diff --git a/paddleaudio/paddleaudio/backends/soundfile_backend.py b/paddleaudio/paddleaudio/backends/soundfile_backend.py
index 2b920284..c1155654 100644
--- a/paddleaudio/paddleaudio/backends/soundfile_backend.py
+++ b/paddleaudio/paddleaudio/backends/soundfile_backend.py
@@ -11,6 +11,7 @@
 # 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 os
 import warnings
 from typing import Optional
 from typing import Tuple
@@ -19,7 +20,6 @@ from typing import Union
 import numpy as np
 import resampy
 import soundfile as sf
-from numpy import ndarray as array
 from scipy.io import wavfile
 
 from ..utils import ParameterError
@@ -38,13 +38,21 @@ RESAMPLE_MODES = ['kaiser_best', 'kaiser_fast']
 EPS = 1e-8
 
 
-def resample(y: array, src_sr: int, target_sr: int,
-             mode: str='kaiser_fast') -> array:
-    """ Audio resampling
-     This function is the same as using resampy.resample().
-     Notes:
-        The default mode is kaiser_fast.  For better audio quality, use mode = 'kaiser_fast'
-     """
+def resample(y: np.ndarray,
+             src_sr: int,
+             target_sr: int,
+             mode: str='kaiser_fast') -> np.ndarray:
+    """Audio resampling.
+
+    Args:
+        y (np.ndarray): Input waveform array in 1D or 2D.
+        src_sr (int): Source sample rate.
+        target_sr (int): Target sample rate.
+        mode (str, optional): The resampling filter to use. Defaults to 'kaiser_fast'.
+
+    Returns:
+        np.ndarray: `y` resampled to `target_sr`
+    """
 
     if mode == 'kaiser_best':
         warnings.warn(
@@ -53,7 +61,7 @@ def resample(y: array, src_sr: int, target_sr: int,
 
     if not isinstance(y, np.ndarray):
         raise ParameterError(
-            'Only support numpy array, but received y in {type(y)}')
+            'Only support numpy np.ndarray, but received y in {type(y)}')
 
     if mode not in RESAMPLE_MODES:
         raise ParameterError(f'resample mode must in {RESAMPLE_MODES}')
@@ -61,9 +69,17 @@ def resample(y: array, src_sr: int, target_sr: int,
     return resampy.resample(y, src_sr, target_sr, filter=mode)
 
 
-def to_mono(y: array, merge_type: str='average') -> array:
-    """ convert sterior audio to mono
+def to_mono(y: np.ndarray, merge_type: str='average') -> np.ndarray:
+    """Convert sterior audio to mono.
+
+    Args:
+        y (np.ndarray): Input waveform array in 1D or 2D.
+        merge_type (str, optional): Merge type to generate mono waveform. Defaults to 'average'.
+
+    Returns:
+        np.ndarray: `y` with mono channel.
     """
+
     if merge_type not in MERGE_TYPES:
         raise ParameterError(
             f'Unsupported merge type {merge_type}, available types are {MERGE_TYPES}'
@@ -101,18 +117,34 @@ def to_mono(y: array, merge_type: str='average') -> array:
     return y_out
 
 
-def _safe_cast(y: array, dtype: Union[type, str]) -> array:
-    """ data type casting in a safe way, i.e., prevent overflow or underflow
-    This function is used internally.
+def _safe_cast(y: np.ndarray, dtype: Union[type, str]) -> np.ndarray:
+    """Data type casting in a safe way, i.e., prevent overflow or underflow.
+
+    Args:
+        y (np.ndarray): Input waveform array in 1D or 2D.
+        dtype (Union[type, str]): Data type of waveform.
+
+    Returns:
+        np.ndarray: `y` after safe casting.
     """
-    return np.clip(y, np.iinfo(dtype).min, np.iinfo(dtype).max).astype(dtype)
+    if 'float' in str(y.dtype):
+        return np.clip(y, np.finfo(dtype).min,
+                       np.finfo(dtype).max).astype(dtype)
+    else:
+        return np.clip(y, np.iinfo(dtype).min,
+                       np.iinfo(dtype).max).astype(dtype)
 
 
-def depth_convert(y: array, dtype: Union[type, str],
-                  dithering: bool=True) -> array:
-    """Convert audio array to target dtype safely
-    This function convert audio waveform to a target dtype, with addition steps of
+def depth_convert(y: np.ndarray, dtype: Union[type, str]) -> np.ndarray:
+    """Convert audio array to target dtype safely. This function convert audio waveform to a target dtype, with addition steps of
     preventing overflow/underflow and preserving audio range.
+
+    Args:
+        y (np.ndarray): Input waveform array in 1D or 2D.
+        dtype (Union[type, str]): Data type of waveform.
+
+    Returns:
+        np.ndarray: `y` after safe casting.
     """
 
     SUPPORT_DTYPE = ['int16', 'int8', 'float32', 'float64']
@@ -157,14 +189,20 @@ def depth_convert(y: array, dtype: Union[type, str],
     return y
 
 
-def sound_file_load(file: str,
+def sound_file_load(file: os.PathLike,
                     offset: Optional[float]=None,
                     dtype: str='int16',
-                    duration: Optional[int]=None) -> Tuple[array, int]:
-    """Load audio using soundfile library
-    This function load audio file using libsndfile.
-    Reference:
-        http://www.mega-nerd.com/libsndfile/#Features
+                    duration: Optional[int]=None) -> Tuple[np.ndarray, int]:
+    """Load audio using soundfile library. This function load audio file using libsndfile.
+
+    Args:
+        file (os.PathLike): File of waveform.
+        offset (Optional[float], optional): Offset to the start of waveform. Defaults to None.
+        dtype (str, optional): Data type of waveform. Defaults to 'int16'.
+        duration (Optional[int], optional): Duration of waveform to read. Defaults to None.
+
+    Returns:
+        Tuple[np.ndarray, int]: Waveform in ndarray and its samplerate.
     """
     with sf.SoundFile(file) as sf_desc:
         sr_native = sf_desc.samplerate
@@ -179,9 +217,17 @@ def sound_file_load(file: str,
     return y, sf_desc.samplerate
 
 
-def normalize(y: array, norm_type: str='linear',
-              mul_factor: float=1.0) -> array:
-    """ normalize an input audio with additional multiplier.
+def normalize(y: np.ndarray, norm_type: str='linear',
+              mul_factor: float=1.0) -> np.ndarray:
+    """Normalize an input audio with additional multiplier.
+
+    Args:
+        y (np.ndarray): Input waveform array in 1D or 2D.
+        norm_type (str, optional): Type of normalization. Defaults to 'linear'.
+        mul_factor (float, optional): Scaling factor. Defaults to 1.0.
+
+    Returns:
+        np.ndarray: `y` after normalization.
     """
 
     if norm_type == 'linear':
@@ -199,12 +245,13 @@ def normalize(y: array, norm_type: str='linear',
     return y
 
 
-def save(y: array, sr: int, file: str) -> None:
-    """Save audio file to disk.
-    This function saves audio to disk using scipy.io.wavfile, with additional step
-    to convert input waveform to int16 unless it already is int16
-    Notes:
-        It only support raw wav format.
+def save(y: np.ndarray, sr: int, file: os.PathLike) -> None:
+    """Save audio file to disk. This function saves audio to disk using scipy.io.wavfile, with additional step to convert input waveform to int16.
+
+    Args:
+        y (np.ndarray): Input waveform array in 1D or 2D.
+        sr (int): Sample rate.
+        file (os.PathLike): Path of auido file to save.
     """
     if not file.endswith('.wav'):
         raise ParameterError(
@@ -226,7 +273,7 @@ def save(y: array, sr: int, file: str) -> None:
 
 
 def load(
-        file: str,
+        file: os.PathLike,
         sr: Optional[int]=None,
         mono: bool=True,
         merge_type: str='average',  # ch0,ch1,random,average
@@ -236,11 +283,24 @@ def load(
         offset: float=0.0,
         duration: Optional[int]=None,
         dtype: str='float32',
-        resample_mode: str='kaiser_fast') -> Tuple[array, int]:
-    """Load audio file from disk.
-    This function loads audio from disk using using audio beackend.
-    Parameters:
-    Notes:
+        resample_mode: str='kaiser_fast') -> Tuple[np.ndarray, int]:
+    """Load audio file from disk. This function loads audio from disk using using audio beackend.
+
+    Args:
+        file (os.PathLike): Path of auido file to load.
+        sr (Optional[int], optional): Sample rate of loaded waveform. Defaults to None.
+        mono (bool, optional): Return waveform with mono channel. Defaults to True.
+        merge_type (str, optional): Merge type of multi-channels waveform. Defaults to 'average'.
+        normal (bool, optional): Waveform normalization. Defaults to True.
+        norm_type (str, optional): Type of normalization. Defaults to 'linear'.
+        norm_mul_factor (float, optional): Scaling factor. Defaults to 1.0.
+        offset (float, optional): Offset to the start of waveform. Defaults to 0.0.
+        duration (Optional[int], optional): Duration of waveform to read. Defaults to None.
+        dtype (str, optional): Data type of waveform. Defaults to 'float32'.
+        resample_mode (str, optional): The resampling filter to use. Defaults to 'kaiser_fast'.
+
+    Returns:
+        Tuple[np.ndarray, int]: Waveform in ndarray and its samplerate.
     """
 
     y, r = sound_file_load(file, offset=offset, dtype=dtype, duration=duration)
diff --git a/paddleaudio/paddleaudio/compliance/kaldi.py b/paddleaudio/paddleaudio/compliance/kaldi.py
index 8cb9b666..538be019 100644
--- a/paddleaudio/paddleaudio/compliance/kaldi.py
+++ b/paddleaudio/paddleaudio/compliance/kaldi.py
@@ -220,7 +220,7 @@ def spectrogram(waveform: Tensor,
     """Compute and return a spectrogram from a waveform. The output is identical to Kaldi's.
 
     Args:
-        waveform (Tensor): A waveform tensor with shape [C, T].
+        waveform (Tensor): A waveform tensor with shape `(C, T)`.
         blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
         channel (int, optional): Select the channel of waveform. Defaults to -1.
         dither (float, optional): Dithering constant . Defaults to 0.0.
@@ -239,7 +239,7 @@ def spectrogram(waveform: Tensor,
         window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
 
     Returns:
-        Tensor: A spectrogram tensor with shape (m, padded_window_size // 2 + 1) where m is the number of frames
+        Tensor: A spectrogram tensor with shape `(m, padded_window_size // 2 + 1)` where m is the number of frames
             depends on frame_length and frame_shift.
     """
     dtype = waveform.dtype
@@ -422,7 +422,7 @@ def fbank(waveform: Tensor,
     """Compute and return filter banks from a waveform. The output is identical to Kaldi's.
 
     Args:
-        waveform (Tensor): A waveform tensor with shape [C, T].
+        waveform (Tensor): A waveform tensor with shape `(C, T)`.
         blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
         channel (int, optional): Select the channel of waveform. Defaults to -1.
         dither (float, optional): Dithering constant . Defaults to 0.0.
@@ -451,7 +451,7 @@ def fbank(waveform: Tensor,
         window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
 
     Returns:
-        Tensor: A filter banks tensor with shape (m, n_mels).
+        Tensor: A filter banks tensor with shape `(m, n_mels)`.
     """
     dtype = waveform.dtype
 
@@ -542,7 +542,7 @@ def mfcc(waveform: Tensor,
             identical to Kaldi's.
 
     Args:
-        waveform (Tensor): A waveform tensor with shape [C, T].
+        waveform (Tensor): A waveform tensor with shape `(C, T)`.
         blackman_coeff (float, optional): Coefficient for Blackman window.. Defaults to 0.42.
         cepstral_lifter (float, optional): Scaling of output mfccs. Defaults to 22.0.
         channel (int, optional): Select the channel of waveform. Defaults to -1.
@@ -571,7 +571,7 @@ def mfcc(waveform: Tensor,
         window_type (str, optional): Choose type of window for FFT computation. Defaults to POVEY.
 
     Returns:
-        Tensor: A mel frequency cepstral coefficients tensor with shape (m, n_mfcc).
+        Tensor: A mel frequency cepstral coefficients tensor with shape `(m, n_mfcc)`.
     """
     assert n_mfcc <= n_mels, 'n_mfcc cannot be larger than n_mels: %d vs %d' % (
         n_mfcc, n_mels)
diff --git a/paddleaudio/paddleaudio/compliance/librosa.py b/paddleaudio/paddleaudio/compliance/librosa.py
index 167795c3..d7ceb2b4 100644
--- a/paddleaudio/paddleaudio/compliance/librosa.py
+++ b/paddleaudio/paddleaudio/compliance/librosa.py
@@ -19,7 +19,6 @@ from typing import Union
 
 import numpy as np
 import scipy
-from numpy import ndarray as array
 from numpy.lib.stride_tricks import as_strided
 from scipy import signal
 
@@ -32,7 +31,6 @@ __all__ = [
     'mfcc',
     'hz_to_mel',
     'mel_to_hz',
-    'split_frames',
     'mel_frequencies',
     'power_to_db',
     'compute_fbank_matrix',
@@ -49,7 +47,8 @@ __all__ = [
 ]
 
 
-def pad_center(data: array, size: int, axis: int=-1, **kwargs) -> array:
+def _pad_center(data: np.ndarray, size: int, axis: int=-1,
+                **kwargs) -> np.ndarray:
     """Pad an array to a target length along a target axis.
 
     This differs from `np.pad` by centering the data prior to padding,
@@ -69,8 +68,10 @@ def pad_center(data: array, size: int, axis: int=-1, **kwargs) -> array:
     return np.pad(data, lengths, **kwargs)
 
 
-def split_frames(x: array, frame_length: int, hop_length: int,
-                 axis: int=-1) -> array:
+def _split_frames(x: np.ndarray,
+                  frame_length: int,
+                  hop_length: int,
+                  axis: int=-1) -> np.ndarray:
     """Slice a data array into (overlapping) frames.
 
     This function is aligned with librosa.frame
@@ -142,11 +143,16 @@ def _check_audio(y, mono=True) -> bool:
     return True
 
 
-def hz_to_mel(frequencies: Union[float, List[float], array],
-              htk: bool=False) -> array:
-    """Convert Hz to Mels
+def hz_to_mel(frequencies: Union[float, List[float], np.ndarray],
+              htk: bool=False) -> np.ndarray:
+    """Convert Hz to Mels.
 
-    This function is aligned with librosa.
+    Args:
+        frequencies (Union[float, List[float], np.ndarray]): Frequencies in Hz.
+        htk (bool, optional): Use htk scaling. Defaults to False.
+
+    Returns:
+        np.ndarray: Frequency in mels.
     """
     freq = np.asanyarray(frequencies)
 
@@ -177,10 +183,16 @@ def hz_to_mel(frequencies: Union[float, List[float], array],
     return mels
 
 
-def mel_to_hz(mels: Union[float, List[float], array], htk: int=False) -> array:
+def mel_to_hz(mels: Union[float, List[float], np.ndarray],
+              htk: int=False) -> np.ndarray:
     """Convert mel bin numbers to frequencies.
 
-    This function is aligned with librosa.
+    Args:
+        mels (Union[float, List[float], np.ndarray]): Frequency in mels.
+        htk (bool, optional): Use htk scaling. Defaults to False.
+
+    Returns:
+        np.ndarray: Frequencies in Hz.
     """
     mel_array = np.asanyarray(mels)
 
@@ -212,10 +224,17 @@ def mel_to_hz(mels: Union[float, List[float], array], htk: int=False) -> array:
 def mel_frequencies(n_mels: int=128,
                     fmin: float=0.0,
                     fmax: float=11025.0,
-                    htk: bool=False) -> array:
-    """Compute mel frequencies
+                    htk: bool=False) -> np.ndarray:
+    """Compute mel frequencies.
+
+    Args:
+        n_mels (int, optional): Number of mel bins. Defaults to 128.
+        fmin (float, optional): Minimum frequency in Hz. Defaults to 0.0.
+        fmax (float, optional): Maximum frequency in Hz. Defaults to 11025.0.
+        htk (bool, optional): Use htk scaling. Defaults to False.
 
-    This function is aligned with librosa.
+    Returns:
+        np.ndarray: Vector of n_mels frequencies in Hz with shape `(n_mels,)`.
     """
     # 'Center freqs' of mel bands - uniformly spaced between limits
     min_mel = hz_to_mel(fmin, htk=htk)
@@ -226,10 +245,15 @@ def mel_frequencies(n_mels: int=128,
     return mel_to_hz(mels, htk=htk)
 
 
-def fft_frequencies(sr: int, n_fft: int) -> array:
+def fft_frequencies(sr: int, n_fft: int) -> np.ndarray:
     """Compute fourier frequencies.
 
-    This function is aligned with librosa.
+    Args:
+        sr (int): Sample rate.
+        n_fft (int): FFT size.
+
+    Returns:
+        np.ndarray: FFT frequencies in Hz with shape `(n_fft//2 + 1,)`.
     """
     return np.linspace(0, float(sr) / 2, int(1 + n_fft // 2), endpoint=True)
 
@@ -241,10 +265,22 @@ def compute_fbank_matrix(sr: int,
                          fmax: Optional[float]=None,
                          htk: bool=False,
                          norm: str="slaney",
-                         dtype: type=np.float32):
+                         dtype: type=np.float32) -> np.ndarray:
     """Compute fbank matrix.
 
-    This funciton is aligned with librosa.
+    Args:
+        sr (int): Sample rate.
+        n_fft (int): FFT size.
+        n_mels (int, optional): Number of mel bins. Defaults to 128.
+        fmin (float, optional): Minimum frequency in Hz. Defaults to 0.0.
+        fmax (Optional[float], optional): Maximum frequency in Hz. Defaults to None.
+        htk (bool, optional): Use htk scaling. Defaults to False.
+        norm (str, optional): Type of normalization. Defaults to "slaney".
+        dtype (type, optional): Data type. Defaults to np.float32.
+
+
+    Returns:
+        np.ndarray: Mel transform matrix with shape `(n_mels, n_fft//2 + 1)`.
     """
     if norm != "slaney":
         raise ParameterError('norm must set to slaney')
@@ -289,17 +325,28 @@ def compute_fbank_matrix(sr: int,
     return weights
 
 
-def stft(x: array,
+def stft(x: np.ndarray,
          n_fft: int=2048,
          hop_length: Optional[int]=None,
          win_length: Optional[int]=None,
          window: str="hann",
          center: bool=True,
          dtype: type=np.complex64,
-         pad_mode: str="reflect") -> array:
+         pad_mode: str="reflect") -> np.ndarray:
     """Short-time Fourier transform (STFT).
 
-    This function is aligned with librosa.
+    Args:
+        x (np.ndarray): Input waveform in one dimension.
+        n_fft (int, optional): FFT size. Defaults to 2048.
+        hop_length (Optional[int], optional): Number of steps to advance between adjacent windows. Defaults to None.
+        win_length (Optional[int], optional): The size of window. Defaults to None.
+        window (str, optional): A string of window specification. Defaults to "hann".
+        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.
+        dtype (type, optional): Data type of STFT results. Defaults to np.complex64.
+        pad_mode (str, optional): Choose padding pattern when `center` is `True`. Defaults to "reflect".
+
+    Returns:
+        np.ndarray: The complex STFT output with shape `(n_fft//2 + 1, num_frames)`
     """
     _check_audio(x)
 
@@ -314,7 +361,7 @@ def stft(x: array,
     fft_window = signal.get_window(window, win_length, fftbins=True)
 
     # Pad the window out to n_fft size
-    fft_window = pad_center(fft_window, n_fft)
+    fft_window = _pad_center(fft_window, n_fft)
 
     # Reshape so that the window can be broadcast
     fft_window = fft_window.reshape((-1, 1))
@@ -333,7 +380,7 @@ def stft(x: array,
         )
 
     # Window the time series.
-    x_frames = split_frames(x, frame_length=n_fft, hop_length=hop_length)
+    x_frames = _split_frames(x, frame_length=n_fft, hop_length=hop_length)
     # Pre-allocate the STFT matrix
     stft_matrix = np.empty(
         (int(1 + n_fft // 2), x_frames.shape[1]), dtype=dtype, order="F")
@@ -352,16 +399,20 @@ def stft(x: array,
     return stft_matrix
 
 
-def power_to_db(spect: array,
+def power_to_db(spect: np.ndarray,
                 ref: float=1.0,
                 amin: float=1e-10,
-                top_db: Optional[float]=80.0) -> array:
-    """Convert a power spectrogram (amplitude squared) to decibel (dB) units
+                top_db: Optional[float]=80.0) -> np.ndarray:
+    """Convert a power spectrogram (amplitude squared) to decibel (dB) units.  This computes the scaling `10 * log10(spect / ref)` in a numerically stable way.
 
-    This computes the scaling ``10 * log10(spect / ref)`` in a numerically
-    stable way.
+    Args:
+        spect (np.ndarray): STFT power spectrogram of an input waveform.
+        ref (float, optional): Scaling factor of spectrogram. Defaults to 1.0.
+        amin (float, optional): Minimum threshold. Defaults to 1e-10.
+        top_db (Optional[float], optional): Threshold the output at `top_db` below the peak. Defaults to 80.0.
 
-    This function is aligned with librosa.
+    Returns:
+        np.ndarray: Power spectrogram in db scale.
     """
     spect = np.asarray(spect)
 
@@ -394,49 +445,27 @@ def power_to_db(spect: array,
     return log_spec
 
 
-def mfcc(x,
+def mfcc(x: np.ndarray,
          sr: int=16000,
-         spect: Optional[array]=None,
+         spect: Optional[np.ndarray]=None,
          n_mfcc: int=20,
          dct_type: int=2,
          norm: str="ortho",
          lifter: int=0,
-         **kwargs) -> array:
+         **kwargs) -> np.ndarray:
     """Mel-frequency cepstral coefficients (MFCCs)
 
-    This function is NOT strictly aligned with librosa. The following example shows how to get the
-    same result with librosa:
-
-    # mfcc:
-     kwargs = {
-        'window_size':512,
-        'hop_length':320,
-        'mel_bins':64,
-        'fmin':50,
-         'to_db':False}
-    a = mfcc(x,
-        spect=None,
-        n_mfcc=20,
-        dct_type=2,
-        norm='ortho',
-        lifter=0,
-        **kwargs)
-
-    # librosa mfcc:
-    spect = librosa.feature.melspectrogram(y=x,sr=16000,n_fft=512,
-                                              win_length=512,
-                                              hop_length=320,
-                                              n_mels=64, fmin=50)
-    b = librosa.feature.mfcc(y=x,
-        sr=16000,
-        S=spect,
-        n_mfcc=20,
-        dct_type=2,
-        norm='ortho',
-        lifter=0)
-
-    assert np.mean( (a-b)**2) < 1e-8
+    Args:
+        x (np.ndarray): Input waveform in one dimension.
+        sr (int, optional): Sample rate. Defaults to 16000.
+        spect (Optional[np.ndarray], optional): Input log-power Mel spectrogram. Defaults to None.
+        n_mfcc (int, optional): Number of cepstra in MFCC. Defaults to 20.
+        dct_type (int, optional): Discrete cosine transform (DCT) type. Defaults to 2.
+        norm (str, optional): Type of normalization. Defaults to "ortho".
+        lifter (int, optional): Cepstral filtering. Defaults to 0.
 
+    Returns:
+        np.ndarray: A mel frequency cepstral coefficients tensor with shape `(n_mfcc, num_frames)`.
     """
     if spect is None:
         spect = melspectrogram(x, sr=sr, **kwargs)
@@ -454,12 +483,12 @@ def mfcc(x,
             f"MFCC lifter={lifter} must be a non-negative number")
 
 
-def melspectrogram(x: array,
+def melspectrogram(x: np.ndarray,
                    sr: int=16000,
                    window_size: int=512,
                    hop_length: int=320,
                    n_mels: int=64,
-                   fmin: int=50,
+                   fmin: float=50.0,
                    fmax: Optional[float]=None,
                    window: str='hann',
                    center: bool=True,
@@ -468,27 +497,28 @@ def melspectrogram(x: array,
                    to_db: bool=True,
                    ref: float=1.0,
                    amin: float=1e-10,
-                   top_db: Optional[float]=None) -> array:
+                   top_db: Optional[float]=None) -> np.ndarray:
     """Compute mel-spectrogram.
 
-    Parameters:
-        x: numpy.ndarray
-        The input wavform is a numpy array [shape=(n,)]
-
-        window_size: int, typically 512, 1024, 2048, etc.
-        The window size for framing, also used as n_fft for stft
-
+    Args:
+        x (np.ndarray): Input waveform in one dimension.
+        sr (int, optional): Sample rate. Defaults to 16000.
+        window_size (int, optional): Size of FFT and window length. Defaults to 512.
+        hop_length (int, optional): Number of steps to advance between adjacent windows. Defaults to 320.
+        n_mels (int, optional): Number of mel bins. Defaults to 64.
+        fmin (float, optional): Minimum frequency in Hz. Defaults to 50.0.
+        fmax (Optional[float], optional): Maximum frequency in Hz. Defaults to None.
+        window (str, optional): A string of window specification. Defaults to "hann".
+        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".
+        power (float, optional): Exponent for the magnitude melspectrogram. Defaults to 2.0.
+        to_db (bool, optional): Enable db scale. Defaults to True.
+        ref (float, optional): Scaling factor of spectrogram. Defaults to 1.0.
+        amin (float, optional): Minimum threshold. Defaults to 1e-10.
+        top_db (Optional[float], optional): Threshold the output at `top_db` below the peak. Defaults to None.
 
     Returns:
-        The mel-spectrogram in power scale or db scale(default)
-
-
-    Notes:
-    1. sr is default to 16000, which is commonly used in speech/speaker processing.
-    2. when fmax is None, it is set to sr//2.
-    3. this function will convert mel spectgrum to db scale by default. This is different
-    that of librosa.
-
+        np.ndarray: The mel-spectrogram in power scale or db scale with shape `(n_mels, num_frames)`.
     """
     _check_audio(x, mono=True)
     if len(x) <= 0:
@@ -518,18 +548,28 @@ def melspectrogram(x: array,
         return mel_spect
 
 
-def spectrogram(x: array,
+def spectrogram(x: np.ndarray,
                 sr: int=16000,
                 window_size: int=512,
                 hop_length: int=320,
                 window: str='hann',
                 center: bool=True,
                 pad_mode: str='reflect',
-                power: float=2.0) -> array:
-    """Compute spectrogram from an input waveform.
+                power: float=2.0) -> np.ndarray:
+    """Compute spectrogram.
+
+    Args:
+        x (np.ndarray): Input waveform in one dimension.
+        sr (int, optional): Sample rate. Defaults to 16000.
+        window_size (int, optional): Size of FFT and window length. Defaults to 512.
+        hop_length (int, optional): Number of steps to advance between adjacent windows. Defaults to 320.
+        window (str, optional): A string of window specification. Defaults to "hann".
+        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".
+        power (float, optional): Exponent for the magnitude melspectrogram. Defaults to 2.0.
 
-    This function is a wrapper for librosa.feature.stft, with addition step to
-    compute the magnitude of the complex spectrogram.
+    Returns:
+        np.ndarray: The STFT spectrogram in power scale `(n_fft//2 + 1, num_frames)`.
     """
 
     s = stft(
@@ -544,18 +584,16 @@ def spectrogram(x: array,
     return np.abs(s)**power
 
 
-def mu_encode(x: array, mu: int=255, quantized: bool=True) -> array:
-    """Mu-law encoding.
-
-    Compute the mu-law decoding given an input code.
-    When quantized is True, the result will be converted to
-    integer in range [0,mu-1]. Otherwise, the resulting signal
-    is in range [-1,1]
-
+def mu_encode(x: np.ndarray, mu: int=255, quantized: bool=True) -> np.ndarray:
+    """Mu-law encoding. Encode waveform based on mu-law companding. When quantized is True, the result will be converted to integer in range `[0,mu-1]`. Otherwise, the resulting waveform is in range `[-1,1]`.
 
-    Reference:
-        https://en.wikipedia.org/wiki/%CE%9C-law_algorithm
+    Args:
+        x (np.ndarray): The input waveform to encode.
+        mu (int, optional): The endoceding parameter. Defaults to 255.
+        quantized (bool, optional): If `True`, quantize the encoded values into `1 + mu` distinct integer values. Defaults to True.
 
+    Returns:
+        np.ndarray: The mu-law encoded waveform.
     """
     mu = 255
     y = np.sign(x) * np.log1p(mu * np.abs(x)) / np.log1p(mu)
@@ -564,17 +602,16 @@ def mu_encode(x: array, mu: int=255, quantized: bool=True) -> array:
     return y
 
 
-def mu_decode(y: array, mu: int=255, quantized: bool=True) -> array:
-    """Mu-law decoding.
-
-    Compute the mu-law decoding given an input code.
+def mu_decode(y: np.ndarray, mu: int=255, quantized: bool=True) -> np.ndarray:
+    """Mu-law decoding. Compute the mu-law decoding given an input code. It assumes that the input `y` is in range `[0,mu-1]` when quantize is True and `[-1,1]` otherwise.
 
-    it assumes that the input y is in
-    range [0,mu-1] when quantize is True and [-1,1] otherwise
-
-    Reference:
-        https://en.wikipedia.org/wiki/%CE%9C-law_algorithm
+    Args:
+        y (np.ndarray): The encoded waveform.
+        mu (int, optional): The endoceding parameter. Defaults to 255.
+        quantized (bool, optional): If `True`, the input is assumed to be quantized to `1 + mu` distinct integer values. Defaults to True.
 
+    Returns:
+        np.ndarray: The mu-law decoded waveform.
     """
     if mu < 1:
         raise ParameterError('mu is typically set as 2**k-1, k=1, 2, 3,...')
@@ -586,7 +623,7 @@ def mu_decode(y: array, mu: int=255, quantized: bool=True) -> array:
     return x
 
 
-def randint(high: int) -> int:
+def _randint(high: int) -> int:
     """Generate one random integer in range [0 high)
 
      This is a helper function for random data augmentaiton
@@ -594,20 +631,18 @@ def randint(high: int) -> int:
     return int(np.random.randint(0, high=high))
 
 
-def rand() -> float:
-    """Generate one floating-point number in range [0 1)
-
-    This is a helper function for random data augmentaiton
-    """
-    return float(np.random.rand(1))
-
-
-def depth_augment(y: array,
+def depth_augment(y: np.ndarray,
                   choices: List=['int8', 'int16'],
-                  probs: List[float]=[0.5, 0.5]) -> array:
-    """ Audio depth augmentation
+                  probs: List[float]=[0.5, 0.5]) -> np.ndarray:
+    """ Audio depth augmentation. Do audio depth augmentation to simulate the distortion brought by quantization.
+
+    Args:
+        y (np.ndarray): Input waveform array in 1D or 2D.
+        choices (List, optional): A list of data type to depth conversion. Defaults to ['int8', 'int16'].
+        probs (List[float], optional): Probabilities to depth conversion. Defaults to [0.5, 0.5].
 
-    Do audio depth augmentation to simulate the distortion brought by quantization.
+    Returns:
+        np.ndarray: The augmented waveform.
     """
     assert len(probs) == len(
         choices
@@ -621,13 +656,18 @@ def depth_augment(y: array,
     return y2
 
 
-def adaptive_spect_augment(spect: array, tempo_axis: int=0,
-                           level: float=0.1) -> array:
-    """Do adpative spectrogram augmentation
+def adaptive_spect_augment(spect: np.ndarray,
+                           tempo_axis: int=0,
+                           level: float=0.1) -> np.ndarray:
+    """Do adpative spectrogram augmentation. The level of the augmentation is gowern by the paramter level, ranging from 0 to 1, with 0 represents no augmentation.
 
-    The level of the augmentation is gowern by the paramter level,
-    ranging from 0 to 1, with 0 represents no augmentation。
+    Args:
+        spect (np.ndarray): Input spectrogram.
+        tempo_axis (int, optional): Indicate the tempo axis. Defaults to 0.
+        level (float, optional): The level factor of masking. Defaults to 0.1.
 
+    Returns:
+        np.ndarray: The augmented spectrogram.
     """
     assert spect.ndim == 2., 'only supports 2d tensor or numpy array'
     if tempo_axis == 0:
@@ -643,32 +683,40 @@ def adaptive_spect_augment(spect: array, tempo_axis: int=0,
 
     if tempo_axis == 0:
         for _ in range(num_time_mask):
-            start = randint(nt - time_mask_width)
+            start = _randint(nt - time_mask_width)
             spect[start:start + time_mask_width, :] = 0
         for _ in range(num_freq_mask):
-            start = randint(nf - freq_mask_width)
+            start = _randint(nf - freq_mask_width)
             spect[:, start:start + freq_mask_width] = 0
     else:
         for _ in range(num_time_mask):
-            start = randint(nt - time_mask_width)
+            start = _randint(nt - time_mask_width)
             spect[:, start:start + time_mask_width] = 0
         for _ in range(num_freq_mask):
-            start = randint(nf - freq_mask_width)
+            start = _randint(nf - freq_mask_width)
             spect[start:start + freq_mask_width, :] = 0
 
     return spect
 
 
-def spect_augment(spect: array,
+def spect_augment(spect: np.ndarray,
                   tempo_axis: int=0,
                   max_time_mask: int=3,
                   max_freq_mask: int=3,
                   max_time_mask_width: int=30,
-                  max_freq_mask_width: int=20) -> array:
-    """Do spectrogram augmentation in both time and freq axis
+                  max_freq_mask_width: int=20) -> np.ndarray:
+    """Do spectrogram augmentation in both time and freq axis.
 
-    Reference:
+    Args:
+        spect (np.ndarray): Input spectrogram.
+        tempo_axis (int, optional): Indicate the tempo axis. Defaults to 0.
+        max_time_mask (int, optional): Maximum number of time masking. Defaults to 3.
+        max_freq_mask (int, optional): Maximum number of frenquence masking. Defaults to 3.
+        max_time_mask_width (int, optional): Maximum width of time masking. Defaults to 30.
+        max_freq_mask_width (int, optional): Maximum width of frenquence masking. Defaults to 20.
 
+    Returns:
+        np.ndarray: The augmented spectrogram.
     """
     assert spect.ndim == 2., 'only supports 2d tensor or numpy array'
     if tempo_axis == 0:
@@ -676,52 +724,64 @@ def spect_augment(spect: array,
     else:
         nf, nt = spect.shape
 
-    num_time_mask = randint(max_time_mask)
-    num_freq_mask = randint(max_freq_mask)
+    num_time_mask = _randint(max_time_mask)
+    num_freq_mask = _randint(max_freq_mask)
 
-    time_mask_width = randint(max_time_mask_width)
-    freq_mask_width = randint(max_freq_mask_width)
+    time_mask_width = _randint(max_time_mask_width)
+    freq_mask_width = _randint(max_freq_mask_width)
 
     if tempo_axis == 0:
         for _ in range(num_time_mask):
-            start = randint(nt - time_mask_width)
+            start = _randint(nt - time_mask_width)
             spect[start:start + time_mask_width, :] = 0
         for _ in range(num_freq_mask):
-            start = randint(nf - freq_mask_width)
+            start = _randint(nf - freq_mask_width)
             spect[:, start:start + freq_mask_width] = 0
     else:
         for _ in range(num_time_mask):
-            start = randint(nt - time_mask_width)
+            start = _randint(nt - time_mask_width)
             spect[:, start:start + time_mask_width] = 0
         for _ in range(num_freq_mask):
-            start = randint(nf - freq_mask_width)
+            start = _randint(nf - freq_mask_width)
             spect[start:start + freq_mask_width, :] = 0
 
     return spect
 
 
-def random_crop1d(y: array, crop_len: int) -> array:
-    """ Do random cropping on 1d input signal
+def random_crop1d(y: np.ndarray, crop_len: int) -> np.ndarray:
+    """ Random cropping on a input waveform.
 
-    The input is a 1d signal, typically a sound waveform
+    Args:
+        y (np.ndarray): Input waveform array in 1D.
+        crop_len (int): Length of waveform to crop.
+
+    Returns:
+        np.ndarray: The cropped waveform.
     """
     if y.ndim != 1:
         'only accept 1d tensor or numpy array'
     n = len(y)
-    idx = randint(n - crop_len)
+    idx = _randint(n - crop_len)
     return y[idx:idx + crop_len]
 
 
-def random_crop2d(s: array, crop_len: int, tempo_axis: int=0) -> array:
-    """ Do random cropping for 2D array, typically a spectrogram.
+def random_crop2d(s: np.ndarray, crop_len: int,
+                  tempo_axis: int=0) -> np.ndarray:
+    """ Random cropping on a spectrogram.
 
-    The cropping is done in temporal direction on the time-freq input signal.
+    Args:
+        s (np.ndarray): Input spectrogram in 2D.
+        crop_len (int): Length of spectrogram to crop.
+        tempo_axis (int, optional): Indicate the tempo axis. Defaults to 0.
+
+    Returns:
+        np.ndarray: The cropped spectrogram.
     """
     if tempo_axis >= s.ndim:
         raise ParameterError('axis out of range')
 
     n = s.shape[tempo_axis]
-    idx = randint(high=n - crop_len)
+    idx = _randint(high=n - crop_len)
     sli = [slice(None) for i in range(s.ndim)]
     sli[tempo_axis] = slice(idx, idx + crop_len)
     out = s[tuple(sli)]
diff --git a/paddleaudio/paddleaudio/features/layers.py b/paddleaudio/paddleaudio/features/layers.py
index 6afd234a..877a5ae8 100644
--- a/paddleaudio/paddleaudio/features/layers.py
+++ b/paddleaudio/paddleaudio/features/layers.py
@@ -44,29 +44,16 @@ class Spectrogram(nn.Layer):
         """Compute spectrogram of a given signal, typically an audio waveform.
         The spectorgram is defined as the complex norm of the short-time
         Fourier transformation.
-        Parameters:
-            n_fft (int): the number of frequency components of the discrete Fourier transform.
-                The default value is 2048,
-            hop_length (int|None): the hop length of the short time FFT. If None, it is set to win_length//4.
-                The default value is None.
-            win_length: the window length of the short time FFt. If None, it is set to same as n_fft.
-                The default value is None.
-            window (str): the name of the window function applied to the single before the Fourier transform.
-                The folllowing window names are supported: 'hamming','hann','kaiser','gaussian',
-                'exponential','triang','bohman','blackman','cosine','tukey','taylor'.
-                The default value is 'hann'
-            power (float): Exponent for the magnitude spectrogram. The default value is 2.0.
-            center (bool): if True, the signal is padded so that frame t is centered at x[t * hop_length].
-                If False, frame t begins at x[t * hop_length]
-                The default value is True
-            pad_mode (str): the mode to pad the signal if necessary. The supported modes are 'reflect'
-                and 'constant'. The default value is 'reflect'.
-            dtype (str): the data type of input and window.
-        Notes:
-            The Spectrogram transform relies on STFT transform to compute the spectrogram.
-            By default, the weights are not learnable. To fine-tune the Fourier coefficients,
-            set stop_gradient=False before training.
-            For more information, see STFT().
+
+        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 single 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 paddle.float32.
         """
         super(Spectrogram, self).__init__()