Ryu1845’s gists

Ryu1845 / chunked_audio_inference.py

Created August 16, 2024 08:04

	from typing import Callable

	import numpy as np
	from tqdm import tqdm

	def wsola_chunked_processing(audio: np.ndarray, sr: int, chunk_size: int, hop_size: int, mod_func: Callable[[np.ndarray], np.ndarray]):
	# Check if chunk_size is larger than the audio length
	if chunk_size >= len(audio):
	# Process the entire audio in one go
	output = mod_func(audio).squeeze()

Ryu1845 / convnext.py

Last active August 5, 2024 11:57 — forked from amirshamaei/convnext.py

A ConvNet for the 2020s (1D version)

	# Copyright (c) Meta Platforms, Inc. and affiliates.

	# All rights reserved.

	# This source code is licensed under the license found in the
	# LICENSE file in the root directory of this source tree.


	import torch
	import torch.nn as nn

Ryu1845 / fsq.py

Created September 28, 2023 08:01

FSQ Implementation from the paper

	def round_ste(z):

	"""Round with straight through gradients."""

	zhat = jnp.round(z)

	return z + jax.lax.stop_gradient(zhat - z)

	class FSQ:

Ryu1845 / zero_init.py

Last active August 1, 2024 19:38

ZerO Initialization copied from the original repo (https://github.com/jiaweizzhao/ZerO-initialization/)

	import math
	import torch

	def hadamard(n: int, dtype=torch.int8):
	"""This function is a port of the one in scipy.linalg"""

	if n < 1:
	lg2 = 0
	else:
	lg2 = int(math.log(n, 2))

Ryu1845 / came.py

Last active July 17, 2023 23:22

CAME: Confidence-guided Adaptive Memory Efficient Optimization from the official repo (https://github.com/huawei-noah/Pretrained-Language-Model/blob/master/CAME/came.py)

	import math

	import torch
	import torch.optim


	class CAME(torch.optim.Optimizer):
	"""Implements CAME algorithm.
	This implementation is based on:
	`CAME: Confidence-guided Adaptive Memory Efficient Optimization`

Ryu1845 / bst_layer.py

Created June 19, 2023 20:29

JAX implementation of Block-State Transfomer (copied from https://arxiv.org/abs/2306.09539)

	"""Block-State Transformer Layer."""

	# Block Transformers are non-recurrent and parallelizable.
	block_transformer = jax.vmap(BRecT.nonrecurrent_cell)

	def BST(u):
	"""Block-State Transformer Layer."""
	global MF # True if Multi-Filter, False otherwise (SH/MH)

	# split inputs into windows (l/w, w, d)

Ryu1845 / lru.py

Last active December 13, 2023 13:17

Linear Recurrent Unit (LRU) from the paper ["Resurrecting Recurrent Neural Networks for Long Sequences"](https://arxiv.org/abs/2303.06349)

	"""
	Simplified Implementation of the Linear Recurrent Unit
	------------------------------------------------------
	We present here a simplified JAX implementation of the Linear Recurrent Unit (LRU).
	The state of the LRU is driven by the input $(u_k)_{k=1}^L$ of sequence length $L$
	according to the following formula (and efficiently parallelized using an associative scan):
	$x_{k} = \Lambda x_{k-1} +\exp(\gamma^{\log})\odot (B u_{k})$,
	and the output is computed at each timestamp $k$ as follows: $y_k = C x_k + D u_k$.
	In our code, $B,C$ follow Glorot initialization, with $B$ scaled additionally by a factor 2
	to account for halving the state variance by taking the real part of the output projection.

Ryu1845 / split.py

Last active March 18, 2023 19:45 — forked from ptrblck/numpy split vs PyTorch split

numpy split vs PyTorch split

	import torch
	import numpy as np

	# numpy
	a = np.random.rand(10, 20)
	tmp0 = np.split(a, indices_or_sections=5, axis=0) # split into 5 sections
	for t in tmp0:
	print(t.shape)
	# (2, 20)
	# (2, 20)

Ryu1845 / template.ass

Created January 8, 2023 14:08

vtuber clip template subtitle file (modified from https://github.com/idolactivities/hololive-translations/)

	[Script Info]
	; Script generated by Aegisub 9010-makepkg-6f546951b
	; http://www.aegisub.org/
	ScriptType: v4.00+
	WrapStyle: 0
	ScaledBorderAndShadow: yes
	YCbCr Matrix: TV.709
	PlayResX: 1280
	PlayResY: 720

Ryu1845 / snscraper-tweet-viewer.html

Created November 2, 2022 22:10

	<!DOCTYPE html>
	<html>
	<head>
	<title>SNScrape Viewer</title>
	<script src="https://unpkg.com/mustache@latest"></script>
	<script src="https://cdn.tailwindcss.com"></script>

	<script>
	function checkVisible(elm, threshold, mode) {
	threshold = threshold \|\| 0;

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