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import numpy as np


def freq2mel(freq):
    return 1127.01048 * np.log(1 + freq / 700.0)

def mel2freq(mel):
    return (np.exp(mel / 1127.01048) - 1) * 700

def mel_binning_matrix(specgram_window_size, sample_frequency, num_mel_bands):

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# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update

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"""
Little script demonstration how to run cudnn rnns
without cudnn using dynamic rnn with the same weights
(e.g. train on cudnn, use with dynamic rnn on cpu).

Note: this will run slower than cudnn on a gpu (see below).
Tested on Titan X Pascal:
With cudnn 3.5s vs. with dynamic_rnn 8s to run through 79 batches
with batch size 128.
Network: input size: 127, 2 layer bidirectional LSTM with num_units 200.

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In your local clone of your forked repository, you can add the original GitHub repository as a "remote". ("Remotes" are like nicknames for the URLs of repositories - origin is one, for example.) Then you can fetch all the branches from that upstream repository, and rebase your work to continue working on the upstream version. In terms of commands that might look like:

Add the remote, call it "upstream":

git remote add upstream https://github.com/whoever/whatever.git

Fetch all the branches of that remote into remote-tracking branches, such as upstream/master:

git fetch upstream