VoiceFixer VoiceFixer is a framework for general speech restoration.

Overview

Open In Colab PyPI version

VoiceFixer

VoiceFixer is a framework for general speech restoration. We aim at the restoration of severly degraded speech and historical speech.

46dPxJ.png

Paper

⚠️ We submit this paper to ICLR2022. Preprint on arxiv will be available before Oct.03 2021!

Usage

⚠️ Still working on it, stay tuned! Expect to be available before 2021.09.30.

Environment

# Download dataset and prepare running environment
source init.sh 

Train from scratch

Let's take VF_UNet(voicefixer with unet as analysis module) as an example. Other model have the similar training and evaluation logic.

cd general_speech_restoration/voicefixer/unet
source run.sh

After that, you will get a log directory that look like this

├── unet
│   └── log
│       └── 2021-09-27-xxx
│           └── version_0
│               └── checkpoints
                    └──epoch=1.ckpt
│               └── code

Evaluation

Automatic evaluation and generate .csv file for the results.

cd general_speech_restoration/voicefixer/unet
# Basic usage
python3 handler.py  -c <str, path-to-checkpoint> \
                    -t <str, testset> \ 
                    -l <int, limit-utterance-number> \ 
                    -d <str, description of this evaluation> \ 

For example, if you like to evaluate on all testset. And each testset you intend to limit the number to 10 utterance.

python3 handler.py  -c  log/2021-09-27-xxx/version_0/checkpoints/epoch=1.ckpt \
                    -t  base \ 
                    -l  10 \ 
                    -d  ten_utterance_for_each_testset \ 

There are generally seven testsets:

  • base: all testset
  • clip: testset with speech that have clipping threshold of 0.1, 0.25, and 0.5
  • reverb: testset with reverberate speech
  • general_speech_restoration: testset with speech that contain all kinds of random distortions
  • enhancement: testset with noisy speech
  • speech_super_resolution: testset with low resolution speech that have sampling rate of 2kHz, 4kHz, 8kHz, 16kHz, and 24kHz.

Demo

Demo page

Demo page contains comparison between single task speech restoration, general speech restoration, and voicefixer.

Pip package

We wrote a pip package for voicefixer.

Colab

You can try voicefixer using your own voice on colab!

real-life-example real-life-example real-life-example

Project Structure

.
├── dataloaders 
│   ├── augmentation # code for speech data augmentation.
│   └── dataloader # code for different kinds of dataloaders.
├── datasets 
│   ├── datasetParser # code for preparing each dataset
│   └── se # Dataset for speech enhancement (source init.sh)
│       ├── RIR_44k # Room Impulse Response 44.1kHz
│       │   ├── test
│       │   └── train
│       ├── TestSets # Evaluation datasets
│       │   ├── ALL_GSR # General speech restoration testset
│       │   │   ├── simulated
│       │   │   └── target
│       │   ├── DECLI # Speech declipping testset
│       │   │   ├── 0.1 # Different clipping threshold
│       │   │   ├── 0.25
│       │   │   ├── 0.5
│       │   │   └── GroundTruth
│       │   ├── DENOISE # Speech enhancement testset
│       │   │   └── vd_test
│       │   │       ├── clean_testset_wav
│       │   │       └── noisy_testset_wav
│       │   ├── DEREV # Speech dereverberation testset
│       │   │   ├── GroundTruth
│       │   │   └── Reverb_Speech
│       │   └── SR # Speech super resolution testset
│       │       ├── GroundTruth
│       │       └── cheby1
│       │           ├── 1000 # Different cutoff frequencies
│       │           ├── 12000
│       │           ├── 2000
│       │           ├── 4000
│       │           └── 8000
│       ├── vd_noise # Noise training dataset
│       └── wav48 # Speech training dataset
│           ├── test # Not used, included for completeness
│           └── train 
├── evaluation # The code for model evaluation
├── exp_results # The Folder that store evaluation result (in handler.py).
├── general_speech_restoration # GSR 
│   ├── unet # GSR_UNet
│   │   └── model_kqq_lstm_mask_gan
│   └── voicefixer # Each folder contains the training entry for each model.
│       ├── dnn # VF_DNN
│       ├── lstm # VF_LSTM
│       ├── unet # VF_UNet
│       └── unet_small # VF_UNet_S
├── resources 
├── single_task_speech_restoration # SSR
│   ├── declip_unet # Declip_UNet
│   ├── derev_unet # Derev_UNet
│   ├── enh_unet # Enh_UNet
│   └── sr_unet # SR_UNet
├── tools
└── callbacks

Citation

⚠️ Will be available once the paper is ready.

Practical Natural Language Processing Tools for Humans is build on the top of Senna Natural Language Processing (NLP)

Practical Natural Language Processing Tools for Humans is build on the top of Senna Natural Language Processing (NLP) predictions: part-of-speech (POS) tags, chunking (CHK), name entity recognition (

jawahar 20 Apr 30, 2022
Diaformer: Automatic Diagnosis via Symptoms Sequence Generation

Diaformer Diaformer: Automatic Diagnosis via Symptoms Sequence Generation (AAAI 2022) Diaformer is an efficient model for automatic diagnosis via symp

Junying Chen 20 Dec 13, 2022
Officile code repository for "A Game-Theoretic Perspective on Risk-Sensitive Reinforcement Learning"

CvarAdversarialRL Official code repository for "A Game-Theoretic Perspective on Risk-Sensitive Reinforcement Learning". Initial setup Create a virtual

Mathieu Godbout 1 Nov 19, 2021
A Word Level Transformer layer based on PyTorch and 🤗 Transformers.

Transformer Embedder A Word Level Transformer layer based on PyTorch and 🤗 Transformers. How to use Install the library from PyPI: pip install transf

Riccardo Orlando 27 Nov 20, 2022
NLTK Source

Natural Language Toolkit (NLTK) NLTK -- the Natural Language Toolkit -- is a suite of open source Python modules, data sets, and tutorials supporting

Natural Language Toolkit 11.4k Jan 04, 2023
A Python wrapper for simple offline real-time dictation (speech-to-text) and speaker-recognition using Vosk.

Simple-Vosk A Python wrapper for simple offline real-time dictation (speech-to-text) and speaker-recognition using Vosk. Check out the official Vosk G

2 Jun 19, 2022
Open-World Entity Segmentation

Open-World Entity Segmentation Project Website Lu Qi*, Jason Kuen*, Yi Wang, Jiuxiang Gu, Hengshuang Zhao, Zhe Lin, Philip Torr, Jiaya Jia This projec

DV Lab 408 Dec 29, 2022
KoBERT - Korean BERT pre-trained cased (KoBERT)

KoBERT KoBERT Korean BERT pre-trained cased (KoBERT) Why'?' Training Environment Requirements How to install How to use Using with PyTorch Using with

SK T-Brain 1k Jan 02, 2023
Text editor on python tkinter to convert english text to other languages with the help of ployglot.

Transliterator Text Editor This is a simple transliteration program which is used to convert english word to phonetically matching word in another lan

Merin Rose Tom 1 Jan 16, 2022
Shirt Bot is a discord bot which uses GPT-3 to generate text

SHIRT BOT · Shirt Bot is a discord bot which uses GPT-3 to generate text. Made by Cyclcrclicly#3420 (474183744685604865) on Discord. Support Server EX

31 Oct 31, 2022
Official PyTorch implementation of Time-aware Large Kernel (TaLK) Convolutions (ICML 2020)

Time-aware Large Kernel (TaLK) Convolutions (Lioutas et al., 2020) This repository contains the source code, pre-trained models, as well as instructio

Vasileios Lioutas 28 Dec 07, 2022
Fine-tuning scripts for evaluating transformer-based models on KLEJ benchmark.

The KLEJ Benchmark Baselines The KLEJ benchmark (Kompleksowa Lista Ewaluacji Językowych) is a set of nine evaluation tasks for the Polish language und

Allegro Tech 17 Oct 18, 2022
Kerberoast with ACL abuse capabilities

targetedKerberoast targetedKerberoast is a Python script that can, like many others (e.g. GetUserSPNs.py), print "kerberoast" hashes for user accounts

Shutdown 213 Dec 22, 2022
Snowball compiler and stemming algorithms

Snowball is a small string processing language for creating stemming algorithms for use in Information Retrieval, plus a collection of stemming algori

Snowball Stemming language and algorithms 613 Jan 07, 2023
TFIDF-based QA system for AIO2 competition

AIO2 TF-IDF Baseline This is a very simple question answering system, which is developed as a lightweight baseline for AIO2 competition. In the traini

Masatoshi Suzuki 4 Feb 19, 2022
Proquabet - Convert your prose into proquints and then you essentially have Vogon poetry

Proquabet Turn your prose into a constant stream of encrypted and meaningless-so

Milo Fultz 2 Oct 10, 2022
This repository describes our reproducible framework for assessing self-supervised representation learning from speech

LeBenchmark: a reproducible framework for assessing SSL from speech Self-Supervised Learning (SSL) using huge unlabeled data has been successfully exp

49 Aug 24, 2022
A Streamlit web app that generates Rick and Morty stories using GPT2.

Rick and Morty Story Generator This project uses a pre-trained GPT2 model, which was fine-tuned on Rick and Morty transcripts, to generate new stories

₸ornike 33 Oct 13, 2022
Code to reprudece NeurIPS paper: Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks

Accelerated Sparse Neural Training: A Provable and Efficient Method to FindN:M Transposable Masks Recently, researchers proposed pruning deep neural n

itay hubara 4 Feb 23, 2022
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