HF's ML for Audio study group

Overview

Hugging Face Machine Learning for Audio Study Group

Welcome to the ML for Audio Study Group. Through a series of presentations, paper reading and discussions, we'll explore the field of applying Machine Learning in the Audio domain. Some examples of this are:

  • Generating synthetic sound out of a given text (think of conversational assistants)
  • Transcribing audio signals to text.
  • Removing noise out of an audio.
  • Separating different sources of audio.
  • Identifying which speaker is talking.
  • And much more!

We suggest you to join the community Discord at http://hf.co/join/discord, and we're looking forward to meet at the #ml-4-audio-study-group channel 🤗 . Remember, this is a community effort so make out of this your space!

Organisation

We'll kick off with some basics and then collaboratively decide the further direction of the group.

Before each session:

  • Read/watch related resources

During each session, you can

  • Ask question in the forum
  • Present a short (~10-15mins) presentation on the topic (agree beforehand)

Before/after:

  • Keep discussing/asking questions about the topic (#ml-4-audio-study channel on discord)
  • Share interesting resources

Schedule

Date Topics Resources (To read before)
Dec 14, 2021 Kickoff + Overview of Audio related usecases (video, questions) The 3 DL Frameworks for e2e Speech Recognition that power your devices
Dec 21, 2021
  • Intro to Audio
  • Automatic Speech Recognition Deep Dive
(video, questions)
Jan 4, 2022 Text to Speech Deep Dive (video, questions)
Jan 18, 2022 pyctcdecode: A simple & fast STT prediction decoding algorithm (demo, slides, questions)

Supplementary Resources

In case you want to solidify a concept, or just want to go down further deep into the speech processing rabbit-hole.

General Resources

  • Slides from LSA352: Slides (no videos available)
  • Slides from CS224S (Latest): Slides (no videos available)
  • Speech & Language Processing Book (Chapters 25 & 26) - E-book

Research Papers

Toolkits

Demos

Owner
Vaibhav Srivastav
Tech Speaker | Computational Linguist | Consultant
Vaibhav Srivastav
Data manipulation and transformation for audio signal processing, powered by PyTorch

torchaudio: an audio library for PyTorch The aim of torchaudio is to apply PyTorch to the audio domain. By supporting PyTorch, torchaudio follows the

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Korean stereoypte detector with TUNiB-Electra and K-StereoSet

Korean Stereotype Detector Korean stereotype sentence classifier using K-StereoSet with TUNiB-Electra Web demo you can test this model easily in demo

Sae_Chan_Oh 11 Feb 18, 2022
A 30000+ Chinese MRC dataset - Delta Reading Comprehension Dataset

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A fast, efficient universal vector embedding utility package.

Magnitude: a fast, simple vector embedding utility library A feature-packed Python package and vector storage file format for utilizing vector embeddi

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CodeJ A python project made to generate code using either OpenAI's codex or GPT-J (Although not as good as codex) Install requirements pip install -r

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(ACL-IJCNLP 2021) Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models.

BERT Convolutions Code for the paper Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models. Contains expe

mlpc-ucsd 21 Jul 18, 2022
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Andrew White 37 Dec 14, 2022
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DeepAmandine This is an artificial intelligence based on GPT-3 that you can chat with, it is very nice and makes a lot of jokes. We wish you a good ex

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Rahul Banerjee 179 Jan 07, 2023
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KLUE Baseline Korean(한국어) KLUE-baseline contains the baseline code for the Korean Language Understanding Evaluation (KLUE) benchmark. See our paper fo

74 Dec 13, 2022
Nested Named Entity Recognition for Chinese Biomedical Text

CBio-NAMER CBioNAMER (Nested nAMed Entity Recognition for Chinese Biomedical Text) is our method used in CBLUE (Chinese Biomedical Language Understand

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TOPSIS implementation in Python Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) CHING-LAI Hwang and Yoon introduced TOPSIS

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A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.

Basic-UI-for-GPT-J-6B-with-low-vram A repository to run GPT-J-6B on low vram systems by using both ram, vram and pinned memory. There seem to be some

90 Dec 25, 2022
A Telegram bot to add notes to Flomo.

flomo bot 使用 Telegram 机器人发送笔记到你的 Flomo. 你需要有一台可访问 Telegram 的服务器。 Steps @BotFather 新建机器人,获取 token Flomo 官网获取 API,链接 https://flomoapp.com/mine?source=in

Zhen 44 Dec 30, 2022
PyTorch Implementation of the paper Single Image Texture Translation for Data Augmentation

SITT The repo contains official PyTorch Implementation of the paper Single Image Texture Translation for Data Augmentation. Authors: Boyi Li Yin Cui T

Boyi Li 52 Jan 05, 2023
Unlimited Call - Text Bombing Tool

FastBomber Unlimited Call - Text Bombing Tool Installation On Termux

Aryan 6 Nov 10, 2022
A unified tokenization tool for Images, Chinese and English.

ICE Tokenizer Token id [0, 20000) are image tokens. Token id [20000, 20100) are common tokens, mainly punctuations. E.g., icetk[20000] == 'unk', ice

THUDM 42 Dec 27, 2022
CCQA A New Web-Scale Question Answering Dataset for Model Pre-Training

CCQA: A New Web-Scale Question Answering Dataset for Model Pre-Training This is the official repository for the code and models of the paper CCQA: A N

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NeoDaysPlus Reduced contrast, expanded, and continuously developed version of the CDDA tileset NeoDays that's being completed with new sprites for mis

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