add AudioMelDataset for training vocoders.
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chenfeiyu
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Union, Optional, Callable, Tuple, Any
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from pathlib import Path
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from multiprocessing import Manager
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import numpy as np
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from paddle.io import Dataset
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import logging
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class AudioMelDataset(Dataset):
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"""Dataset to laod audio and mel dataset.
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Parameters
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----------
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root_dir : Union[Path, str]
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The root of the dataset.
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audio_pattern : str, optional
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A pattern to recursively find all audio files, by default
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"*-wave.npy"
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mel_pattern : str, optional
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A pattern to recursively find all mel feature files, by default
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"*-mel.npy"
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audio_load_fn : Callable, optional
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Function to load the audio, which takes a Path object or str as
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input, by default np.load
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mel_load_fn : Callable, optional
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Function to load the mel features, which takes a Path object or
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str as input, by default np.load
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audio_length_threshold : Optional[int], optional
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The minmimal length(number of samples) of the audio, by default None
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mel_length_threshold : Optional[int], optional
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The minmimal length(number of frames) of the audio, by default None
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return_utt_id : bool, optional
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Whether to include utterance indentifier in the return value of
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__getitem__, by default False
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use_cache : bool, optional
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Whether to cache seen examples while reading, by default False
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"""
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def __init__(self,
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root_dir: Union[Path, str],
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audio_pattern: str="*-wave.npy",
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mel_pattern: str="*-mel.npy",
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audio_load_fn: Callable=np.load,
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mel_load_fn: Callable=np.load,
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audio_length_threshold: Optional[int]=None,
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mel_length_threshold: Optional[int]=None,
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return_utt_id: bool=False,
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use_cache: bool=False):
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root_dir = Path(root_dir).expanduser()
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# find all of audio and mel files
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audio_files = sorted(list(root_dir.rglob(audio_pattern)))
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mel_files = sorted(list(root_dir.rglob(mel_pattern)))
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# filter by threshold
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if audio_length_threshold is not None:
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audio_lengths = [audio_load_fn(f).shape[0] for f in audio_files]
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idxs = [
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idx for idx in range(len(audio_files))
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if audio_lengths[idx] > audio_length_threshold
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]
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if len(audio_files) != len(idxs):
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logging.warning(
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f"Some files are filtered by audio length threshold "
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f"({len(audio_files)} -> {len(idxs)}).")
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audio_files = [audio_files[idx] for idx in idxs]
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mel_files = [mel_files[idx] for idx in idxs]
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if mel_length_threshold is not None:
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mel_lengths = [mel_load_fn(f).shape[1] for f in mel_files]
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idxs = [
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idx for idx in range(len(mel_files))
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if mel_lengths[idx] > mel_length_threshold
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]
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if len(mel_files) != len(idxs):
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logging.warning(
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f"Some files are filtered by mel length threshold "
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f"({len(mel_files)} -> {len(idxs)}).")
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audio_files = [audio_files[idx] for idx in idxs]
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mel_files = [mel_files[idx] for idx in idxs]
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# assert the number of files
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assert len(
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audio_files) != 0, f"Not found any audio files in {root_dir}."
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assert len(audio_files) == len(mel_files), \
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(f"Number of audio and mel files are different "
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f"({len(audio_files)} vs {len(mel_files)}).")
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self.audio_files = audio_files
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self.audio_load_fn = audio_load_fn
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self.mel_load_fn = mel_load_fn
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self.mel_files = mel_files
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if ".npy" in audio_pattern:
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self.utt_ids = [
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f.name.replace("-wave.npy", "") for f in audio_files
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]
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else:
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self.utt_ids = [f.stem for f in audio_files]
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self.return_utt_id = return_utt_id
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self.use_cache = use_cache
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if use_cache:
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self.manager = Manager()
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self.caches = self.manager.list()
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self.caches += [None for _ in range(len(audio_files))]
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def __getitem__(self, idx: int) -> Tuple:
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"""Get an example given the index.
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Parameters
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----------
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idx : int
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The index of the example.
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Returns
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-------
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utt_id : str
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Utterance identifier.
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audio : np.ndarray
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Shape (n_samples, ), the audio.
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mel: np.ndarray
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Shape (n_mels, n_frames), the mel spectrogram.
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"""
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if self.use_cache and self.caches[idx] is not None:
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return self.caches[idx]
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utt_id = self.utt_ids[idx]
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audio = self.audio_load_fn(self.audio_files[idx])
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mel = self.mel_load_fn(self.mel_files[idx])
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if self.return_utt_id:
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items = utt_id, audio, mel
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else:
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items = audio, mel
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if self.use_cache:
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self.caches[idx] = items
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return items
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def __len__(self):
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"""Returns the size of the dataset.
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Returns
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-------
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int
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The length of the dataset
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"""
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return len(self.audio_files)
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