未验证 提交 ec5b27f5 编写于 作者: Y YangZhou 提交者: GitHub

add paddle audio dataset && backend (#45939)

* add audio feature dataset

* fix coding style

* fix coding style2

* rm librosa

* rm voxceleb

* rm librosa in test

* add scipy fftpack

* add functional

* fix setup

* fix setup2

* rm colorlog

* refactor dataset __init__.py

* fix converage

* fix librosa import error

* fix windows test

* fix windows ci

* rm datasets

* fix setup

* remove testdata

* add librosa in requirement

* add librosa in requirement2

* change librosa to 0.8.1

* update ci docker

* fix ci error

* fix ci error2

* fix ci coverage

* fix converage

* fix coverage

* rm audio_base in test, notest,test=coverage

* fix copyright

* rm backend

* add datast in __init__

* rm compliance&&add function test

* fix setup

* fix windows

* fix windows2

* fix test timeout

* add backend & datasets

* fix bugs

* fix ci time issue

* add dataset test

* rm test_audio_feature

* avoid windows isssue, tmp

* note windows isssue

* skip windows issue

* refactor dataset test

* add dataset.py

* fix dtype in layers.mfcc

* fix ci-static-check

* fix dtype in layers.mfcc && fix ci-static-check

* add relative accuracy

* modity API.spec

* skip cuda11.2 test

* skip cuda11.2 test2

* skip cuda11.2

* change dataset name

* fix format

* update api.spec

* update api.spec2

* fix coverage

* add dataset test

* rm download load dict

* rm download load dict in init

* update api.spec3

* fix dataset coverage

* fix coverage

* fix coverage2

* restore api.spec

* restore api.spec2

* fix api-spec 3

* fix api-spec 4

* fix api.spec

* fix api.spec6

* refactor init_backend

* fix typo

* change paddleaudio backend set

* fix get_current_audio_backend()

* fix format

* fix format2

* remove format in parameters

* fix format2

* add warning massage in wave_backend && remove redundant audio util

* rm audio util in print_signatures

* fix format3

* add tess dataset license

* format warning

* add more info in warning msg

* add paddleaudio version check

* replace dataset esc50 with tess

* add tess dataset && rm numpy transform in dataset.py

* fix set audio backend bug

* fix equal error

* fix format && coverage error

* add api example

* fix format

* fix error

* fix typo

* add noqa in __init__

* fix backend doc example error

* rm seed in dataset

* update bakcend example

* fix typo

* fix typo

* fix example err

* fix typo

* fix ci dataset test

* fix example fil

* try to fix ci

* clean dataset doc

* change get_current_audio_backend to get_current_backend

* creplace paddle.audio.backends.info with paddle.audio.info, same with load, save

* fix ci error

* repalce api in test_audio_backend

* fix save&&set_backend exmaple
上级 5a2e5179
......@@ -21,3 +21,13 @@ paddle.audio.functional.functional.mel_frequencies (ArgSpec(args=['n_mels', 'f_m
paddle.audio.functional.functional.mel_to_hz (ArgSpec(args=['mel', 'htk'], varargs=None, varkw=None, defaults=(False,), kwonlyargs=[], kwonlydefaults=None, annotations={'return': typing.Union[float, paddle.Tensor], 'mel': typing.Union[float, paddle.Tensor], 'htk': <class 'bool'>}), ('document', 'e93b432d382f98c60d7c7599489e7072'))
paddle.audio.functional.functional.power_to_db (ArgSpec(args=['spect', 'ref_value', 'amin', 'top_db'], varargs=None, varkw=None, defaults=(1.0, 1e-10, 80.0), kwonlyargs=[], kwonlydefaults=None, annotations={'return': <class 'paddle.Tensor'>, 'spect': <class 'paddle.Tensor'>, 'ref_value': <class 'float'>, 'amin': <class 'float'>, 'top_db': typing.Union[float, NoneType]}), ('document', '28bbb1973e8399e856bfaea0415cecb9'))
paddle.audio.functional.window.get_window (ArgSpec(args=['window', 'win_length', 'fftbins', 'dtype'], varargs=None, varkw=None, defaults=(True, 'float64'), kwonlyargs=[], kwonlydefaults=None, annotations={'return': <class 'paddle.Tensor'>, 'window': typing.Union[str, typing.Tuple[str, float]], 'win_length': <class 'int'>, 'fftbins': <class 'bool'>, 'dtype': <class 'str'>}), ('document', '2418d63da10c0cd5da9ecf0a88ddf783'))
paddle.audio.backends (ArgSpec(), ('document', 'd41d8cd98f00b204e9800998ecf8427e'))
paddle.audio.backends.init_backend.get_current_audio_backend (ArgSpec(args=[], varargs=None, varkw=None, defaults=None, kwonlyargs=[], kwonlydefaults=None, annotations={'return': <class 'str'>}), ('document', '3ff9fd62e8be1f3dc7e34afaf50e1645'))
paddle.audio.backends.init_backend.list_available_backends (ArgSpec(args=[], varargs=None, varkw=None, defaults=None, kwonlyargs=[], kwonlydefaults=None, annotations={'return': typing.List[str]}), ('document', '8eba49f1b69f7ec7fa139a0714a2724e'))
paddle.audio.backends.init_backend.set_backend (ArgSpec(args=['backend_name'], varargs=None, varkw=None, defaults=None, kwonlyargs=[], kwonlydefaults=None, annotations={'backend_name': <class 'str'>}), ('document', '9680247dd97274d345dee415e2787527'))
paddle.audio.backends.wave_backend.info (ArgSpec(args=['filepath', 'format'], varargs=None, varkw=None, defaults=(None,), kwonlyargs=[], kwonlydefaults=None, annotations={'return': <class 'paddle.audio.backends.backend.AudioInfo'>, 'filepath': <class 'str'>, 'format': typing.Union[str, NoneType]}), ('document', 'e0ffd3accd942a9b0a4c08463a9f60f6'))
paddle.audio.backends.wave_backend.load (ArgSpec(args=['filepath', 'frame_offset', 'num_frames', 'normalize', 'channels_first', 'format'], varargs=None, varkw=None, defaults=(0, -1, True, True, None), kwonlyargs=[], kwonlydefaults=None, annotations={'return': typing.Tuple[paddle.Tensor, int], 'filepath': typing.Union[str, pathlib.Path], 'frame_offset': <class 'int'>, 'num_frames': <class 'int'>, 'normalize': <class 'bool'>, 'channels_first': <class 'bool'>, 'format': typing.Union[str, NoneType]}), ('document', '4de50575ca516b4b7c7c82c7fdec808f'))
paddle.audio.backends.wave_backend.save (ArgSpec(args=['filepath', 'src', 'sample_rate', 'channels_first', 'compression', 'format', 'encoding', 'bits_per_sample'], varargs=None, varkw=None, defaults=(True, None, None, None, None), kwonlyargs=[], kwonlydefaults=None, annotations={'filepath': <class 'str'>, 'src': <class 'paddle.Tensor'>, 'sample_rate': <class 'int'>, 'channels_first': <class 'bool'>, 'compression': typing.Union[float, NoneType], 'format': typing.Union[str, NoneType], 'encoding': typing.Union[str, NoneType], 'bits_per_sample': typing.Union[int, NoneType]}), ('document', '4c85cfcd29a0dcdfc32e74db8c0c3961'))
paddle.audio.datasets (ArgSpec(), ('document', 'd41d8cd98f00b204e9800998ecf8427e'))
paddle.audio.datasets.TESS (ArgSpec(), ('document', '3605f3aa2191ede7ddbe594cd27bb067'))
paddle.audio.datasets.TESS.meta_info (ArgSpec(), ('document', '60d548a6f71629c3b69bcda3a30d4819'))
......@@ -14,5 +14,11 @@
from . import features
from . import functional
from . import datasets
from . import backends
__all__ = ["functional", "features"]
from .backends.backend import info, load, save
__all__ = [
"functional", "features", "datasets", "backends", "load", "info", "save"
]
# Copyright (c) 2022 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 . import init_backend
from .init_backend import get_current_backend # noqa: F401
from .init_backend import list_available_backends # noqa: F401
from .init_backend import set_backend
init_backend._init_set_audio_backend()
__all__ = [
'get_current_backend',
'list_available_backends',
'set_backend',
]
# Copyright (c) 2022 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 paddle
from pathlib import Path
from typing import Optional, Tuple, Union
class AudioInfo:
""" Audio info, return type of backend info function """
def __init__(self, sample_rate: int, num_samples: int, num_channels: int,
bits_per_sample: int, encoding: str):
self.sample_rate = sample_rate
self.num_samples = num_samples
self.num_channels = num_channels
self.bits_per_sample = bits_per_sample
self.encoding = encoding
def info(filepath: str) -> AudioInfo:
"""Get signal information of input audio file.
Args:
filepath: audio path or file object.
Returns:
AudioInfo: info of the given audio.
Example:
.. code-block:: python
import os
import paddle
sample_rate = 16000
wav_duration = 0.5
num_channels = 1
num_frames = sample_rate * wav_duration
wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1
waveform = wav_data.tile([num_channels, 1])
base_dir = os.getcwd()
filepath = os.path.join(base_dir, "test.wav")
paddle.audio.save(filepath, waveform, sample_rate)
wav_info = paddle.audio.info(filepath)
"""
# for API doc
raise NotImplementedError("please set audio backend")
def load(filepath: Union[str, Path],
frame_offset: int = 0,
num_frames: int = -1,
normalize: bool = True,
channels_first: bool = True) -> Tuple[paddle.Tensor, int]:
"""Load audio data from file.Load the audio content start form frame_offset, and get num_frames.
Args:
frame_offset: from 0 to total frames,
num_frames: from -1 (means total frames) or number frames which want to read,
normalize:
if True: return audio which norm to (-1, 1), dtype=float32
if False: return audio with raw data, dtype=int16
channels_first:
if True: return audio with shape (channels, time)
Return:
Tuple[paddle.Tensor, int]: (audio_content, sample rate)
Exampels:
.. code-block:: python
import os
import paddle
sample_rate = 16000
wav_duration = 0.5
num_channels = 1
num_frames = sample_rate * wav_duration
wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1
waveform = wav_data.tile([num_channels, 1])
base_dir = os.getcwd()
filepath = os.path.join(base_dir, "test.wav")
paddle.audio.save(filepath, waveform, sample_rate)
wav_data_read, sr = paddle.audio.load(filepath)
"""
# for API doc
raise NotImplementedError("please set audio backend")
def save(
filepath: str,
src: paddle.Tensor,
sample_rate: int,
channels_first: bool = True,
encoding: Optional[str] = None,
bits_per_sample: Optional[int] = 16,
):
"""
Save audio tensor to file.
Args:
filepath: saved path
src: the audio tensor
sample_rate: the number of samples of audio per second.
channels_first: src channel infomation
if True, means input tensor is (channels, time)
if False, means input tensor is (time, channels)
encoding:encoding format, wave_backend only support PCM16 now.
bits_per_sample: bits per sample, wave_backend only support 16 bits now.
Returns:
None
Examples:
.. code-block:: python
import paddle
sample_rate = 16000
wav_duration = 0.5
num_channels = 1
num_frames = sample_rate * wav_duration
wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1
waveform = wav_data.tile([num_channels, 1])
filepath = "./test.wav"
paddle.audio.save(filepath, waveform, sample_rate)
"""
# for API doc
raise NotImplementedError("please set audio backend")
# Copyright (c) 2022 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 sys
import warnings
from . import wave_backend
from . import backend
from typing import List
import paddle
def _check_version(version: str) -> bool:
# require paddleaudio >= 1.0.2
ver_arr = version.split('.')
v0 = int(ver_arr[0])
v1 = int(ver_arr[1])
v2 = int(ver_arr[2])
if v0 < 1:
return False
if v0 == 1 and v1 == 0 and v2 <= 1:
return False
return True
def list_available_backends() -> List[str]:
""" List available backends, the backends in paddleaudio and the default backend.
Returns:
List[str]: The list of available backends.
Examples:
.. code-block:: python
import paddle
sample_rate = 16000
wav_duration = 0.5
num_channels = 1
num_frames = sample_rate * wav_duration
wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1
waveform = wav_data.tile([num_channels, 1])
wav_path = "./test.wav"
current_backend = paddle.audio.backends.get_current_backend()
print(current_backend) # wave_backend, the default backend.
backends = paddle.audio.backends.list_available_backends()
# default backends is ['wave_backend']
# backends is ['wave_backend', 'soundfile'], if have installed paddleaudio >= 1.0.2
if 'soundfile' in backends:
paddle.audio.backends.set_backend('soundfile')
paddle.audio.save(wav_path, waveform, sample_rate)
"""
backends = []
try:
import paddleaudio
except ImportError:
package = "paddleaudio"
warn_msg = (
"Failed importing {}. \n"
"only wave_banckend(only can deal with PCM16 WAV) supportted.\n"
"if want soundfile_backend(more audio type suppported),\n"
"please manually installed (usually with `pip install {} >= 1.0.2`). "
).format(package, package)
warnings.warn(warn_msg)
if "paddleaudio" in sys.modules:
version = paddleaudio.__version__
if _check_version(version) == False:
err_msg = (
"the version of paddleaudio installed is {},\n"
"please ensure the paddleaudio >= 1.0.2.").format(version)
raise ImportError(err_msg)
backends = paddleaudio.backends.list_audio_backends()
backends.append("wave_backend")
return backends
def get_current_backend() -> str:
""" Get the name of the current audio backend
Returns:
str: The name of the current backend,
the wave_backend or backend imported from paddleaudio
Examples:
.. code-block:: python
import paddle
sample_rate = 16000
wav_duration = 0.5
num_channels = 1
num_frames = sample_rate * wav_duration
wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1
waveform = wav_data.tile([num_channels, 1])
wav_path = "./test.wav"
current_backend = paddle.audio.backends.get_current_backend()
print(current_backend) # wave_backend, the default backend.
backends = paddle.audio.backends.list_available_backends()
# default backends is ['wave_backend']
# backends is ['wave_backend', 'soundfile'], if have installed paddleaudio >= 1.0.2
if 'soundfile' in backends:
paddle.audio.backends.set_backend('soundfile')
paddle.audio.save(wav_path, waveform, sample_rate)
"""
current_backend = None
if "paddleaudio" in sys.modules:
import paddleaudio
current_backend = paddleaudio.backends.get_audio_backend()
if paddle.audio.load == paddleaudio.load:
return current_backend
return "wave_backend"
def set_backend(backend_name: str):
"""Set the backend by one of the list_audio_backend return.
Args:
backend (str): one of the list_audio_backend. "wave_backend" is the default. "soundfile" imported from paddleaudio.
Returns:
None
Examples:
.. code-block:: python
import paddle
sample_rate = 16000
wav_duration = 0.5
num_channels = 1
num_frames = sample_rate * wav_duration
wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1
waveform = wav_data.tile([num_channels, 1])
wav_path = "./test.wav"
current_backend = paddle.audio.backends.get_current_backend()
print(current_backend) # wave_backend, the default backend.
backends = paddle.audio.backends.list_available_backends()
# default backends is ['wave_backend']
# backends is ['wave_backend', 'soundfile'], if have installed paddleaudio >= 1.0.2
if 'soundfile' in backends:
paddle.audio.backends.set_backend('soundfile')
paddle.audio.save(wav_path, waveform, sample_rate)
"""
if backend_name not in list_available_backends():
raise NotImplementedError()
if backend_name == "wave_backend":
module = wave_backend
else:
import paddleaudio
paddleaudio.backends.set_audio_backend(backend_name)
module = paddleaudio
for func in ["save", "load", "info"]:
setattr(backend, func, getattr(module, func))
setattr(paddle.audio, func, getattr(module, func))
def _init_set_audio_backend():
# init the default wave_backend.
for func in ["save", "load", "info"]:
setattr(backend, func, getattr(wave_backend, func))
# Copyright (c) 2022 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 paddle
import wave
import numpy as np
from pathlib import Path
from typing import Optional, Tuple, Union
from .backend import AudioInfo
def _error_message():
package = "paddleaudio"
warn_msg = (
"only PCM16 WAV supportted. \n"
"if want support more other audio types, please "
"manually installed (usually with `pip install {}`). \n "
"and use paddle.audio.backends.set_backend('soundfile') to set audio backend"
).format(package)
return warn_msg
def info(filepath: str) -> AudioInfo:
"""Get signal information of input audio file.
Args:
filepath: audio path or file object.
Returns:
AudioInfo: info of the given audio.
Example:
.. code-block:: python
import os
import paddle
sample_rate = 16000
wav_duration = 0.5
num_channels = 1
num_frames = sample_rate * wav_duration
wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1
waveform = wav_data.tile([num_channels, 1])
base_dir = os.getcwd()
filepath = os.path.join(base_dir, "test.wav")
paddle.audio.save(filepath, waveform, sample_rate)
wav_info = paddle.audio.info(filepath)
"""
if hasattr(filepath, 'read'):
file_obj = filepath
else:
file_obj = open(filepath, 'rb')
try:
file_ = wave.open(file_obj)
except wave.Error:
file_obj.seek(0)
file_obj.close()
err_msg = _error_message()
raise NotImplementedError(err_msg)
channels = file_.getnchannels()
sample_rate = file_.getframerate()
sample_frames = file_.getnframes() # audio frame
bits_per_sample = file_.getsampwidth() * 8
encoding = "PCM_S" # default WAV encoding, only support
file_obj.close()
return AudioInfo(sample_rate, sample_frames, channels, bits_per_sample,
encoding)
def load(filepath: Union[str, Path],
frame_offset: int = 0,
num_frames: int = -1,
normalize: bool = True,
channels_first: bool = True) -> Tuple[paddle.Tensor, int]:
"""Load audio data from file. load the audio content start form frame_offset, and get num_frames.
Args:
frame_offset: from 0 to total frames,
num_frames: from -1 (means total frames) or number frames which want to read,
normalize:
if True: return audio which norm to (-1, 1), dtype=float32
if False: return audio with raw data, dtype=int16
channels_first:
if True: return audio with shape (channels, time)
Return:
Tuple[paddle.Tensor, int]: (audio_content, sample rate)
Exampels:
.. code-block:: python
import os
import paddle
sample_rate = 16000
wav_duration = 0.5
num_channels = 1
num_frames = sample_rate * wav_duration
wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1
waveform = wav_data.tile([num_channels, 1])
base_dir = os.getcwd()
filepath = os.path.join(base_dir, "test.wav")
paddle.audio.save(filepath, waveform, sample_rate)
wav_data_read, sr = paddle.audio.load(filepath)
"""
if hasattr(filepath, 'read'):
file_obj = filepath
else:
file_obj = open(filepath, 'rb')
try:
file_ = wave.open(file_obj)
except wave.Error:
file_obj.seek(0)
file_obj.close()
err_msg = _error_message()
raise NotImplementedError(err_msg)
channels = file_.getnchannels()
sample_rate = file_.getframerate()
frames = file_.getnframes() # audio frame
audio_content = file_.readframes(frames)
file_obj.close()
# default_subtype = "PCM_16", only support PCM16 WAV
audio_as_np16 = np.frombuffer(audio_content, dtype=np.int16)
audio_as_np32 = audio_as_np16.astype(np.float32)
if normalize:
# dtype = "float32"
audio_norm = audio_as_np32 / (2**15)
else:
# dtype = "int16"
audio_norm = audio_as_np32
waveform = np.reshape(audio_norm, (frames, channels))
if num_frames != -1:
waveform = waveform[frame_offset:frame_offset + num_frames, :]
waveform = paddle.to_tensor(waveform)
if channels_first:
waveform = paddle.transpose(waveform, perm=[1, 0])
return waveform, sample_rate
def save(
filepath: str,
src: paddle.Tensor,
sample_rate: int,
channels_first: bool = True,
encoding: Optional[str] = None,
bits_per_sample: Optional[int] = 16,
):
"""
Save audio tensor to file.
Args:
filepath: saved path
src: the audio tensor
sample_rate: the number of samples of audio per second.
channels_first: src channel infomation
if True, means input tensor is (channels, time)
if False, means input tensor is (time, channels)
encoding: audio encoding format, wave_backend only support PCM16 now.
bits_per_sample: bits per sample, wave_backend only support 16 bits now.
Returns:
None
Examples:
.. code-block:: python
import paddle
sample_rate = 16000
wav_duration = 0.5
num_channels = 1
num_frames = sample_rate * wav_duration
wav_data = paddle.linspace(-1.0, 1.0, num_frames) * 0.1
waveform = wav_data.tile([num_channels, 1])
filepath = "./test.wav"
paddle.audio.save(filepath, waveform, sample_rate)
"""
assert src.ndim == 2, "Expected 2D tensor"
audio_numpy = src.numpy()
# change src shape to (time, channels)
if channels_first:
audio_numpy = np.transpose(audio_numpy)
channels = audio_numpy.shape[1]
# only support PCM16
if bits_per_sample not in (None, 16):
raise ValueError("Invalid bits_per_sample, only supprt 16 bit")
sample_width = int(bits_per_sample / 8) # 2
if src.dtype == paddle.float32:
audio_numpy = (audio_numpy * (2**15)).astype("<h")
with wave.open(filepath, 'w') as f:
f.setnchannels(channels)
f.setsampwidth(sample_width)
f.setframerate(sample_rate)
f.writeframes(audio_numpy.tobytes())
# Copyright (c) 2022 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 .esc50 import ESC50
from .tess import TESS
__all__ = ["ESC50", "TESS"]
# Copyright (c) 2022 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 List
import paddle
from ..features import MelSpectrogram
from ..features import Spectrogram
from ..features import MFCC
from ..features import LogMelSpectrogram
feat_funcs = {
'raw': None,
'melspectrogram': MelSpectrogram,
'mfcc': MFCC,
'logmelspectrogram': LogMelSpectrogram,
'spectrogram': Spectrogram
}
class AudioClassificationDataset(paddle.io.Dataset):
"""
Base class of audio classification dataset.
"""
def __init__(self,
files: List[str],
labels: List[int],
feat_type: str = 'raw',
sample_rate: int = None,
**kwargs):
"""
Ags:
files (:obj:`List[str]`): A list of absolute path of audio files.
labels (:obj:`List[int]`): Labels of audio files.
feat_type (:obj:`str`, `optional`, defaults to `raw`):
It identifies the feature type that user wants to extrace of an audio file.
"""
super(AudioClassificationDataset, self).__init__()
if feat_type not in feat_funcs.keys():
raise RuntimeError(
f"Unknown feat_type: {feat_type}, it must be one in {list(feat_funcs.keys())}"
)
self.files = files
self.labels = labels
self.feat_type = feat_type
self.sample_rate = sample_rate
self.feat_config = kwargs # Pass keyword arguments to customize feature config
def _get_data(self, input_file: str):
raise NotImplementedError
def _convert_to_record(self, idx):
file, label = self.files[idx], self.labels[idx]
waveform, sample_rate = paddle.audio.load(file)
self.sample_rate = sample_rate
feat_func = feat_funcs[self.feat_type]
record = {}
if len(waveform.shape) == 2:
waveform = waveform.squeeze(0) # 1D input
waveform = paddle.to_tensor(waveform, dtype=paddle.float32)
if feat_func is not None:
waveform = waveform.unsqueeze(0) # (batch_size, T)
if self.feat_type != 'spectrogram':
feature_extractor = feat_func(sr=self.sample_rate,
**self.feat_config)
else:
feature_extractor = feat_func(**self.feat_config)
record['feat'] = feature_extractor(waveform).squeeze(0)
else:
record['feat'] = waveform
record['label'] = label
return record
def __getitem__(self, idx):
record = self._convert_to_record(idx)
return record['feat'], record['label']
def __len__(self):
return len(self.files)
# 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.
import collections
import os
from typing import List
from typing import Tuple
from paddle.utils import download
from paddle.dataset.common import DATA_HOME
from .dataset import AudioClassificationDataset
__all__ = ['ESC50']
class ESC50(AudioClassificationDataset):
"""
The ESC-50 dataset is a labeled collection of 2000 environmental audio recordings
suitable for benchmarking methods of environmental sound classification. The dataset
consists of 5-second-long recordings organized into 50 semantical classes (with
40 examples per class)
Reference:
ESC: Dataset for Environmental Sound Classification
http://dx.doi.org/10.1145/2733373.2806390
Args:
mode (str, optional): It identifies the dataset mode (train or dev). Default:train.
split (int, optional): It specify the fold of dev dataset. Default:1.
feat_type (str, optional): It identifies the feature type that user wants to extrace of an audio file. Default:raw.
archive(dict, optional): it tells where to download the audio archive. Default:None.
Returns:
:ref:`api_paddle_io_Dataset`. An instance of ESC50 dataset.
Examples:
.. code-block:: python
import paddle
mode = 'dev'
esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,
feat_type='raw')
for idx in range(5):
audio, label = esc50_dataset[idx]
# do something with audio, label
print(audio.shape, label)
# [audio_data_length] , label_id
esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,
feat_type='mfcc',
n_mfcc=40)
for idx in range(5):
audio, label = esc50_dataset[idx]
# do something with mfcc feature, label
print(audio.shape, label)
# [feature_dim, length] , label_id
"""
archive = {
'url': 'https://paddleaudio.bj.bcebos.com/datasets/ESC-50-master.zip',
'md5': '7771e4b9d86d0945acce719c7a59305a',
}
label_list = [
# Animals
'Dog',
'Rooster',
'Pig',
'Cow',
'Frog',
'Cat',
'Hen',
'Insects (flying)',
'Sheep',
'Crow',
# Natural soundscapes & water sounds
'Rain',
'Sea waves',
'Crackling fire',
'Crickets',
'Chirping birds',
'Water drops',
'Wind',
'Pouring water',
'Toilet flush',
'Thunderstorm',
# Human, non-speech sounds
'Crying baby',
'Sneezing',
'Clapping',
'Breathing',
'Coughing',
'Footsteps',
'Laughing',
'Brushing teeth',
'Snoring',
'Drinking, sipping',
# Interior/domestic sounds
'Door knock',
'Mouse click',
'Keyboard typing',
'Door, wood creaks',
'Can opening',
'Washing machine',
'Vacuum cleaner',
'Clock alarm',
'Clock tick',
'Glass breaking',
# Exterior/urban noises
'Helicopter',
'Chainsaw',
'Siren',
'Car horn',
'Engine',
'Train',
'Church bells',
'Airplane',
'Fireworks',
'Hand saw',
]
meta = os.path.join('ESC-50-master', 'meta', 'esc50.csv')
meta_info = collections.namedtuple(
'META_INFO',
('filename', 'fold', 'target', 'category', 'esc10', 'src_file', 'take'))
audio_path = os.path.join('ESC-50-master', 'audio')
def __init__(self,
mode: str = 'train',
split: int = 1,
feat_type: str = 'raw',
archive=None,
**kwargs):
if archive is not None:
self.archive = archive
files, labels = self._get_data(mode, split)
super(ESC50, self).__init__(files=files,
labels=labels,
feat_type=feat_type,
**kwargs)
def _get_meta_info(self) -> List[collections.namedtuple]:
ret = []
with open(os.path.join(DATA_HOME, self.meta), 'r') as rf:
for line in rf.readlines()[1:]:
ret.append(self.meta_info(*line.strip().split(',')))
return ret
def _get_data(self, mode: str, split: int) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)) or \
not os.path.isfile(os.path.join(DATA_HOME, self.meta)):
download.get_path_from_url(self.archive['url'],
DATA_HOME,
self.archive['md5'],
decompress=True)
meta_info = self._get_meta_info()
files = []
labels = []
for sample in meta_info:
filename, fold, target, _, _, _, _ = sample
if mode == 'train' and int(fold) != split:
files.append(os.path.join(DATA_HOME, self.audio_path, filename))
labels.append(int(target))
if mode != 'train' and int(fold) == split:
files.append(os.path.join(DATA_HOME, self.audio_path, filename))
labels.append(int(target))
return files, labels
# Copyright (c) 2022 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 collections
import os
from typing import List
from typing import Tuple
from paddle.utils import download
from paddle.dataset.common import DATA_HOME
from .dataset import AudioClassificationDataset
__all__ = ['TESS']
class TESS(AudioClassificationDataset):
"""
TESS is a set of 200 target words were spoken in the carrier phrase
"Say the word _____' by two actresses (aged 26 and 64 years) and
recordings were made of the set portraying each of seven emotions(anger,
disgust, fear, happiness, pleasant surprise, sadness, and neutral).
There are 2800 stimuli in total.
Reference:
Toronto emotional speech set (TESS) https://tspace.library.utoronto.ca/handle/1807/24487
https://doi.org/10.5683/SP2/E8H2MF
Args:
mode (str, optional): It identifies the dataset mode (train or dev). Defaults to train.
n_folds (int, optional): Split the dataset into n folds. 1 fold for dev dataset and n-1 for train dataset. Defaults to 5.
split (int, optional): It specify the fold of dev dataset. Defaults to 1.
feat_type (str, optional): It identifies the feature type that user wants to extrace of an audio file. Defaults to raw.
archive(dict): it tells where to download the audio archive. Defaults to None.
Returns:
:ref:`api_paddle_io_Dataset`. An instance of TESS dataset.
Examples:
.. code-block:: python
import paddle
mode = 'dev'
tess_dataset = paddle.audio.datasets.TESS(mode=mode,
feat_type='raw')
for idx in range(5):
audio, label = tess_dataset[idx]
# do something with audio, label
print(audio.shape, label)
# [audio_data_length] , label_id
tess_dataset = paddle.audio.datasets.TESS(mode=mode,
feat_type='mfcc',
n_mfcc=40)
for idx in range(5):
audio, label = tess_dataset[idx]
# do something with mfcc feature, label
print(audio.shape, label)
# [feature_dim, num_frames] , label_id
"""
archive = {
'url':
'https://bj.bcebos.com/paddleaudio/datasets/TESS_Toronto_emotional_speech_set.zip',
'md5': '1465311b24d1de704c4c63e4ccc470c7',
}
label_list = [
'angry',
'disgust',
'fear',
'happy',
'neutral',
'ps', # pleasant surprise
'sad',
]
meta_info = collections.namedtuple('META_INFO',
('speaker', 'word', 'emotion'))
audio_path = 'TESS_Toronto_emotional_speech_set'
def __init__(self,
mode='train',
n_folds=5,
split=1,
feat_type='raw',
archive=None,
**kwargs):
"""
"""
assert split <= n_folds, f'The selected split should not be larger than n_fold, but got {split} > {n_folds}'
if archive is not None:
self.archive = archive
files, labels = self._get_data(mode, n_folds, split)
super(TESS, self).__init__(files=files,
labels=labels,
feat_type=feat_type,
**kwargs)
def _get_meta_info(self, files) -> List[collections.namedtuple]:
ret = []
for file in files:
basename_without_extend = os.path.basename(file)[:-4]
ret.append(self.meta_info(*basename_without_extend.split('_')))
return ret
def _get_data(self, mode, n_folds, split) -> Tuple[List[str], List[int]]:
if not os.path.isdir(os.path.join(DATA_HOME, self.audio_path)):
download.get_path_from_url(self.archive['url'],
DATA_HOME,
self.archive['md5'],
decompress=True)
wav_files = []
for root, _, files in os.walk(os.path.join(DATA_HOME, self.audio_path)):
for file in files:
if file.endswith('.wav'):
wav_files.append(os.path.join(root, file))
meta_info = self._get_meta_info(wav_files)
files = []
labels = []
n_samples_per_fold = len(meta_info) // n_folds
for idx, sample in enumerate(meta_info):
_, _, emotion = sample
target = self.label_list.index(emotion)
fold = idx // n_samples_per_fold + 1
if mode == 'train' and int(fold) != split:
files.append(wav_files[idx])
labels.append(target)
if mode != 'train' and int(fold) == split:
files.append(wav_files[idx])
labels.append(target)
return files, labels
# Copyright (c) 2022 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 unittest
import soundfile
import numpy as np
import os
import paddle.audio
class TestAudioBackends(unittest.TestCase):
def setUp(self):
self.initParmas()
def initParmas(self):
def get_wav_data(dtype: str, num_channels: int, num_frames: int):
dtype_ = getattr(paddle, dtype)
base = paddle.linspace(-1.0, 1.0, num_frames, dtype=dtype_) * 0.1
data = base.tile([num_channels, 1])
return data
self.duration = 0.5
self.num_channels = 1
self.sr = 16000
self.dtype = "float32"
self.window_size = 1024
waveform_tensor = get_wav_data(self.dtype,
self.num_channels,
num_frames=self.duration * self.sr)
# shape (1, 8000)
self.waveform = waveform_tensor.numpy()
def test_backend(self):
base_dir = os.getcwd()
wave_wav_path = os.path.join(base_dir, "wave_test.wav")
paddle.audio.save(wave_wav_path,
paddle.to_tensor(self.waveform),
self.sr,
channels_first=True)
# test backends(wave)(wave_backend) info
wav_info = paddle.audio.info(wave_wav_path)
self.assertTrue(wav_info.sample_rate, self.sr)
self.assertTrue(wav_info.num_channels, self.num_channels)
self.assertTrue(wav_info.bits_per_sample, 16)
with open(wave_wav_path, 'rb') as file_:
wav_info = paddle.audio.info(file_)
self.assertTrue(wav_info.sample_rate, self.sr)
self.assertTrue(wav_info.num_channels, self.num_channels)
self.assertTrue(wav_info.bits_per_sample, 16)
# test backends(wave_backend) load & save
wav_data, sr = paddle.audio.load(wave_wav_path)
np.testing.assert_array_almost_equal(wav_data, self.waveform, decimal=4)
with soundfile.SoundFile(wave_wav_path, "r") as file_:
dtype = "float32"
frames = file_._prepare_read(0, None, -1)
waveform = file_.read(frames, dtype, always_2d=True)
waveform = waveform.T
np.testing.assert_array_almost_equal(wav_data, waveform)
with open(wave_wav_path, 'rb') as file_:
wav_data, sr = paddle.audio.load(file_,
normalize=False,
num_frames=10000)
with soundfile.SoundFile(wave_wav_path, "r") as file_:
dtype = "int16"
frames = file_._prepare_read(0, None, -1)
waveform = file_.read(frames, dtype, always_2d=True)
waveform = waveform.T
np.testing.assert_array_almost_equal(wav_data, waveform)
current_backend = paddle.audio.backends.get_current_backend()
self.assertTrue(current_backend in ["wave_backend", "soundfile"])
paddle.audio.backends.set_backend("wave_backend")
backends = paddle.audio.backends.list_available_backends()
for backend in backends:
self.assertTrue(backend in ["wave_backend", "soundfile"])
# Test error
try:
paddle.audio.backends.set_backend("jfiji")
except NotImplementedError:
pass
try:
import paddleaudio
backends = paddle.audio.backends.list_available_backends()
for backend in backends:
self.assertTrue(backend in ["wave_backend", "soundfile"])
current_backend = paddle.audio.backends.get_current_backend()
self.assertTrue(current_backend, "wave_backend")
paddleaudio.backends.set_audio_backend("soundfile")
paddle.audio.backends.set_backend("soundfile")
current_backend = paddle.audio.backends.get_current_backend()
self.assertTrue(current_backend, "soundfile")
wav_info = paddle.audio.info(wave_wav_path)
self.assertTrue(wav_info.sample_rate, self.sr)
self.assertTrue(wav_info.num_channels, self.num_channels)
self.assertTrue(wav_info.bits_per_sample, 16)
paddle.audio.backends.set_backend("wave_backend")
except ImportError:
pass
try:
paddle.audio.save(wave_wav_path,
paddle.to_tensor(self.waveform),
self.sr,
bits_per_sample=24,
channels_first=True)
except ValueError:
pass
try:
paddle.audio.save(wave_wav_path,
paddle.to_tensor(self.waveform).unsqueeze(0),
self.sr)
except AssertionError:
pass
fake_data = np.array([0, 1, 2, 3, 4, 6], np.float32)
soundfile.write(wave_wav_path, fake_data, 1, subtype="DOUBLE")
try:
wav_info = paddle.audio.info(wave_wav_path)
except NotImplementedError:
pass
try:
wav_data = paddle.audio.load(wave_wav_path)
except NotImplementedError:
pass
if os.path.exists(wave_wav_path):
os.remove(wave_wav_path)
if __name__ == '__main__':
unittest.main()
# Copyright (c) 2022 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 unittest
import numpy as np
import paddle
import itertools
from parameterized import parameterized
def parameterize(*params):
return parameterized.expand(list(itertools.product(*params)))
class TestAudioDatasets(unittest.TestCase):
@parameterize(["dev", "train"], [40, 64])
def test_tess_dataset(self, mode: str, params: int):
"""
TESS dataset
Reference:
Toronto emotional speech set (TESS) https://tspace.library.utoronto.ca/handle/1807/24487
https://doi.org/10.5683/SP2/E8H2MF
"""
archive = {
'url':
'https://bj.bcebos.com/paddleaudio/datasets/TESS_Toronto_emotional_speech_set_lite.zip',
'md5': '9ffb5e3adf28d4d6b787fa94bd59b975',
} # small part of TESS dataset for test.
tess_dataset = paddle.audio.datasets.TESS(mode=mode,
feat_type='mfcc',
n_mfcc=params,
archive=archive)
idx = np.random.randint(0, 30)
elem = tess_dataset[idx]
self.assertTrue(elem[0].shape[0] == params)
self.assertTrue(0 <= elem[1] <= 6)
tess_dataset = paddle.audio.datasets.TESS(mode=mode,
feat_type='spectrogram',
n_fft=params)
elem = tess_dataset[idx]
self.assertTrue(elem[0].shape[0] == (params // 2 + 1))
self.assertTrue(0 <= elem[1] <= 6)
tess_dataset = paddle.audio.datasets.TESS(mode="dev",
feat_type='logmelspectrogram',
n_mels=params)
elem = tess_dataset[idx]
self.assertTrue(elem[0].shape[0] == params)
self.assertTrue(0 <= elem[1] <= 6)
tess_dataset = paddle.audio.datasets.TESS(mode="dev",
feat_type='melspectrogram',
n_mels=params)
elem = tess_dataset[idx]
self.assertTrue(elem[0].shape[0] == params)
self.assertTrue(0 <= elem[1] <= 6)
@parameterize(["dev", "train"], [40, 64])
def test_esc50_dataset(self, mode: str, params: int):
"""
ESC50 dataset
Reference:
ESC: Dataset for Environmental Sound Classification
http://dx.doi.org/10.1145/2733373.2806390
"""
archive = {
'url':
'https://bj.bcebos.com/paddleaudio/datasets/ESC-50-master-lite.zip',
'md5': '1e9ba53265143df5b2804a743f2d1956',
} # small part of ESC50 dataset for test.
esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,
feat_type='raw',
archive=archive)
idx = np.random.randint(0, 6)
elem = esc50_dataset[idx]
self.assertTrue(elem[0].shape[0] == 220500)
self.assertTrue(0 <= elem[1] <= 2)
esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,
feat_type='mfcc',
n_mfcc=params,
archive=archive)
idx = np.random.randint(0, 6)
elem = esc50_dataset[idx]
self.assertTrue(elem[0].shape[0] == params)
self.assertTrue(0 <= elem[1] <= 2)
esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,
feat_type='spectrogram',
n_fft=params)
elem = esc50_dataset[idx]
self.assertTrue(elem[0].shape[0] == (params // 2 + 1))
self.assertTrue(0 <= elem[1] <= 2)
esc50_dataset = paddle.audio.datasets.ESC50(
mode=mode, feat_type='logmelspectrogram', n_mels=params)
elem = esc50_dataset[idx]
self.assertTrue(elem[0].shape[0] == params)
self.assertTrue(0 <= elem[1] <= 2)
esc50_dataset = paddle.audio.datasets.ESC50(mode=mode,
feat_type='melspectrogram',
n_mels=params)
elem = esc50_dataset[idx]
self.assertTrue(elem[0].shape[0] == params)
self.assertTrue(0 <= elem[1] <= 2)
if __name__ == '__main__':
unittest.main()
......@@ -373,6 +373,8 @@ packages=['paddle',
'paddle.audio',
'paddle.audio.functional',
'paddle.audio.features',
'paddle.audio.datasets',
'paddle.audio.backends',
'paddle.text',
'paddle.text.datasets',
'paddle.incubate',
......
......@@ -221,17 +221,47 @@ def process_module(m, attr="__all__"):
def check_public_api():
modulelist = [ #npqa
paddle, paddle.amp, paddle.nn, paddle.nn.functional,
paddle.nn.initializer, paddle.nn.utils, paddle.static, paddle.static.nn,
paddle.io, paddle.jit, paddle.metric, paddle.distribution,
paddle.optimizer, paddle.optimizer.lr, paddle.regularizer, paddle.text,
paddle.utils, paddle.utils.download, paddle.utils.profiler,
paddle.utils.cpp_extension, paddle.sysconfig, paddle.vision,
paddle.vision.datasets, paddle.vision.models, paddle.vision.transforms,
paddle.vision.ops, paddle.distributed, paddle.distributed.fleet,
paddle.distributed.fleet.utils, paddle.distributed.parallel,
paddle.distributed.utils, paddle.callbacks, paddle.hub, paddle.autograd,
paddle.incubate, paddle.inference, paddle.onnx, paddle.device
paddle,
paddle.amp,
paddle.nn,
paddle.nn.functional,
paddle.nn.initializer,
paddle.nn.utils,
paddle.static,
paddle.static.nn,
paddle.io,
paddle.jit,
paddle.metric,
paddle.distribution,
paddle.optimizer,
paddle.optimizer.lr,
paddle.regularizer,
paddle.text,
paddle.utils,
paddle.utils.download,
paddle.utils.profiler,
paddle.utils.cpp_extension,
paddle.sysconfig,
paddle.vision,
paddle.vision.datasets,
paddle.vision.models,
paddle.vision.transforms,
paddle.vision.ops,
paddle.distributed,
paddle.distributed.fleet,
paddle.distributed.fleet.utils,
paddle.distributed.parallel,
paddle.distributed.utils,
paddle.callbacks,
paddle.hub,
paddle.autograd,
paddle.incubate,
paddle.inference,
paddle.onnx,
paddle.device,
paddle.audio,
paddle.audio.backends,
paddle.audio.datasets,
]
apinum = 0
......
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