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spectrogram layers

Project description

SPELA - spectrogram layers

Rewrote kapre using tensorflow.keras
credits go to Keunwoo Choi for writing kapre

My main goal for rewriting it with tensorflow.keras is to use it with TensorFlow Lite
since Keunwoo Choi used core keras and I had problems converting the model to
tensorflow lite.

Implementing audio features inside the keras layers allows the preprocessing
computations to be done on the GPU as highlighted in their paper

Checkout this Speaker Recognition project to see the usage of Spela.

Installation

The package uses tensorflow but is not listed as requirement, please install it.

pip install spela

or

git clone https://github.com/kongkip/spela.git
cd spela
python setup.py install

Usage

spectrogram

import tensorflow as tf
from spela.spectrogram import Spectrogram

# a one channel audio with 16000 sample rate
input_shape = (1, 16000)

x = get_data()
y = get_data()


model = tf.keras.Sequential()
model.add(Spectrogram(n_dft=512, n_hop=256, input_shape=(input_shape),
                      return_decibel_spectrogram=True, power_spectrogram=2.0,
                      trainable_kernel=False, name='static_stft'))

model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001), loss=tf.keras.losses.categorical_crossentropy
              , metrics=["acc"])

print(model.summary())

model.fit(x,y)

Mel Spectrogram

import tensorflow as tf
from spela.melspectrogram import Melspectrogram

# a one channel audio with 16000 sample rate
input_shape = (1, 16000)

x = get_data()
y = get_data()

model = tf.keras.Sequential()
model.add(Melspectrogram(sr=SR, n_mels=128,
          n_dft=512, n_hop=256, input_shape=input_shape,
          return_decibel_melgram=True,
          trainable_kernel=False, name='melgram'))

model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.001), loss=tf.keras.losses.categorical_crossentropy
              , metrics=["acc"])

print(model.summary())

model.fit(x,y)

Project details


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