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# -*- coding: utf-8 -*-
# Copyright 2020 The HuggingFace Inc. team and Minh Nguyen (@dathudeptrai)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tensorflow Auto Config modules."""
import logging
import yaml
import os
from collections import OrderedDict
from tensorflow_tts.configs import (
FastSpeechConfig,
FastSpeech2Config,
MelGANGeneratorConfig,
MultiBandMelGANGeneratorConfig,
HifiGANGeneratorConfig,
Tacotron2Config,
ParallelWaveGANGeneratorConfig,
)
from tensorflow_tts.utils import CACHE_DIRECTORY, CONFIG_FILE_NAME, LIBRARY_NAME
from tensorflow_tts import __version__ as VERSION
from huggingface_hub import hf_hub_url, cached_download
CONFIG_MAPPING = OrderedDict(
[
("fastspeech", FastSpeechConfig),
("fastspeech2", FastSpeech2Config),
("multiband_melgan_generator", MultiBandMelGANGeneratorConfig),
("melgan_generator", MelGANGeneratorConfig),
("hifigan_generator", HifiGANGeneratorConfig),
("tacotron2", Tacotron2Config),
("parallel_wavegan_generator", ParallelWaveGANGeneratorConfig),
]
)
class AutoConfig:
def __init__(self):
raise EnvironmentError(
"AutoConfig is designed to be instantiated "
"using the `AutoConfig.from_pretrained(pretrained_path)` method."
)
@classmethod
def from_pretrained(cls, pretrained_path, **kwargs):
# load weights from hf hub
if not os.path.isfile(pretrained_path):
# retrieve correct hub url
download_url = hf_hub_url(
repo_id=pretrained_path, filename=CONFIG_FILE_NAME
)
pretrained_path = str(
cached_download(
url=download_url,
library_name=LIBRARY_NAME,
library_version=VERSION,
cache_dir=CACHE_DIRECTORY,
)
)
with open(pretrained_path) as f:
config = yaml.load(f, Loader=yaml.Loader)
try:
model_type = config["model_type"]
config_class = CONFIG_MAPPING[model_type]
config_class = config_class(**config[model_type + "_params"], **kwargs)
config_class.set_config_params(config)
return config_class
except Exception:
raise ValueError(
"Unrecognized config in {}. "
"Should have a `model_type` key in its config.yaml, or contain one of the following strings "
"in its name: {}".format(
pretrained_path, ", ".join(CONFIG_MAPPING.keys())
)
)
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