An evaluation toolkit for voice conversion models.

Related tags

Deep LearningVoice-conversion-evaluation
Overview

Voice-conversion-evaluation

An evaluation toolkit for voice conversion models.

Sample test pair

Generate the metadata for evaluating models.
The directory of parsers contains several available corpus parsers.

  python sampler.py [name of source corpus] [path of source dir] [name of target corpus] [path of target dir] -n [number of samples] -nt [number of target utterances] -o [path of output dir]

The pairs of metadata are sorted by src_second for long to short.
The metadata contains:

  • source_corpus: The name of the source corpus.
  • source_corpus_speaker_number: The number of speaker in source corpus.
  • source_random_seed: Random seed used for sampling source utterance.
  • target_corpus: The name of the target corpus.
  • target_corpus_speaker_number: The number of speaker in target corpus.
  • target_random_seed: Random seed used for sampling target utterances.
  • n_samples: number of samples
  • n_target_samples: number of target utterances
  • pairs: List of evaluating pairs
    • source_speaker: The name of the source speaker.
    • target_speaker: The name of the target speaker.
    • src_utt: The relative path of the source utterance, which is relative to the source dir.
    • tgt_utts: List of the relative path of target utterances, which is relative to the target dir.
    • content: The content of the source utterance.
    • src_second: The second of the source utterance.
    • converted: The entry does not appear when use sampler, you need to add the relative path for your converted output.

Metrics

The metrics include automatic mean opinion score assessment, character error rate, and speaker verification acceptance rate.

  • Automatic mean opinion score assessment
    • Ensemble several MBNet which is implemented by sky1456723.
      python calculate_objective_metric.py -d [data_dir] -r metrics/mean_opinion_score
  • Character error rate:
    • Use the automatic speech recognition model provided by Hugging Face.
    • The word error rate on Librispeech test-other is 3.9.
      python calculate_objective_metric.py -d [data_dir] -r metrics/character_error_rate
  • Speaker verification acceptance rate:
    • You can calculate the threshold by metrics/speaker_verification/equal_error_rate/.
    • And some pre-calculated thresholds are in metrics/speaker_verification/equal_error_rate/threshold.yaml.
      python calculate_objective_metric.py -d [data_dir] -r metrics/speaker_verification -t [target_dir] -th [threshold path]
You might also like...
Voice assistant - Voice assistant with python

🌐 Python Voice Assistant 🌵 - User's greeting 🌵 - Writing tasks to todo-list ?

PythonToday 10 Dec 26, 2022
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.

MMdnn MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model manage

Microsoft 5.7k Jan 9, 2023
SIMULEVAL A General Evaluation Toolkit for Simultaneous Translation

SimulEval SimulEval is a general evaluation framework for simultaneous translation on text and speech. Requirement python = 3.7.0 Installation git cl

Facebook Research 48 Dec 28, 2022
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

CNTK Chat Windows build status Linux build status The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes

Microsoft 17.3k Dec 29, 2022
Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

CNTK Chat Windows build status Linux build status The Microsoft Cognitive Toolkit (https://cntk.ai) is a unified deep learning toolkit that describes

Microsoft 17k Feb 11, 2021
This project is a loose implementation of paper "Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach"

Stock Market Buy/Sell/Hold prediction Using convolutional Neural Network This repo is an attempt to implement the research paper titled "Algorithmic F

Asutosh Nayak 136 Dec 28, 2022
PlenOctrees: NeRF-SH Training & Conversion

PlenOctrees Official Repo: NeRF-SH training and conversion This repository contains code to train NeRF-SH and to extract the PlenOctree, constituting

Alex Yu 323 Dec 29, 2022
Implementation for "Manga Filling Style Conversion with Screentone Variational Autoencoder" (SIGGRAPH ASIA 2020 issue)

Manga Filling with ScreenVAE SIGGRAPH ASIA 2020 | Project Website | BibTex This repository is for ScreenVAE introduced in the following paper "Manga F

null 30 Dec 24, 2022
[ICCV2021] IICNet: A Generic Framework for Reversible Image Conversion
[ICCV2021] IICNet: A Generic Framework for Reversible Image Conversion

IICNet - Invertible Image Conversion Net Official PyTorch Implementation for IICNet: A Generic Framework for Reversible Image Conversion (ICCV2021). D

felixcheng97 55 Dec 6, 2022
Releases(checkpoints)
Owner
GitHub Repository
This repository contains the implementation of the HealthGen model, a generative model to synthesize realistic EHR time series data with missingness

HealthGen: Conditional EHR Time Series Generation This repository contains the implementation of the HealthGen model, a generative model to synthesize

0 Jan 20, 2022
This repo is customed for VisDrone.

Object Detection for VisDrone(无人机航拍图像目标检测) My environment 1、Windows10 (Linux available) 2、tensorflow = 1.12.0 3、python3.6 (anaconda) 4、cv2 5、ensemble

53 Jul 17, 2022
JFB: Jacobian-Free Backpropagation for Implicit Models

JFB: Jacobian-Free Backpropagation for Implicit Models

Typal Research 28 Dec 11, 2022
Classification of ecg datas for disease detection

ecg_classification Classification of ecg datas for disease detection

Atacan ÖZKAN 5 Sep 09, 2022
PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"

DiscoGAN in PyTorch PyTorch implementation of Learning to Discover Cross-Domain Relations with Generative Adversarial Networks. * All samples in READM

Taehoon Kim 1k Jan 04, 2023
Transformer - Transformer in PyTorch

Transformer 完成进度 Embeddings and PositionalEncoding with example. MultiHeadAttent

Tianyang Li 1 Jan 06, 2022
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs

Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs ArXiv Abstract Convolutional Neural Networks (CNNs) have become the de f

Philipp Benz 12 Oct 24, 2022
A Novel Plug-in Module for Fine-grained Visual Classification

Pytorch implementation for A Novel Plug-in Module for Fine-Grained Visual Classification. fine-grained visual classification task.

ChouPoYung 109 Dec 20, 2022
Age and Gender prediction using Keras

cnn_age_gender Age and Gender prediction using Keras Dataset example : Description : UTKFace dataset is a large-scale face dataset with long age span

XN3UR0N 58 May 03, 2022
Rax is a Learning-to-Rank library written in JAX

🦖 Rax: Composable Learning to Rank using JAX Rax is a Learning-to-Rank library written in JAX. Rax provides off-the-shelf implementations of ranking

Google 247 Dec 27, 2022
SEC'21: Sparse Bitmap Compression for Memory-Efficient Training onthe Edge

Training Deep Learning Models on The Edge Training on the Edge enables continuous learning from new data for deployed neural networks on memory-constr

Brown University Scale Lab 4 Nov 18, 2022
Google Brain - Ventilator Pressure Prediction

Google Brain - Ventilator Pressure Prediction https://www.kaggle.com/c/ventilator-pressure-prediction The ventilator data used in this competition was

Samuele Cucchi 1 Feb 11, 2022
Implementation for the EMNLP 2021 paper "Interactive Machine Comprehension with Dynamic Knowledge Graphs".

Interactive Machine Comprehension with Dynamic Knowledge Graphs Implementation for the EMNLP 2021 paper. Dependencies apt-get -y update apt-get instal

Xingdi (Eric) Yuan 19 Aug 23, 2022
(SIGIR2020) “Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback’’

Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback About This repository accompanies the real-world experiments conducted i

yuta-saito 19 Dec 01, 2022
Dynamica causal Bayesian optimisation

Dynamic Causal Bayesian Optimization This is a Python implementation of Dynamic Causal Bayesian Optimization as presented at NeurIPS 2021. Abstract Th

nd308 18 Nov 22, 2022
The Submission for SIMMC 2.0 Challenge 2021

The Submission for SIMMC 2.0 Challenge 2021 challenge website Requirements python 3.8.8 pytorch 1.8.1 transformers 4.8.2 apex for multi-gpu nltk Prepr

5 Jul 26, 2022
Unofficial PyTorch Implementation for HifiFace (https://arxiv.org/abs/2106.09965)

HifiFace — Unofficial Pytorch Implementation Image source: HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping (figure 1, pg. 1)

MINDs Lab 218 Jan 04, 2023
Rlmm blender toolkit - A set of tools to streamline level generation in UDK straight from Blender

rlmm_blender_toolkit A set of tools to streamline level generation in UDK straig

Rocket League Mapmaking 0 Jan 15, 2022
This is the pytorch implementation of the paper - Axiomatic Attribution for Deep Networks.

Integrated Gradients This is the pytorch implementation of "Axiomatic Attribution for Deep Networks". The original tensorflow version could be found h

Tianhong Dai 150 Dec 23, 2022
Code for the Lovász-Softmax loss (CVPR 2018)

The Lovász-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks Maxim Berman, Amal Ranne

Maxim Berman 1.3k Jan 04, 2023