Mapping[typing.Any, typing.Any],
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) -> None:
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"""Recursively overwrites values in base dictionary with values from new dictionary"""
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for key, value in new_dict.items():
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if isinstance(value, collections.Mapping) and (
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base_dict.get(key) is not None
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):
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TrainingConfig.recursive_update(base_dict[key], value)
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else:
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base_dict[key] = value
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