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PaddleSpeech/parakeet/exps/waveflow/ljspeech.py

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2.9 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.
from pathlib import Path
import numpy as np
import pandas
from paddle.io import Dataset
from parakeet.data.batch import batch_spec
from parakeet.data.batch import batch_wav
class LJSpeech(Dataset):
"""A simple dataset adaptor for the processed ljspeech dataset."""
def __init__(self, root):
self.root = Path(root).expanduser()
meta_data = pandas.read_csv(
str(self.root / "metadata.csv"),
sep="\t",
header=None,
names=["fname", "frames", "samples"])
records = []
for row in meta_data.itertuples():
mel_path = str(self.root / "mel" / (row.fname + ".npy"))
wav_path = str(self.root / "wav" / (row.fname + ".npy"))
records.append((mel_path, wav_path))
self.records = records
def __getitem__(self, i):
mel_name, wav_name = self.records[i]
mel = np.load(mel_name)
wav = np.load(wav_name)
return mel, wav
def __len__(self):
return len(self.records)
class LJSpeechCollector(object):
"""A simple callable to batch LJSpeech examples."""
def __init__(self, padding_value=0.):
self.padding_value = padding_value
def __call__(self, examples):
mels = [example[0] for example in examples]
wavs = [example[1] for example in examples]
mels, _ = batch_spec(mels, pad_value=self.padding_value)
wavs, _ = batch_wav(wavs, pad_value=self.padding_value)
return mels, wavs
class LJSpeechClipCollector(object):
def __init__(self, clip_frames=65, hop_length=256):
self.clip_frames = clip_frames
self.hop_length = hop_length
def __call__(self, examples):
mels = []
wavs = []
for example in examples:
mel_clip, wav_clip = self.clip(example)
mels.append(mel_clip)
wavs.append(wav_clip)
mels = np.stack(mels)
wavs = np.stack(wavs)
return mels, wavs
def clip(self, example):
mel, wav = example
frames = mel.shape[-1]
start = np.random.randint(0, frames - self.clip_frames)
mel_clip = mel[:, start:start + self.clip_frames]
wav_clip = wav[start * self.hop_length:(start + self.clip_frames) *
self.hop_length]
return mel_clip, wav_clip