mirror of https://github.com/coqui-ai/TTS.git
218 lines
9.0 KiB
Python
218 lines
9.0 KiB
Python
import io
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import json
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import os
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import zipfile
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from pathlib import Path
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from shutil import copyfile, rmtree
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import requests
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from TTS.config import load_config
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from TTS.utils.generic_utils import get_user_data_dir
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class ModelManager(object):
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"""Manage TTS models defined in .models.json.
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It provides an interface to list and download
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models defines in '.model.json'
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Models are downloaded under '.TTS' folder in the user's
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home path.
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Args:
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models_file (str): path to .model.json
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"""
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def __init__(self, models_file=None, output_prefix=None):
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super().__init__()
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if output_prefix is None:
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self.output_prefix = get_user_data_dir("tts")
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else:
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self.output_prefix = os.path.join(output_prefix, "tts")
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self.models_dict = None
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if models_file is not None:
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self.read_models_file(models_file)
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else:
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# try the default location
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path = Path(__file__).parent / "../.models.json"
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self.read_models_file(path)
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def read_models_file(self, file_path):
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"""Read .models.json as a dict
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Args:
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file_path (str): path to .models.json.
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"""
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with open(file_path, "r", encoding="utf-8") as json_file:
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self.models_dict = json.load(json_file)
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def _list_models(self, model_type, model_count=0):
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model_list = []
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for lang in self.models_dict[model_type]:
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for dataset in self.models_dict[model_type][lang]:
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for model in self.models_dict[model_type][lang][dataset]:
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model_full_name = f"{model_type}--{lang}--{dataset}--{model}"
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output_path = os.path.join(self.output_prefix, model_full_name)
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if os.path.exists(output_path):
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print(f" {model_count}: {model_type}/{lang}/{dataset}/{model} [already downloaded]")
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else:
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print(f" {model_count}: {model_type}/{lang}/{dataset}/{model}")
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model_list.append(f"{model_type}/{lang}/{dataset}/{model}")
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model_count += 1
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return model_list
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def _list_for_model_type(self, model_type):
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print(" Name format: language/dataset/model")
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models_name_list = []
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model_count = 1
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model_type = "tts_models"
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models_name_list.extend(self._list_models(model_type, model_count))
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return [name.replace(model_type + "/", "") for name in models_name_list]
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def list_models(self):
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print(" Name format: type/language/dataset/model")
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models_name_list = []
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model_count = 1
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for model_type in self.models_dict:
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model_list = self._list_models(model_type, model_count)
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models_name_list.extend(model_list)
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return models_name_list
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def list_tts_models(self):
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"""Print all `TTS` models and return a list of model names
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Format is `language/dataset/model`
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"""
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return self._list_for_model_type("tts_models")
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def list_vocoder_models(self):
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"""Print all the `vocoder` models and return a list of model names
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Format is `language/dataset/model`
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"""
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return self._list_for_model_type("vocoder_models")
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def list_langs(self):
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"""Print all the available languages"""
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print(" Name format: type/language")
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for model_type in self.models_dict:
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for lang in self.models_dict[model_type]:
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print(f" >: {model_type}/{lang} ")
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def list_datasets(self):
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"""Print all the datasets"""
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print(" Name format: type/language/dataset")
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for model_type in self.models_dict:
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for lang in self.models_dict[model_type]:
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for dataset in self.models_dict[model_type][lang]:
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print(f" >: {model_type}/{lang}/{dataset}")
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def download_model(self, model_name):
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"""Download model files given the full model name.
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Model name is in the format
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'type/language/dataset/model'
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e.g. 'tts_model/en/ljspeech/tacotron'
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Every model must have the following files:
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- *.pth.tar : pytorch model checkpoint file.
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- config.json : model config file.
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- scale_stats.npy (if exist): scale values for preprocessing.
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Args:
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model_name (str): model name as explained above.
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"""
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# fetch model info from the dict
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model_type, lang, dataset, model = model_name.split("/")
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model_full_name = f"{model_type}--{lang}--{dataset}--{model}"
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model_item = self.models_dict[model_type][lang][dataset][model]
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# set the model specific output path
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output_path = os.path.join(self.output_prefix, model_full_name)
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output_model_path = os.path.join(output_path, "model_file.pth.tar")
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output_config_path = os.path.join(output_path, "config.json")
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if os.path.exists(output_path):
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print(f" > {model_name} is already downloaded.")
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else:
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os.makedirs(output_path, exist_ok=True)
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print(f" > Downloading model to {output_path}")
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# download from github release
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self._download_zip_file(model_item["github_rls_url"], output_path)
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# update paths in the config.json
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self._update_paths(output_path, output_config_path)
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return output_model_path, output_config_path, model_item
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def _update_paths(self, output_path: str, config_path: str) -> None:
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"""Update paths for certain files in config.json after download.
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Args:
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output_path (str): local path the model is downloaded to.
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config_path (str): local config.json path.
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"""
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output_stats_path = os.path.join(output_path, "scale_stats.npy")
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output_d_vector_file_path = os.path.join(output_path, "speakers.json")
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output_speaker_ids_file_path = os.path.join(output_path, "speaker_ids.json")
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speaker_encoder_config_path = os.path.join(output_path, "config_se.json")
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speaker_encoder_model_path = os.path.join(output_path, "model_se.pth.tar")
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# update the scale_path.npy file path in the model config.json
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self._update_path("audio.stats_path", output_stats_path, config_path)
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# update the speakers.json file path in the model config.json to the current path
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self._update_path("d_vector_file", output_d_vector_file_path, config_path)
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self._update_path("model_args.d_vector_file", output_d_vector_file_path, config_path)
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# update the speaker_ids.json file path in the model config.json to the current path
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self._update_path("speakers_file", output_speaker_ids_file_path, config_path)
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self._update_path("model_args.speakers_file", output_speaker_ids_file_path, config_path)
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# update the speaker_encoder file path in the model config.json to the current path
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self._update_path("speaker_encoder_model_path", speaker_encoder_model_path, config_path)
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self._update_path("model_args.speaker_encoder_model_path", speaker_encoder_model_path, config_path)
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self._update_path("speaker_encoder_config_path", speaker_encoder_config_path, config_path)
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self._update_path("model_args.speaker_encoder_config_path", speaker_encoder_config_path, config_path)
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@staticmethod
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def _update_path(field_name, new_path, config_path):
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"""Update the path in the model config.json for the current environment after download"""
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if os.path.exists(new_path):
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config = load_config(config_path)
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field_names = field_name.split(".")
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if len(field_names) > 1:
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# field name points to a sub-level field
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sub_conf = config
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for fd in field_names[:-1]:
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if fd in sub_conf:
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sub_conf = sub_conf[fd]
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else:
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return
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sub_conf[field_names[-1]] = new_path
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else:
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# field name points to a top-level field
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config[field_name] = new_path
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config.save_json(config_path)
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@staticmethod
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def _download_zip_file(file_url, output_folder):
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"""Download the github releases"""
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# download the file
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r = requests.get(file_url)
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# extract the file
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with zipfile.ZipFile(io.BytesIO(r.content)) as z:
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z.extractall(output_folder)
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# move the files to the outer path
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for file_path in z.namelist()[1:]:
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src_path = os.path.join(output_folder, file_path)
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dst_path = os.path.join(output_folder, os.path.basename(file_path))
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copyfile(src_path, dst_path)
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# remove the extracted folder
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rmtree(os.path.join(output_folder, z.namelist()[0]))
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@staticmethod
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def _check_dict_key(my_dict, key):
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if key in my_dict.keys() and my_dict[key] is not None:
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if not isinstance(key, str):
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return True
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if isinstance(key, str) and len(my_dict[key]) > 0:
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return True
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return False
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