mirror of https://github.com/coqui-ai/TTS.git
76 lines
2.3 KiB
Python
76 lines
2.3 KiB
Python
import json
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import os
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import re
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import yaml
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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 open(json_path, "r", encoding="utf-8") as f:
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input_str = f.read()
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# handle comments
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input_str = re.sub(r"\\\n", "", input_str)
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input_str = re.sub(r"//.*\n", "\n", input_str)
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data = json.loads(input_str)
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return data
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def _search_configs(model_name):
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config_class = None
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paths = ["TTS.tts.configs", "TTS.vocoder.configs", "TTS.speaker_encoder"]
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for path in paths:
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try:
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config_class = find_module(path, model_name + "_config")
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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):
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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) -> None:
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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 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 open(config_path, "r", encoding="utf-8") as f:
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input_str = f.read()
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data = json.loads(input_str)
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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 = _search_configs(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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