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Sam Khoze
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import json
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import os
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import re
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from typing import Dict
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import fsspec
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import yaml
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from coqpit import Coqpit
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from TTS.config.shared_configs import *
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from TTS.utils.generic_utils import find_module
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def read_json_with_comments(json_path):
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"""for backward compat."""
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# fallback to json
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with fsspec.open(json_path, "r", encoding="utf-8") as f:
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input_str = f.read()
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# handle comments but not urls with //
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input_str = re.sub(r"(\"(?:[^\"\\]|\\.)*\")|(/\*(?:.|[\\n\\r])*?\*/)|(//.*)", lambda m: m.group(1) or m.group(2) or "", input_str)
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return json.loads(input_str)
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def register_config(model_name: str) -> Coqpit:
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"""Find the right config for the given model name.
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Args:
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model_name (str): Model name.
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Raises:
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ModuleNotFoundError: No matching config for the model name.
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Returns:
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Coqpit: config class.
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"""
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config_class = None
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config_name = model_name + "_config"
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# TODO: fix this
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if model_name == "xtts":
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from TTS.tts.configs.xtts_config import XttsConfig
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config_class = XttsConfig
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paths = ["TTS.tts.configs", "TTS.vocoder.configs", "TTS.encoder.configs", "TTS.vc.configs"]
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for path in paths:
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try:
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config_class = find_module(path, config_name)
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except ModuleNotFoundError:
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pass
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if config_class is None:
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raise ModuleNotFoundError(f" [!] Config for {model_name} cannot be found.")
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return config_class
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def _process_model_name(config_dict: Dict) -> str:
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"""Format the model name as expected. It is a band-aid for the old `vocoder` model names.
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Args:
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config_dict (Dict): A dictionary including the config fields.
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Returns:
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str: Formatted modelname.
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"""
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model_name = config_dict["model"] if "model" in config_dict else config_dict["generator_model"]
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model_name = model_name.replace("_generator", "").replace("_discriminator", "")
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return model_name
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def load_config(config_path: str) -> Coqpit:
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"""Import `json` or `yaml` files as TTS configs. First, load the input file as a `dict` and check the model name
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to find the corresponding Config class. Then initialize the Config.
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Args:
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config_path (str): path to the config file.
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Raises:
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TypeError: given config file has an unknown type.
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Returns:
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Coqpit: TTS config object.
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"""
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config_dict = {}
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ext = os.path.splitext(config_path)[1]
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if ext in (".yml", ".yaml"):
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with fsspec.open(config_path, "r", encoding="utf-8") as f:
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data = yaml.safe_load(f)
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elif ext == ".json":
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try:
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with fsspec.open(config_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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except json.decoder.JSONDecodeError:
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# backwards compat.
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data = read_json_with_comments(config_path)
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else:
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raise TypeError(f" [!] Unknown config file type {ext}")
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config_dict.update(data)
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model_name = _process_model_name(config_dict)
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config_class = register_config(model_name.lower())
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config = config_class()
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config.from_dict(config_dict)
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return config
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def check_config_and_model_args(config, arg_name, value):
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"""Check the give argument in `config.model_args` if exist or in `config` for
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the given value.
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Return False if the argument does not exist in `config.model_args` or `config`.
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This is to patch up the compatibility between models with and without `model_args`.
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TODO: Remove this in the future with a unified approach.
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"""
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if hasattr(config, "model_args"):
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if arg_name in config.model_args:
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return config.model_args[arg_name] == value
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if hasattr(config, arg_name):
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return config[arg_name] == value
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return False
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def get_from_config_or_model_args(config, arg_name):
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"""Get the given argument from `config.model_args` if exist or in `config`."""
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if hasattr(config, "model_args"):
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if arg_name in config.model_args:
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return config.model_args[arg_name]
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return config[arg_name]
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def get_from_config_or_model_args_with_default(config, arg_name, def_val):
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"""Get the given argument from `config.model_args` if exist or in `config`."""
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if hasattr(config, "model_args"):
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if arg_name in config.model_args:
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return config.model_args[arg_name]
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if hasattr(config, arg_name):
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return config[arg_name]
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return def_val
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from dataclasses import asdict, dataclass
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from typing import List
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from coqpit import Coqpit, check_argument
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from trainer import TrainerConfig
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@dataclass
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class BaseAudioConfig(Coqpit):
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"""Base config to definge audio processing parameters. It is used to initialize
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```TTS.utils.audio.AudioProcessor.```
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Args:
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fft_size (int):
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Number of STFT frequency levels aka.size of the linear spectogram frame. Defaults to 1024.
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win_length (int):
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Each frame of audio is windowed by window of length ```win_length``` and then padded with zeros to match
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```fft_size```. Defaults to 1024.
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hop_length (int):
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Number of audio samples between adjacent STFT columns. Defaults to 1024.
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frame_shift_ms (int):
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Set ```hop_length``` based on milliseconds and sampling rate.
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frame_length_ms (int):
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Set ```win_length``` based on milliseconds and sampling rate.
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stft_pad_mode (str):
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Padding method used in STFT. 'reflect' or 'center'. Defaults to 'reflect'.
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sample_rate (int):
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Audio sampling rate. Defaults to 22050.
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resample (bool):
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Enable / Disable resampling audio to ```sample_rate```. Defaults to ```False```.
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preemphasis (float):
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Preemphasis coefficient. Defaults to 0.0.
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ref_level_db (int): 20
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Reference Db level to rebase the audio signal and ignore the level below. 20Db is assumed the sound of air.
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Defaults to 20.
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do_sound_norm (bool):
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Enable / Disable sound normalization to reconcile the volume differences among samples. Defaults to False.
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log_func (str):
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Numpy log function used for amplitude to DB conversion. Defaults to 'np.log10'.
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do_trim_silence (bool):
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Enable / Disable trimming silences at the beginning and the end of the audio clip. Defaults to ```True```.
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do_amp_to_db_linear (bool, optional):
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enable/disable amplitude to dB conversion of linear spectrograms. Defaults to True.
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do_amp_to_db_mel (bool, optional):
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enable/disable amplitude to dB conversion of mel spectrograms. Defaults to True.
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pitch_fmax (float, optional):
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Maximum frequency of the F0 frames. Defaults to ```640```.
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pitch_fmin (float, optional):
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Minimum frequency of the F0 frames. Defaults to ```1```.
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trim_db (int):
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Silence threshold used for silence trimming. Defaults to 45.
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do_rms_norm (bool, optional):
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enable/disable RMS volume normalization when loading an audio file. Defaults to False.
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db_level (int, optional):
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dB level used for rms normalization. The range is -99 to 0. Defaults to None.
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power (float):
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Exponent used for expanding spectrogra levels before running Griffin Lim. It helps to reduce the
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artifacts in the synthesized voice. Defaults to 1.5.
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griffin_lim_iters (int):
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Number of Griffing Lim iterations. Defaults to 60.
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num_mels (int):
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Number of mel-basis frames that defines the frame lengths of each mel-spectrogram frame. Defaults to 80.
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mel_fmin (float): Min frequency level used for the mel-basis filters. ~50 for male and ~95 for female voices.
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It needs to be adjusted for a dataset. Defaults to 0.
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mel_fmax (float):
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Max frequency level used for the mel-basis filters. It needs to be adjusted for a dataset.
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spec_gain (int):
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Gain applied when converting amplitude to DB. Defaults to 20.
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signal_norm (bool):
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enable/disable signal normalization. Defaults to True.
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min_level_db (int):
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minimum db threshold for the computed melspectrograms. Defaults to -100.
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symmetric_norm (bool):
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enable/disable symmetric normalization. If set True normalization is performed in the range [-k, k] else
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[0, k], Defaults to True.
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max_norm (float):
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```k``` defining the normalization range. Defaults to 4.0.
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clip_norm (bool):
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enable/disable clipping the our of range values in the normalized audio signal. Defaults to True.
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stats_path (str):
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Path to the computed stats file. Defaults to None.
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"""
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# stft parameters
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fft_size: int = 1024
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win_length: int = 1024
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hop_length: int = 256
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frame_shift_ms: int = None
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frame_length_ms: int = None
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stft_pad_mode: str = "reflect"
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# audio processing parameters
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sample_rate: int = 22050
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resample: bool = False
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preemphasis: float = 0.0
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ref_level_db: int = 20
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do_sound_norm: bool = False
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log_func: str = "np.log10"
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# silence trimming
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do_trim_silence: bool = True
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trim_db: int = 45
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# rms volume normalization
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do_rms_norm: bool = False
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db_level: float = None
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# griffin-lim params
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power: float = 1.5
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griffin_lim_iters: int = 60
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# mel-spec params
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num_mels: int = 80
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mel_fmin: float = 0.0
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mel_fmax: float = None
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spec_gain: int = 20
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do_amp_to_db_linear: bool = True
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do_amp_to_db_mel: bool = True
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# f0 params
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pitch_fmax: float = 640.0
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pitch_fmin: float = 1.0
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# normalization params
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signal_norm: bool = True
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min_level_db: int = -100
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symmetric_norm: bool = True
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max_norm: float = 4.0
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clip_norm: bool = True
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stats_path: str = None
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def check_values(
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self,
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):
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"""Check config fields"""
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c = asdict(self)
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check_argument("num_mels", c, restricted=True, min_val=10, max_val=2056)
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check_argument("fft_size", c, restricted=True, min_val=128, max_val=4058)
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check_argument("sample_rate", c, restricted=True, min_val=512, max_val=100000)
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check_argument(
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"frame_length_ms",
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c,
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restricted=True,
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min_val=10,
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max_val=1000,
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alternative="win_length",
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)
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check_argument("frame_shift_ms", c, restricted=True, min_val=1, max_val=1000, alternative="hop_length")
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check_argument("preemphasis", c, restricted=True, min_val=0, max_val=1)
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check_argument("min_level_db", c, restricted=True, min_val=-1000, max_val=10)
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check_argument("ref_level_db", c, restricted=True, min_val=0, max_val=1000)
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check_argument("power", c, restricted=True, min_val=1, max_val=5)
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check_argument("griffin_lim_iters", c, restricted=True, min_val=10, max_val=1000)
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# normalization parameters
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check_argument("signal_norm", c, restricted=True)
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check_argument("symmetric_norm", c, restricted=True)
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check_argument("max_norm", c, restricted=True, min_val=0.1, max_val=1000)
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check_argument("clip_norm", c, restricted=True)
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check_argument("mel_fmin", c, restricted=True, min_val=0.0, max_val=1000)
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check_argument("mel_fmax", c, restricted=True, min_val=500.0, allow_none=True)
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check_argument("spec_gain", c, restricted=True, min_val=1, max_val=100)
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check_argument("do_trim_silence", c, restricted=True)
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check_argument("trim_db", c, restricted=True)
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@dataclass
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class BaseDatasetConfig(Coqpit):
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"""Base config for TTS datasets.
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Args:
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formatter (str):
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Formatter name that defines used formatter in ```TTS.tts.datasets.formatter```. Defaults to `""`.
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dataset_name (str):
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Unique name for the dataset. Defaults to `""`.
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path (str):
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Root path to the dataset files. Defaults to `""`.
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meta_file_train (str):
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Name of the dataset meta file. Or a list of speakers to be ignored at training for multi-speaker datasets.
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Defaults to `""`.
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ignored_speakers (List):
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List of speakers IDs that are not used at the training. Default None.
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language (str):
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Language code of the dataset. If defined, it overrides `phoneme_language`. Defaults to `""`.
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phonemizer (str):
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Phonemizer used for that dataset's language. By default it uses `DEF_LANG_TO_PHONEMIZER`. Defaults to `""`.
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meta_file_val (str):
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Name of the dataset meta file that defines the instances used at validation.
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meta_file_attn_mask (str):
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Path to the file that lists the attention mask files used with models that require attention masks to
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train the duration predictor.
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"""
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formatter: str = ""
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dataset_name: str = ""
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path: str = ""
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meta_file_train: str = ""
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ignored_speakers: List[str] = None
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language: str = ""
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phonemizer: str = ""
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meta_file_val: str = ""
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meta_file_attn_mask: str = ""
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def check_values(
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self,
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):
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"""Check config fields"""
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c = asdict(self)
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check_argument("formatter", c, restricted=True)
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check_argument("path", c, restricted=True)
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check_argument("meta_file_train", c, restricted=True)
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check_argument("meta_file_val", c, restricted=False)
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check_argument("meta_file_attn_mask", c, restricted=False)
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@dataclass
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class BaseTrainingConfig(TrainerConfig):
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"""Base config to define the basic 🐸TTS training parameters that are shared
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among all the models. It is based on ```Trainer.TrainingConfig```.
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Args:
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model (str):
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Name of the model that is used in the training.
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num_loader_workers (int):
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Number of workers for training time dataloader.
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num_eval_loader_workers (int):
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Number of workers for evaluation time dataloader.
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"""
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model: str = None
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# dataloading
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num_loader_workers: int = 0
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num_eval_loader_workers: int = 0
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use_noise_augment: bool = False
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