paddleaudio.features.layers module

class paddleaudio.features.layers.LogMelSpectrogram(sr: int = 22050, n_fft: int = 512, hop_length: Optional[int] = None, win_length: Optional[int] = None, window: str = 'hann', power: float = 2.0, center: bool = True, pad_mode: str = 'reflect', n_mels: int = 64, f_min: float = 50.0, f_max: Optional[float] = None, htk: bool = False, norm: Union[str, float] = 'slaney', ref_value: float = 1.0, amin: float = 1e-10, top_db: Optional[float] = None, dtype: str = 'float32')

Bases: Layer

Compute log-mel-spectrogram feature of given signals, typically audio waveforms.

Args:

sr (int, optional): Sample rate. Defaults to 22050. n_fft (int, optional): The number of frequency components of the discrete Fourier transform. Defaults to 512. hop_length (Optional[int], optional): The hop length of the short time FFT. If None, it is set to win_length//4. Defaults to None. win_length (Optional[int], optional): The window length of the short time FFT. If None, it is set to same as n_fft. Defaults to None. window (str, optional): The window function applied to the signal before the Fourier transform. Supported window functions: 'hamming', 'hann', 'kaiser', 'gaussian', 'exponential', 'triang', 'bohman', 'blackman', 'cosine', 'tukey', 'taylor'. Defaults to 'hann'. power (float, optional): Exponent for the magnitude spectrogram. Defaults to 2.0. center (bool, optional): Whether to pad x to make that the \(t imes hop\_length\) at the center of t-th frame. Defaults to True. pad_mode (str, optional): Choose padding pattern when center is True. Defaults to 'reflect'. n_mels (int, optional): Number of mel bins. Defaults to 64. f_min (float, optional): Minimum frequency in Hz. Defaults to 50.0. f_max (Optional[float], optional): Maximum frequency in Hz. Defaults to None. htk (bool, optional): Use HTK formula in computing fbank matrix. Defaults to False. norm (Union[str, float], optional): Type of normalization in computing fbank matrix. Slaney-style is used by default. You can specify norm=1.0/2.0 to use customized p-norm normalization. Defaults to 'slaney'. ref_value (float, optional): The reference value. If smaller than 1.0, the db level of the signal will be pulled up accordingly. Otherwise, the db level is pushed down. Defaults to 1.0. amin (float, optional): The minimum value of input magnitude. Defaults to 1e-10. top_db (Optional[float], optional): The maximum db value of spectrogram. Defaults to None. dtype (str, optional): Data type of input and window. Defaults to 'float32'.

Methods

__call__(*inputs, **kwargs)	Call self as a function.
add_parameter(name, parameter)	Adds a Parameter instance.
add_sublayer(name, sublayer)	Adds a sub Layer instance.
apply(fn)	Applies fn recursively to every sublayer (as returned by .sublayers()) as well as self.
buffers([include_sublayers])	Returns a list of all buffers from current layer and its sub-layers.
children()	Returns an iterator over immediate children layers.
clear_gradients()	Clear the gradients of all parameters for this layer.
create_parameter(shape[, attr, dtype, ...])	Create parameters for this layer.
create_tensor([name, persistable, dtype])	Create Tensor for this layer.
create_variable([name, persistable, dtype])	Create Tensor for this layer.
eval()	Sets this Layer and all its sublayers to evaluation mode.
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(x)	Args:
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
load_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
named_buffers([prefix, include_sublayers])	Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor.
named_children()	Returns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself.
named_parameters([prefix, include_sublayers])	Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter.
named_sublayers([prefix, include_self, ...])	Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer.
parameters([include_sublayers])	Returns a list of all Parameters from current layer and its sub-layers.
register_buffer(name, tensor[, persistable])	Registers a tensor as buffer into the layer.
register_forward_post_hook(hook)	Register a forward post-hook for Layer.
register_forward_pre_hook(hook)	Register a forward pre-hook for Layer.
set_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
set_state_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
state_dict([destination, include_sublayers, ...])	Get all parameters and persistable buffers of current layer and its sub-layers.
sublayers([include_self])	Returns a list of sub layers.
to([device, dtype, blocking])	Cast the parameters and buffers of Layer by the give device, dtype and blocking.
to_static_state_dict([destination, ...])	Get all parameters and buffers of current layer and its sub-layers.
train()	Sets this Layer and all its sublayers to training mode.

backward
register_state_dict_hook

forward(x: Tensor) → Tensor

Args:

x (Tensor): Tensor of waveforms with shape (N, T)

Returns:

Tensor: Log mel spectrograms with shape (N, n_mels, num_frames).

class paddleaudio.features.layers.MFCC(sr: int = 22050, n_mfcc: int = 40, n_fft: int = 512, hop_length: Optional[int] = None, win_length: Optional[int] = None, window: str = 'hann', power: float = 2.0, center: bool = True, pad_mode: str = 'reflect', n_mels: int = 64, f_min: float = 50.0, f_max: Optional[float] = None, htk: bool = False, norm: Union[str, float] = 'slaney', ref_value: float = 1.0, amin: float = 1e-10, top_db: Optional[float] = None, dtype: str = paddle.float32)

Bases: Layer

Compute mel frequency cepstral coefficients(MFCCs) feature of given waveforms.

Args:

sr (int, optional): Sample rate. Defaults to 22050. n_mfcc (int, optional): [description]. Defaults to 40. n_fft (int, optional): The number of frequency components of the discrete Fourier transform. Defaults to 512. hop_length (Optional[int], optional): The hop length of the short time FFT. If None, it is set to win_length//4. Defaults to None. win_length (Optional[int], optional): The window length of the short time FFT. If None, it is set to same as n_fft. Defaults to None. window (str, optional): The window function applied to the signal before the Fourier transform. Supported window functions: 'hamming', 'hann', 'kaiser', 'gaussian', 'exponential', 'triang', 'bohman', 'blackman', 'cosine', 'tukey', 'taylor'. Defaults to 'hann'. power (float, optional): Exponent for the magnitude spectrogram. Defaults to 2.0. center (bool, optional): Whether to pad x to make that the \(t imes hop\_length\) at the center of t-th frame. Defaults to True. pad_mode (str, optional): Choose padding pattern when center is True. Defaults to 'reflect'. n_mels (int, optional): Number of mel bins. Defaults to 64. f_min (float, optional): Minimum frequency in Hz. Defaults to 50.0. f_max (Optional[float], optional): Maximum frequency in Hz. Defaults to None. htk (bool, optional): Use HTK formula in computing fbank matrix. Defaults to False. norm (Union[str, float], optional): Type of normalization in computing fbank matrix. Slaney-style is used by default. You can specify norm=1.0/2.0 to use customized p-norm normalization. Defaults to 'slaney'. ref_value (float, optional): The reference value. If smaller than 1.0, the db level of the signal will be pulled up accordingly. Otherwise, the db level is pushed down. Defaults to 1.0. amin (float, optional): The minimum value of input magnitude. Defaults to 1e-10. top_db (Optional[float], optional): The maximum db value of spectrogram. Defaults to None. dtype (str, optional): Data type of input and window. Defaults to 'float32'.

Methods

__call__(*inputs, **kwargs)	Call self as a function.
add_parameter(name, parameter)	Adds a Parameter instance.
add_sublayer(name, sublayer)	Adds a sub Layer instance.
apply(fn)	Applies fn recursively to every sublayer (as returned by .sublayers()) as well as self.
buffers([include_sublayers])	Returns a list of all buffers from current layer and its sub-layers.
children()	Returns an iterator over immediate children layers.
clear_gradients()	Clear the gradients of all parameters for this layer.
create_parameter(shape[, attr, dtype, ...])	Create parameters for this layer.
create_tensor([name, persistable, dtype])	Create Tensor for this layer.
create_variable([name, persistable, dtype])	Create Tensor for this layer.
eval()	Sets this Layer and all its sublayers to evaluation mode.
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(x)	Args:
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
load_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
named_buffers([prefix, include_sublayers])	Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor.
named_children()	Returns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself.
named_parameters([prefix, include_sublayers])	Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter.
named_sublayers([prefix, include_self, ...])	Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer.
parameters([include_sublayers])	Returns a list of all Parameters from current layer and its sub-layers.
register_buffer(name, tensor[, persistable])	Registers a tensor as buffer into the layer.
register_forward_post_hook(hook)	Register a forward post-hook for Layer.
register_forward_pre_hook(hook)	Register a forward pre-hook for Layer.
set_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
set_state_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
state_dict([destination, include_sublayers, ...])	Get all parameters and persistable buffers of current layer and its sub-layers.
sublayers([include_self])	Returns a list of sub layers.
to([device, dtype, blocking])	Cast the parameters and buffers of Layer by the give device, dtype and blocking.
to_static_state_dict([destination, ...])	Get all parameters and buffers of current layer and its sub-layers.
train()	Sets this Layer and all its sublayers to training mode.

backward
register_state_dict_hook

forward(x: Tensor) → Tensor

Args:

x (Tensor): Tensor of waveforms with shape (N, T)

Returns:

Tensor: Mel frequency cepstral coefficients with shape (N, n_mfcc, num_frames).

class paddleaudio.features.layers.MelSpectrogram(sr: int = 22050, n_fft: int = 512, hop_length: Optional[int] = None, win_length: Optional[int] = None, window: str = 'hann', power: float = 2.0, center: bool = True, pad_mode: str = 'reflect', n_mels: int = 64, f_min: float = 50.0, f_max: Optional[float] = None, htk: bool = False, norm: Union[str, float] = 'slaney', dtype: str = 'float32')

Bases: Layer

Compute the melspectrogram of given signals, typically audio waveforms. It is computed by multiplying spectrogram with Mel filter bank matrix.

Args:

sr (int, optional): Sample rate. Defaults to 22050. n_fft (int, optional): The number of frequency components of the discrete Fourier transform. Defaults to 512. hop_length (Optional[int], optional): The hop length of the short time FFT. If None, it is set to win_length//4. Defaults to None. win_length (Optional[int], optional): The window length of the short time FFT. If None, it is set to same as n_fft. Defaults to None. window (str, optional): The window function applied to the signal before the Fourier transform. Supported window functions: 'hamming', 'hann', 'kaiser', 'gaussian', 'exponential', 'triang', 'bohman', 'blackman', 'cosine', 'tukey', 'taylor'. Defaults to 'hann'. power (float, optional): Exponent for the magnitude spectrogram. Defaults to 2.0. center (bool, optional): Whether to pad x to make that the \(t imes hop\_length\) at the center of t-th frame. Defaults to True. pad_mode (str, optional): Choose padding pattern when center is True. Defaults to 'reflect'. n_mels (int, optional): Number of mel bins. Defaults to 64. f_min (float, optional): Minimum frequency in Hz. Defaults to 50.0. f_max (Optional[float], optional): Maximum frequency in Hz. Defaults to None. htk (bool, optional): Use HTK formula in computing fbank matrix. Defaults to False. norm (Union[str, float], optional): Type of normalization in computing fbank matrix. Slaney-style is used by default. You can specify norm=1.0/2.0 to use customized p-norm normalization. Defaults to 'slaney'. dtype (str, optional): Data type of input and window. Defaults to 'float32'.

Methods

__call__(*inputs, **kwargs)	Call self as a function.
add_parameter(name, parameter)	Adds a Parameter instance.
add_sublayer(name, sublayer)	Adds a sub Layer instance.
apply(fn)	Applies fn recursively to every sublayer (as returned by .sublayers()) as well as self.
buffers([include_sublayers])	Returns a list of all buffers from current layer and its sub-layers.
children()	Returns an iterator over immediate children layers.
clear_gradients()	Clear the gradients of all parameters for this layer.
create_parameter(shape[, attr, dtype, ...])	Create parameters for this layer.
create_tensor([name, persistable, dtype])	Create Tensor for this layer.
create_variable([name, persistable, dtype])	Create Tensor for this layer.
eval()	Sets this Layer and all its sublayers to evaluation mode.
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(x)	Args:
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
load_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
named_buffers([prefix, include_sublayers])	Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor.
named_children()	Returns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself.
named_parameters([prefix, include_sublayers])	Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter.
named_sublayers([prefix, include_self, ...])	Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer.
parameters([include_sublayers])	Returns a list of all Parameters from current layer and its sub-layers.
register_buffer(name, tensor[, persistable])	Registers a tensor as buffer into the layer.
register_forward_post_hook(hook)	Register a forward post-hook for Layer.
register_forward_pre_hook(hook)	Register a forward pre-hook for Layer.
set_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
set_state_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
state_dict([destination, include_sublayers, ...])	Get all parameters and persistable buffers of current layer and its sub-layers.
sublayers([include_self])	Returns a list of sub layers.
to([device, dtype, blocking])	Cast the parameters and buffers of Layer by the give device, dtype and blocking.
to_static_state_dict([destination, ...])	Get all parameters and buffers of current layer and its sub-layers.
train()	Sets this Layer and all its sublayers to training mode.

backward
register_state_dict_hook

forward(x: Tensor) → Tensor

Args:

x (Tensor): Tensor of waveforms with shape (N, T)

Returns:

Tensor: Mel spectrograms with shape (N, n_mels, num_frames).

class paddleaudio.features.layers.Spectrogram(n_fft: int = 512, hop_length: Optional[int] = None, win_length: Optional[int] = None, window: str = 'hann', power: float = 2.0, center: bool = True, pad_mode: str = 'reflect', dtype: str = 'float32')

Bases: Layer

Compute spectrogram of given signals, typically audio waveforms. The spectorgram is defined as the complex norm of the short-time Fourier transformation.

Args:

n_fft (int, optional): The number of frequency components of the discrete Fourier transform. Defaults to 512. hop_length (Optional[int], optional): The hop length of the short time FFT. If None, it is set to win_length//4. Defaults to None. win_length (Optional[int], optional): The window length of the short time FFT. If None, it is set to same as n_fft. Defaults to None. window (str, optional): The window function applied to the signal before the Fourier transform. Supported window functions: 'hamming', 'hann', 'kaiser', 'gaussian', 'exponential', 'triang', 'bohman', 'blackman', 'cosine', 'tukey', 'taylor'. Defaults to 'hann'. power (float, optional): Exponent for the magnitude spectrogram. Defaults to 2.0. center (bool, optional): Whether to pad x to make that the \(t imes hop\_length\) at the center of t-th frame. Defaults to True. pad_mode (str, optional): Choose padding pattern when center is True. Defaults to 'reflect'. dtype (str, optional): Data type of input and window. Defaults to 'float32'.

Methods

__call__(*inputs, **kwargs)	Call self as a function.
add_parameter(name, parameter)	Adds a Parameter instance.
add_sublayer(name, sublayer)	Adds a sub Layer instance.
apply(fn)	Applies fn recursively to every sublayer (as returned by .sublayers()) as well as self.
buffers([include_sublayers])	Returns a list of all buffers from current layer and its sub-layers.
children()	Returns an iterator over immediate children layers.
clear_gradients()	Clear the gradients of all parameters for this layer.
create_parameter(shape[, attr, dtype, ...])	Create parameters for this layer.
create_tensor([name, persistable, dtype])	Create Tensor for this layer.
create_variable([name, persistable, dtype])	Create Tensor for this layer.
eval()	Sets this Layer and all its sublayers to evaluation mode.
extra_repr()	Extra representation of this layer, you can have custom implementation of your own layer.
forward(x)	Args:
full_name()	Full name for this layer, composed by name_scope + "/" + MyLayer.__class__.__name__
load_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
named_buffers([prefix, include_sublayers])	Returns an iterator over all buffers in the Layer, yielding tuple of name and Tensor.
named_children()	Returns an iterator over immediate children layers, yielding both the name of the layer as well as the layer itself.
named_parameters([prefix, include_sublayers])	Returns an iterator over all parameters in the Layer, yielding tuple of name and parameter.
named_sublayers([prefix, include_self, ...])	Returns an iterator over all sublayers in the Layer, yielding tuple of name and sublayer.
parameters([include_sublayers])	Returns a list of all Parameters from current layer and its sub-layers.
register_buffer(name, tensor[, persistable])	Registers a tensor as buffer into the layer.
register_forward_post_hook(hook)	Register a forward post-hook for Layer.
register_forward_pre_hook(hook)	Register a forward pre-hook for Layer.
set_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
set_state_dict(state_dict[, use_structured_name])	Set parameters and persistable buffers from state_dict.
state_dict([destination, include_sublayers, ...])	Get all parameters and persistable buffers of current layer and its sub-layers.
sublayers([include_self])	Returns a list of sub layers.
to([device, dtype, blocking])	Cast the parameters and buffers of Layer by the give device, dtype and blocking.
to_static_state_dict([destination, ...])	Get all parameters and buffers of current layer and its sub-layers.
train()	Sets this Layer and all its sublayers to training mode.

backward
register_state_dict_hook

forward(x: Tensor) → Tensor

Args:

x (Tensor): Tensor of waveforms with shape (N, T)

Returns:

Tensor: Spectrograms with shape (N, n_fft//2 + 1, num_frames).