import paddle from paddle import nn from paddlespeech.audio.compliance import kaldi from paddlespeech.s2t.utils.log import Log logger = Log(__name__).getlog() __all__ = ['KaldiFbank'] class KaldiFbank(nn.Layer): def __init__( self, fs=16000, n_mels=80, n_shift=160, # unit:sample, 10ms win_length=400, # unit:sample, 25ms energy_floor=0.0, dither=0.0): """ Args: fs (int): sample rate of the audio n_mels (int): number of mel filter banks n_shift (int): number of points in a frame shift win_length (int): number of points in a frame windows energy_floor (float): Floor on energy in Spectrogram computation (absolute) dither (float): Dithering constant. Default 0.0 """ super().__init__() self.fs = fs self.n_mels = n_mels num_point_ms = fs / 1000 self.n_frame_length = win_length / num_point_ms self.n_frame_shift = n_shift / num_point_ms self.energy_floor = energy_floor self.dither = dither def __repr__(self): return ( "{name}(fs={fs}, n_mels={n_mels}, " "n_frame_shift={n_frame_shift}, n_frame_length={n_frame_length}, " "dither={dither}))".format( name=self.__class__.__name__, fs=self.fs, n_mels=self.n_mels, n_frame_shift=self.n_frame_shift, n_frame_length=self.n_frame_length, dither=self.dither, )) def forward(self, x: paddle.Tensor): """ Args: x (paddle.Tensor): shape (Ti). Not support: [Time, Channel] and Batch mode. Returns: paddle.Tensor: (T, D) """ assert x.ndim == 1 feat = kaldi.fbank( x.unsqueeze(0), # append channel dim, (C, Ti) n_mels=self.n_mels, frame_length=self.n_frame_length, frame_shift=self.n_frame_shift, dither=self.dither, energy_floor=self.energy_floor, sr=self.fs) assert feat.ndim == 2 # (T,D) return feat