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add AudioDataset and MelDataset

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chenfeiyu 2021-06-10 16:26:09 +08:00
parent 3c964fde54
commit 6a8b3f92df
2 changed files with 265 additions and 0 deletions
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133
parakeet/datasets/audio_dataset.py Normal file
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# Copyright (c) 2021 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 typing import Union, Optional, Callable, Tuple
from pathlib import Path
from multiprocessing import Manager
import numpy as np
from paddle.io import Dataset
import logging
class AudioDataset(Dataset):
"""Dataset to load audio.
Parameters
----------
root_dir : Union[Path, str]
The root of the dataset.
audio_pattern : str
A pattern to recursively find all audio files, by default "*-wave.npy"
audio_length_threshold : int, optional
The minmimal length(number of samples) of the audio, by default None
audio_load_fn : Callable, optional
Function to load the audio, which takes a Path object or str as input,
by default np.load
return_utt_id : bool, optional
Whether to include utterance indentifier in the return value of
__getitem__, by default False
use_cache : bool, optional
Whether to cache seen examples while reading, by default False
"""
def __init__(
self,
root_dir: Union[Path, str],
audio_pattern: str="*-wave.npy",
audio_length_threshold: Optional[int]=None,
audio_load_fn: Callable=np.load,
return_utt_id: bool=False,
use_cache: bool=False, ):
# allow str and Path that contains '~'
root_dir = Path(root_dir).expanduser()
# recursively find all of audio files that match thr pattern
audio_files = sorted(list(root_dir.rglob(audio_pattern)))
# filter by threshold
if audio_length_threshold is not None:
audio_lengths = [audio_load_fn(f).shape[0] for f in audio_files]
idxs = [
idx for idx in range(len(audio_files))
if audio_lengths[idx] > audio_length_threshold
]
if len(audio_files) != len(idxs):
logging.warning(
f"some files are filtered by audio length threshold "
f"({len(audio_files)} -> {len(idxs)}).")
audio_files = [audio_files[idx] for idx in idxs]
# assert the number of files
assert len(
audio_files) != 0, f"Not any audio files found in {root_dir}."
self.audio_files = audio_files
self.audio_load_fn = audio_load_fn
self.return_utt_id = return_utt_id
# TODO(chenfeiyu): better strategy to get utterance id
if ".npy" in audio_pattern:
self.utt_ids = [
f.name.replace("-wave.npy", "") for f in audio_files
]
else:
self.utt_ids = [f.stem for f in audio_files]
self.use_cache = use_cache
if use_cache:
# use manager to share object between multiple processes
# avoid per-reader process caching
self.manager = Manager()
self.caches = self.manager.list()
self.caches += [None for _ in range(len(audio_files))]
def __getitem__(self, idx: int) -> Tuple[str, np.ndarray]:
"""Get an example given the index.
Parameters
----------
idx : int
The index.
Returns
-------
utt_id : str
Utterance identifier.
audio : np.ndarray
Shape (n_samples, ), the audio.
"""
if self.use_cache and self.caches[idx] is not None:
return self.caches[idx]
utt_id = self.utt_ids[idx]
audio = self.audio_load_fn(self.audio_files[idx])
if self.return_utt_id:
items = utt_id, audio
else:
items = audio
if self.use_cache:
self.caches[idx] = items
return items
def __len__(self) -> int:
"""Returns the size of the dataset.
Returns
-------
int
The length of the dataset
"""
return len(self.audio_files)

132
parakeet/datasets/mel_dataset.py Normal file
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# Copyright (c) 2021 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 typing import Union, Optional, Callable, Tuple
from pathlib import Path
from multiprocessing import Manager
import numpy as np
from paddle.io import Dataset
import logging
class MelDataset(Dataset):
"""Dataset to load mel-spectrograms.
Parameters
----------
root_dir : Union[Path, str]
The root of the dataset.
mel_pattern : str, optional
A pattern to recursively find all mel feature files, by default
"*-feats.npy"
mel_length_threshold : Optional[int], optional
The minmimal length(number of frames) of the audio, by default None
mel_load_fn : Callable, optional
Function to load the audio, which takes a Path object or str as input,
by default np.load
return_utt_id : bool, optional
Whether to include utterance indentifier in the return value of
__getitem__, by default False
use_cahce : bool, optional
Whether to cache seen examples while reading, by default False
"""
def __init__(
self,
root_dir: Union[Path, str],
mel_pattern: str="*-feats.npy",
mel_length_threshold: Optional[int]=None,
mel_load_fn: Callable=np.load,
return_utt_id: bool=False,
use_cahce: bool=False, ):
# allow str and Path that contains '~'
root_dir = Path(root_dir).expanduser()
# find all of the mel files
mel_files = sorted(list(root_dir.rglob(mel_pattern)))
# filter by threshold
if mel_length_threshold is not None:
mel_lengths = [mel_load_fn(f).shape[1] for f in mel_files]
idxs = [
idx for idx in range(len(mel_files))
if mel_lengths[idx] > mel_length_threshold
]
if len(mel_files) != len(idxs):
logging.warning(
f"Some files are filtered by mel length threshold "
f"({len(mel_files)} -> {len(idxs)}).")
mel_files = [mel_files[idx] for idx in idxs]
# assert the number of files
assert len(mel_files) != 0, f"Not found any mel files in {root_dir}."
self.mel_files = mel_files
self.mel_load_fn = mel_load_fn
# TODO(chenfeiyu): better strategy to get utterance id
if ".npy" in mel_pattern:
self.utt_ids = [
f.name.replace("-feats.npy", "") for f in mel_files
]
else:
self.utt_ids = [f.stem for f in mel_files]
self.return_utt_id = return_utt_id
self.use_cache = use_cahce
if use_cahce:
self.manager = Manager()
self.caches = self.manager.list()
self.caches += [None for _ in range(len(mel_files))]
def __getitem__(self, idx):
"""Get an example given the index.
Parameters
----------
idx : int
The index
Returns
-------
utt_id : str
Utterance identifier.
audio : np.ndarray
Shape (n_mels, n_frames), the mel spectrogram.
"""
if self.use_cache and self.caches[idx] is not None:
return self.caches[idx]
utt_id = self.utt_ids[idx]
mel = self.mel_load_fn(self.mel_files[idx])
if self.return_utt_id:
items = utt_id, mel
else:
items = mel
if self.use_cache:
self.caches[idx] = items
return items
def __len__(self):
"""Returns the size of the dataset.
Returns
-------
int
The length of the dataset
"""
return len(self.mel_files)