Source code for

from __future__ import absolute_import, print_function

import as wav
import sys

    from deepspeech.utils import audioToInputVector
except ImportError:
    import numpy as np
    from python_speech_features import mfcc
    from six.moves import range

    class DeprecationWarning:
        displayed = False

    def audioToInputVector(audio, fs, numcep, numcontext):
        if DeprecationWarning.displayed is not True:
            DeprecationWarning.displayed = True
            print('------------------------------------------------------------------------', file=sys.stderr)
            print('WARNING: libdeepspeech failed to load, resorting to deprecated code',      file=sys.stderr)
            print('         Refer to for instructions on installing libdeepspeech', file=sys.stderr)
            print('------------------------------------------------------------------------', file=sys.stderr)

        # Get mfcc coefficients
        features = mfcc(audio, samplerate=fs, numcep=numcep)

        # We only keep every second feature (BiRNN stride = 2)
        features = features[::2]

        # One stride per time step in the input
        num_strides = len(features)

        # Add empty initial and final contexts
        empty_context = np.zeros((numcontext, numcep), dtype=features.dtype)
        features = np.concatenate((empty_context, features, empty_context))

        # Create a view into the array with overlapping strides of size
        # numcontext (past) + 1 (present) + numcontext (future)
        window_size = 2*numcontext+1
        train_inputs = np.lib.stride_tricks.as_strided(
            (num_strides, window_size, numcep),
            (features.strides[0], features.strides[0], features.strides[1]),

        # Flatten the second and third dimensions
        train_inputs = np.reshape(train_inputs, [num_strides, -1])

        # Whiten inputs (TODO: Should we whiten?)
        # Copy the strided array so that we can write to it safely
        train_inputs = np.copy(train_inputs)
        train_inputs = (train_inputs - np.mean(train_inputs))/np.std(train_inputs)

        # Return results
        return train_inputs

[docs]def audiofile_to_input_vector(audio_filename, numcep, numcontext): r""" Given a WAV audio file at ``audio_filename``, calculates ``numcep`` MFCC features at every 0.01s time step with a window length of 0.025s. Appends ``numcontext`` context frames to the left and right of each time step, and returns this data in a numpy array. """ # Load wav files fs, audio = return audioToInputVector(audio, fs, numcep, numcontext)