# 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 functools import partial from typing import Optional from typing import Union import paddle import paddle.nn as nn from paddle import Tensor from ..functional import compute_fbank_matrix from ..functional import create_dct from ..functional import power_to_db from ..functional.window import get_window class Spectrogram(nn.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 :math:`t \times 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'. Returns: :ref:`api_paddle_nn_Layer`. An instance of Spectrogram. Examples: .. code-block:: python import paddle from paddle.audio.features import Spectrogram 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]) feature_extractor = Spectrogram(n_fft=512, window = 'hann', power = 1.0) feats = feature_extractor(waveform) """ def __init__(self, n_fft: int = 512, hop_length: Optional[int] = 512, win_length: Optional[int] = None, window: str = 'hann', power: float = 1.0, center: bool = True, pad_mode: str = 'reflect', dtype: str = 'float32') -> None: super(Spectrogram, self).__init__() assert power > 0, 'Power of spectrogram must be > 0.' self.power = power if win_length is None: win_length = n_fft self.fft_window = get_window(window, win_length, fftbins=True, dtype=dtype) self._stft = partial(paddle.signal.stft, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=self.fft_window, center=center, pad_mode=pad_mode) self.register_buffer('fft_window', self.fft_window) def forward(self, 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)`. """ stft = self._stft(x) spectrogram = paddle.pow(paddle.abs(stft), self.power) return spectrogram class MelSpectrogram(nn.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 :math:`t \times 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'. Returns: :ref:`api_paddle_nn_Layer`. An instance of MelSpectrogram. Examples: .. code-block:: python import paddle from paddle.audio.features import MelSpectrogram 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]) feature_extractor = MelSpectrogram(sr=sample_rate, n_fft=512, window = 'hann', power = 1.0) feats = feature_extractor(waveform) """ def __init__(self, sr: int = 22050, n_fft: int = 2048, hop_length: Optional[int] = 512, 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') -> None: super(MelSpectrogram, self).__init__() self._spectrogram = Spectrogram(n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, power=power, center=center, pad_mode=pad_mode, dtype=dtype) self.n_mels = n_mels self.f_min = f_min self.f_max = f_max self.htk = htk self.norm = norm if f_max is None: f_max = sr // 2 self.fbank_matrix = compute_fbank_matrix(sr=sr, n_fft=n_fft, n_mels=n_mels, f_min=f_min, f_max=f_max, htk=htk, norm=norm, dtype=dtype) self.register_buffer('fbank_matrix', self.fbank_matrix) def forward(self, x: Tensor) -> Tensor: """ Args: x (Tensor): Tensor of waveforms with shape `(N, T)` Returns: Tensor: Mel spectrograms with shape `(N, n_mels, num_frames)`. """ spect_feature = self._spectrogram(x) mel_feature = paddle.matmul(self.fbank_matrix, spect_feature) return mel_feature class LogMelSpectrogram(nn.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 :math:`t \times 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'. Returns: :ref:`api_paddle_nn_Layer`. An instance of LogMelSpectrogram. Examples: .. code-block:: python import paddle from paddle.audio.features import LogMelSpectrogram 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]) feature_extractor = LogMelSpectrogram(sr=sample_rate, n_fft=512, window = 'hann', power = 1.0) feats = feature_extractor(waveform) """ def __init__(self, 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') -> None: super(LogMelSpectrogram, self).__init__() self._melspectrogram = MelSpectrogram(sr=sr, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, power=power, center=center, pad_mode=pad_mode, n_mels=n_mels, f_min=f_min, f_max=f_max, htk=htk, norm=norm, dtype=dtype) self.ref_value = ref_value self.amin = amin self.top_db = top_db def forward(self, 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)`. """ mel_feature = self._melspectrogram(x) log_mel_feature = power_to_db(mel_feature, ref_value=self.ref_value, amin=self.amin, top_db=self.top_db) return log_mel_feature class MFCC(nn.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 :math:`t \times 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'. Returns: :ref:`api_paddle_nn_Layer`. An instance of MFCC. Examples: .. code-block:: python import paddle from paddle.audio.features import MFCC 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]) feature_extractor = MFCC(sr=sample_rate, n_fft=512, window = 'hann') feats = feature_extractor(waveform) """ def __init__(self, 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 = 'float32') -> None: super(MFCC, self).__init__() assert n_mfcc <= n_mels, 'n_mfcc cannot be larger than n_mels: %d vs %d' % ( n_mfcc, n_mels) self._log_melspectrogram = LogMelSpectrogram(sr=sr, n_fft=n_fft, hop_length=hop_length, win_length=win_length, window=window, power=power, center=center, pad_mode=pad_mode, n_mels=n_mels, f_min=f_min, f_max=f_max, htk=htk, norm=norm, ref_value=ref_value, amin=amin, top_db=top_db, dtype=dtype) self.dct_matrix = create_dct(n_mfcc=n_mfcc, n_mels=n_mels, dtype=dtype) self.register_buffer('dct_matrix', self.dct_matrix) def forward(self, 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)`. """ log_mel_feature = self._log_melspectrogram(x) mfcc = paddle.matmul(log_mel_feature.transpose( (0, 2, 1)), self.dct_matrix).transpose((0, 2, 1)) # (B, n_mels, L) return mfcc