semvec/pypi-packages huggingface dataset:
spela 0.0.5
spectrogram layers
License: MIT

# SPELA - spectrogram layers
Rewrote [kapre](https://github.com/kongkip/kapre#installation) 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](https://arxiv.org/abs/1706.05781)

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.
```bash
pip install spela
```
or
```bash
git clone https://github.com/kongkip/spela.git
cd spela
python setup.py install
```

# Usage
## spectrogram
```python
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
```python
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)
```
psa-prizes 0.1.2
The PSA prizes project.
License: MIT
Requires Python: >=3.7,<4.0
Keywords: trading cards,grading,PSA,prizes,ebay

.. image:: https://badge.fury.io/py/psa-prizes.svg
  :target: https://pypi.org/project/psa-prizes

.. image:: https://github.com/tostenzel/psa-prizes/workflows/Continuous%20Integration/badge.svg?branch=master
  :target: https://github.com/tostenzel/psa-prizes/actions

.. image:: https://readthedocs.org/projects/psa-prizes/badge/?version=latest
   :target: https://psa-prizes.readthedocs.io/en/latest/?badge=latest

.. image:: https://codecov.io/gh/tostenzel/psa-prizes/branch/master/graph/badge.svg
  :target: https://codecov.io/gh/tostenzel/psa-prizes

.. image:: https://app.codacy.com/project/badge/Grade/8b4d19500d434a18a899405d71d2654e
   :alt: Codacy Badge
   :target: https://app.codacy.com/manual/tostenzel/psa-prizes?utm_source=github.com&utm_medium=referral&utm_content=tostenzel/psa-prizes&utm_campaign=Badge_Grade_Dashboard