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# -*- coding: utf-8 -*-
# Copyright 2020 The TensorFlowTTS Team.
#
# 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 Processor modules."""
import logging
import json
import os
from collections import OrderedDict
from tensorflow_tts.processor import (
LJSpeechProcessor,
KSSProcessor,
BakerProcessor,
LibriTTSProcessor,
ThorstenProcessor,
LJSpeechUltimateProcessor,
SynpaflexProcessor,
JSUTProcessor,
)
from tensorflow_tts.utils import CACHE_DIRECTORY, PROCESSOR_FILE_NAME, LIBRARY_NAME
from tensorflow_tts import __version__ as VERSION
from huggingface_hub import hf_hub_url, cached_download
CONFIG_MAPPING = OrderedDict(
[
("LJSpeechProcessor", LJSpeechProcessor),
("KSSProcessor", KSSProcessor),
("BakerProcessor", BakerProcessor),
("LibriTTSProcessor", LibriTTSProcessor),
("ThorstenProcessor", ThorstenProcessor),
("LJSpeechUltimateProcessor", LJSpeechUltimateProcessor),
("SynpaflexProcessor", SynpaflexProcessor),
("JSUTProcessor", JSUTProcessor),
]
)
class AutoProcessor:
def __init__(self):
raise EnvironmentError(
"AutoProcessor is designed to be instantiated "
"using the `AutoProcessor.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=PROCESSOR_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, "r") as f:
config = json.load(f)
try:
processor_name = config["processor_name"]
processor_class = CONFIG_MAPPING[processor_name]
processor_class = processor_class(
data_dir=None, loaded_mapper_path=pretrained_path
)
return processor_class
except Exception:
raise ValueError(
"Unrecognized processor in {}. "
"Should have a `processor_name` key in its config.json, or contain one of the following strings "
"in its name: {}".format(
pretrained_path, ", ".join(CONFIG_MAPPING.keys())
)
)
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