import json
import os
import zipfile
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Dict, Tuple
import requests
from tqdm import tqdm
from TTS.config import load_config
from TTS.utils.generic_utils import get_user_data_dir
LICENSE_URLS = {
"cc by-nc-nd 4.0": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
"mpl": "https://www.mozilla.org/en-US/MPL/2.0/",
"mpl2": "https://www.mozilla.org/en-US/MPL/2.0/",
"mpl 2.0": "https://www.mozilla.org/en-US/MPL/2.0/",
"mit": "https://choosealicense.com/licenses/mit/",
"apache 2.0": "https://choosealicense.com/licenses/apache-2.0/",
"apache2": "https://choosealicense.com/licenses/apache-2.0/",
"cc-by-sa 4.0": "https://creativecommons.org/licenses/by-sa/4.0/",
}
class ModelManager(object):
"""Manage TTS models defined in .models.json.
It provides an interface to list and download
models defines in '.model.json'
Models are downloaded under '.TTS' folder in the user's
home path.
Args:
models_file (str): path to .model.json file. Defaults to None.
output_prefix (str): prefix to `tts` to download models. Defaults to None
progress_bar (bool): print a progress bar when donwloading a file. Defaults to False.
verbose (bool): print info. Defaults to True.
"""
def __init__(self, models_file=None, output_prefix=None, progress_bar=False, verbose=True):
super().__init__()
self.progress_bar = progress_bar
self.verbose = verbose
if output_prefix is None:
self.output_prefix = get_user_data_dir("tts")
else:
self.output_prefix = os.path.join(output_prefix, "tts")
self.models_dict = None
if models_file is not None:
self.read_models_file(models_file)
else:
# try the default location
path = Path(__file__).parent / "../.models.json"
self.read_models_file(path)
def read_models_file(self, file_path):
"""Read .models.json as a dict
Args:
file_path (str): path to .models.json.
"""
with open(file_path, "r", encoding="utf-8") as json_file:
self.models_dict = json.load(json_file)
def _list_models(self, model_type, model_count=0):
if self.verbose:
print(" Name format: type/language/dataset/model")
model_list = []
for lang in self.models_dict[model_type]:
for dataset in self.models_dict[model_type][lang]:
for model in self.models_dict[model_type][lang][dataset]:
model_full_name = f"{model_type}--{lang}--{dataset}--{model}"
output_path = os.path.join(self.output_prefix, model_full_name)
if self.verbose:
if os.path.exists(output_path):
print(f" {model_count}: {model_type}/{lang}/{dataset}/{model} [already downloaded]")
else:
print(f" {model_count}: {model_type}/{lang}/{dataset}/{model}")
model_list.append(f"{model_type}/{lang}/{dataset}/{model}")
model_count += 1
return model_list
def _list_for_model_type(self, model_type):
models_name_list = []
model_count = 1
model_type = "tts_models"
models_name_list.extend(self._list_models(model_type, model_count))
return models_name_list
def list_models(self):
models_name_list = []
model_count = 1
for model_type in self.models_dict:
model_list = self._list_models(model_type, model_count)
models_name_list.extend(model_list)
return models_name_list
def model_info_by_idx(self, model_query):
"""Print the description of the model from .models.json file using model_idx
Args:
model_query (str): <model_tye>/<model_idx>
"""
model_name_list = []
model_type, model_query_idx = model_query.split("/")
try:
model_query_idx = int(model_query_idx)
if model_query_idx <= 0:
print("> model_query_idx should be a positive integer!")
return
except:
print("> model_query_idx should be an integer!")
return
model_count = 0
if model_type in self.models_dict:
for lang in self.models_dict[model_type]:
for dataset in self.models_dict[model_type][lang]:
for model in self.models_dict[model_type][lang][dataset]:
model_name_list.append(f"{model_type}/{lang}/{dataset}/{model}")
model_count += 1
else:
print(f"> model_type {model_type} does not exist in the list.")
return
if model_query_idx > model_count:
print(f"model query idx exceeds the number of available models [{model_count}] ")
else:
model_type, lang, dataset, model = model_name_list[model_query_idx - 1].split("/")
print(f"> model type : {model_type}")
print(f"> language supported : {lang}")
print(f"> dataset used : {dataset}")
print(f"> model name : {model}")
if "description" in self.models_dict[model_type][lang][dataset][model]:
print(f"> description : {self.models_dict[model_type][lang][dataset][model]['description']}")
else:
print("> description : coming soon")
if "default_vocoder" in self.models_dict[model_type][lang][dataset][model]:
print(f"> default_vocoder : {self.models_dict[model_type][lang][dataset][model]['default_vocoder']}")
def model_info_by_full_name(self, model_query_name):
"""Print the description of the model from .models.json file using model_full_name
Args:
model_query_name (str): Format is <model_type>/<language>/<dataset>/<model_name>
"""
model_type, lang, dataset, model = model_query_name.split("/")
if model_type in self.models_dict:
if lang in self.models_dict[model_type]:
if dataset in self.models_dict[model_type][lang]:
if model in self.models_dict[model_type][lang][dataset]:
print(f"> model type : {model_type}")
print(f"> language supported : {lang}")
print(f"> dataset used : {dataset}")
print(f"> model name : {model}")
if "description" in self.models_dict[model_type][lang][dataset][model]:
print(
f"> description : {self.models_dict[model_type][lang][dataset][model]['description']}"
)
else:
print("> description : coming soon")
if "default_vocoder" in self.models_dict[model_type][lang][dataset][model]:
print(
f"> default_vocoder : {self.models_dict[model_type][lang][dataset][model]['default_vocoder']}"
)
else:
print(f"> model {model} does not exist for {model_type}/{lang}/{dataset}.")
else:
print(f"> dataset {dataset} does not exist for {model_type}/{lang}.")
else:
print(f"> lang {lang} does not exist for {model_type}.")
else:
print(f"> model_type {model_type} does not exist in the list.")
def list_tts_models(self):
"""Print all `TTS` models and return a list of model names
Format is `language/dataset/model`
"""
return self._list_for_model_type("tts_models")
def list_vocoder_models(self):
"""Print all the `vocoder` models and return a list of model names
Format is `language/dataset/model`
"""
return self._list_for_model_type("vocoder_models")
def list_langs(self):
"""Print all the available languages"""
print(" Name format: type/language")
for model_type in self.models_dict:
for lang in self.models_dict[model_type]:
print(f" >: {model_type}/{lang} ")
def list_datasets(self):
"""Print all the datasets"""
print(" Name format: type/language/dataset")
for model_type in self.models_dict:
for lang in self.models_dict[model_type]:
for dataset in self.models_dict[model_type][lang]:
print(f" >: {model_type}/{lang}/{dataset}")
@staticmethod
def print_model_license(model_item: Dict):
"""Print the license of a model
Args:
model_item (dict): model item in the models.json
"""
if "license" in model_item and model_item["license"].strip() != "":
print(f" > Model's license - {model_item['license']}")
if model_item["license"].lower() in LICENSE_URLS:
print(f" > Check {LICENSE_URLS[model_item['license'].lower()]} for more info.")
else:
print(" > Check https://opensource.org/licenses for more info.")
else:
print(" > Model's license - No license information available")
def download_model(self, model_name):
"""Download model files given the full model name.
Model name is in the format
'type/language/dataset/model'
e.g. 'tts_model/en/ljspeech/tacotron'
Every model must have the following files:
- *.pth : pytorch model checkpoint file.
- config.json : model config file.
- scale_stats.npy (if exist): scale values for preprocessing.
Args:
model_name (str): model name as explained above.
"""
# fetch model info from the dict
model_type, lang, dataset, model = model_name.split("/")
model_full_name = f"{model_type}--{lang}--{dataset}--{model}"
model_item = self.models_dict[model_type][lang][dataset][model]
# set the model specific output path
output_path = os.path.join(self.output_prefix, model_full_name)
if os.path.exists(output_path):
print(f" > {model_name} is already downloaded.")
else:
os.makedirs(output_path, exist_ok=True)
print(f" > Downloading model to {output_path}")
# download from github release
self._download_zip_file(model_item["github_rls_url"], output_path, self.progress_bar)
self.print_model_license(model_item=model_item)
# find downloaded files
output_model_path, output_config_path = self._find_files(output_path)
# update paths in the config.json
self._update_paths(output_path, output_config_path)
return output_model_path, output_config_path, model_item
@staticmethod
def _find_files(output_path: str) -> Tuple[str, str]:
"""Find the model and config files in the output path
Args:
output_path (str): path to the model files
Returns:
Tuple[str, str]: path to the model file and config file
"""
model_file = None
config_file = None
for file_name in os.listdir(output_path):
if file_name in ["model_file.pth", "model_file.pth.tar", "model.pth"]:
model_file = os.path.join(output_path, file_name)
elif file_name == "config.json":
config_file = os.path.join(output_path, file_name)
if model_file is None:
raise ValueError(" [!] Model file not found in the output path")
if config_file is None:
raise ValueError(" [!] Config file not found in the output path")
return model_file, config_file
@staticmethod
def _find_speaker_encoder(output_path: str) -> str:
"""Find the speaker encoder file in the output path
Args:
output_path (str): path to the model files
Returns:
str: path to the speaker encoder file
"""
speaker_encoder_file = None
for file_name in os.listdir(output_path):
if file_name in ["model_se.pth", "model_se.pth.tar"]:
speaker_encoder_file = os.path.join(output_path, file_name)
return speaker_encoder_file
def _update_paths(self, output_path: str, config_path: str) -> None:
"""Update paths for certain files in config.json after download.
Args:
output_path (str): local path the model is downloaded to.
config_path (str): local config.json path.
"""
output_stats_path = os.path.join(output_path, "scale_stats.npy")
output_d_vector_file_path = os.path.join(output_path, "speakers.json")
output_speaker_ids_file_path = os.path.join(output_path, "speaker_ids.json")
speaker_encoder_config_path = os.path.join(output_path, "config_se.json")
speaker_encoder_model_path = self._find_speaker_encoder(output_path)
# update the scale_path.npy file path in the model config.json
self._update_path("audio.stats_path", output_stats_path, config_path)
# update the speakers.json file path in the model config.json to the current path
self._update_path("d_vector_file", output_d_vector_file_path, config_path)
self._update_path("model_args.d_vector_file", output_d_vector_file_path, config_path)
# update the speaker_ids.json file path in the model config.json to the current path
self._update_path("speakers_file", output_speaker_ids_file_path, config_path)
self._update_path("model_args.speakers_file", output_speaker_ids_file_path, config_path)
# update the speaker_encoder file path in the model config.json to the current path
self._update_path("speaker_encoder_model_path", speaker_encoder_model_path, config_path)
self._update_path("model_args.speaker_encoder_model_path", speaker_encoder_model_path, config_path)
self._update_path("speaker_encoder_config_path", speaker_encoder_config_path, config_path)
self._update_path("model_args.speaker_encoder_config_path", speaker_encoder_config_path, config_path)
@staticmethod
def _update_path(field_name, new_path, config_path):
"""Update the path in the model config.json for the current environment after download"""
if new_path and os.path.exists(new_path):
config = load_config(config_path)
field_names = field_name.split(".")
if len(field_names) > 1:
# field name points to a sub-level field
sub_conf = config
for fd in field_names[:-1]:
if fd in sub_conf:
sub_conf = sub_conf[fd]
else:
return
sub_conf[field_names[-1]] = new_path
else:
# field name points to a top-level field
config[field_name] = new_path
config.save_json(config_path)
@staticmethod
def _download_zip_file(file_url, output_folder, progress_bar):
"""Download the github releases"""
# download the file
r = requests.get(file_url, stream=True)
# extract the file
try:
total_size_in_bytes = int(r.headers.get("content-length", 0))
block_size = 1024 # 1 Kibibyte
if progress_bar:
progress_bar = tqdm(total=total_size_in_bytes, unit="iB", unit_scale=True)
temp_zip_name = os.path.join(output_folder, file_url.split("/")[-1])
with open(temp_zip_name, "wb") as file:
for data in r.iter_content(block_size):
if progress_bar:
progress_bar.update(len(data))
file.write(data)
with zipfile.ZipFile(temp_zip_name) as z:
z.extractall(output_folder)
os.remove(temp_zip_name) # delete zip after extract
except zipfile.BadZipFile:
print(f" > Error: Bad zip file - {file_url}")
raise zipfile.BadZipFile # pylint: disable=raise-missing-from
# move the files to the outer path
for file_path in z.namelist()[1:]:
src_path = os.path.join(output_folder, file_path)
dst_path = os.path.join(output_folder, os.path.basename(file_path))
copyfile(src_path, dst_path)
# remove the extracted folder
rmtree(os.path.join(output_folder, z.namelist()[0]))
@staticmethod
def _check_dict_key(my_dict, key):
if key in my_dict.keys() and my_dict[key] is not None:
if not isinstance(key, str):
return True
if isinstance(key, str) and len(my_dict[key]) > 0:
return True
return False