You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
95 lines
3.0 KiB
95 lines
3.0 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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.
|
|
import pickle
|
|
from pathlib import Path
|
|
|
|
import numpy as np
|
|
from paddle.io import Dataset
|
|
|
|
from parakeet.data.batch import batch_spec
|
|
from parakeet.data.batch import batch_text_id
|
|
|
|
|
|
class LJSpeech(Dataset):
|
|
"""A simple dataset adaptor for the processed ljspeech dataset."""
|
|
|
|
def __init__(self, root):
|
|
self.root = Path(root).expanduser()
|
|
records = []
|
|
with open(self.root / "metadata.pkl", 'rb') as f:
|
|
metadata = pickle.load(f)
|
|
for mel_name, text, ids in metadata:
|
|
mel_name = self.root / "mel" / (mel_name + ".npy")
|
|
records.append((mel_name, text, ids))
|
|
self.records = records
|
|
|
|
def __getitem__(self, i):
|
|
mel_name, _, ids = self.records[i]
|
|
mel = np.load(mel_name)
|
|
return ids, mel
|
|
|
|
def __len__(self):
|
|
return len(self.records)
|
|
|
|
|
|
class LJSpeechCollector(object):
|
|
"""A simple callable to batch LJSpeech examples."""
|
|
|
|
def __init__(self, padding_idx=0, padding_value=0., padding_stop_token=1.0):
|
|
self.padding_idx = padding_idx
|
|
self.padding_value = padding_value
|
|
self.padding_stop_token = padding_stop_token
|
|
|
|
def __call__(self, examples):
|
|
texts = []
|
|
mels = []
|
|
text_lens = []
|
|
mel_lens = []
|
|
|
|
for data in examples:
|
|
text, mel = data
|
|
text = np.array(text, dtype=np.int64)
|
|
text_lens.append(len(text))
|
|
mels.append(mel)
|
|
texts.append(text)
|
|
mel_lens.append(mel.shape[1])
|
|
|
|
# Sort by text_len in descending order
|
|
texts = [
|
|
i
|
|
for i, _ in sorted(
|
|
zip(texts, text_lens), key=lambda x: x[1], reverse=True)
|
|
]
|
|
mels = [
|
|
i
|
|
for i, _ in sorted(
|
|
zip(mels, text_lens), key=lambda x: x[1], reverse=True)
|
|
]
|
|
|
|
mel_lens = [
|
|
i
|
|
for i, _ in sorted(
|
|
zip(mel_lens, text_lens), key=lambda x: x[1], reverse=True)
|
|
]
|
|
|
|
mel_lens = np.array(mel_lens, dtype=np.int64)
|
|
text_lens = np.array(sorted(text_lens, reverse=True), dtype=np.int64)
|
|
|
|
# Pad sequence with largest len of the batch
|
|
texts, _ = batch_text_id(texts, pad_id=self.padding_idx)
|
|
mels, _ = batch_spec(mels, pad_value=self.padding_value)
|
|
mels = np.transpose(mels, axes=(0, 2, 1))
|
|
|
|
return texts, mels, text_lens, mel_lens
|