From b602dcd4ae54aa24f467d0db2522530510d60f3f Mon Sep 17 00:00:00 2001
From: Marek Blok <marek.blok@pg.edu.pl>
Date: Wed, 15 Nov 2023 18:44:26 +0000
Subject: [PATCH] Update README.md
---
README.md | 24 ++++--------------------
1 file changed, 4 insertions(+), 20 deletions(-)
diff --git a/README.md b/README.md
index 8f74809..cb42b7c 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,9 @@ HSI Neural Networks
[2] Blok, M., BanaĹ, J., & PietroĹaj, M. (2022). Strategie treningu neuronowego estymatora czÄstotliwoĹci tonu krtaniowego z uĹźyciem generatora syntetycznych samogĹosek. PrzeglÄ
d Telekomunikacyjny+ WiadomoĹci Telekomunikacyjne, 604-607.
+**NOTE:** The below instructions are outdated and relate to the previous versions of the project.
+The latest instructions (in Polish) are available on projects Wiki page: https://git.pg.edu.pl/ife/IFE_raw_base/-/wikis/home
+
# Requirements & installation:
1. Install Miniconda3 for Windows: https://docs.conda.io/en/latest/miniconda.html
2. Create new conda environment:
@@ -13,10 +16,9 @@ HSI Neural Networks
3. Install Pytorch on your active environment:
- use command: <i>conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch</i>
4. Install additional python modules with requirement.txt file from this repository:
- - *???* use command: <i>pip install -r requirements.txt</i>
- use command: <i>conda install -c conda-forge --file requirements.txt</i>
- Once the above steps are completed, the environment is ready to be run.
+Once the above steps are completed, the environment is ready to be run.
# Project overview
@@ -54,21 +56,3 @@ The below files can be treated as examples of proper usage of the scripts:
# Usage
To run the chosen script chose execute it under previously created (and active) conda environment. Use command: <i>python \<script-name></i>example: <i>python prepare_data.py</i>. All paths should be set directly in the script file.
-
-# Additional files
-
-Below are the links to data that is not stored in the repository. These files can be used for running the steps explained in the previous section.
-
-1. Data preparation:
-- [*.dat file with raw data](https://pgedupl-my.sharepoint.com/:u:/g/personal/s119424_o365_student_pg_edu_pl/EZf6b_0dSHlOlM5mLlXyWHwBwULDCxfn5jE0_lK2jWhV3g?e=dagyw2)
-- [*csv file with matching labels](https://pgedupl-my.sharepoint.com/:x:/g/personal/s119424_o365_student_pg_edu_pl/EWRMzbjnN2RGk_-5V5fcbd4B1Q68NLJkjE5av_mXgjgPzQ?e=hOhSfO)
-
-2. Training step:
-- [*.csv with training data - linear, 100 classes <50,400> Hz](https://pgedupl-my.sharepoint.com/:x:/g/personal/s119424_o365_student_pg_edu_pl/EfIZuLECXARCl7JVT99z8ZgBdeJ1mz6HDuBmURf2wOY2MQ?e=wChc4X)
-
-3. Inference step:
-- [*.csv with synthetic evaluation data - linear, 100 classes <50,400> Hz](https://pgedupl-my.sharepoint.com/:x:/g/personal/s119424_o365_student_pg_edu_pl/EdFaTCyEMphDnNOz4gi82dAB0fh0z2NDFtzfSGhpraK_ig?e=lphWxu)
-- [*.csv with speech evaluation data - linear, 100 classes <50,400> Hz](https://pgedupl-my.sharepoint.com/:x:/g/personal/s119424_o365_student_pg_edu_pl/ESh8NF5cw8hIvuICRVa7qiABEzPZAvL2gSkhDa64rpafxg?e=Xx3gyB)
-- [binary with already trained model - ~80% of accuracy](https://pgedupl-my.sharepoint.com/:u:/g/personal/s119424_o365_student_pg_edu_pl/EZGevrxyYxRKg9gYTW3PSSYBV6YCfdg06Nxhpn4Knp8pXg?e=cOtXLN)
-
-All files can be also found here: [Additional files](https://pgedupl-my.sharepoint.com/:f:/g/personal/s119424_o365_student_pg_edu_pl/ElmX25HbIKRJgVeHDqFmoaMBDqipqVyQZHn4HbEDQajRNQ?e=1ltbkQ)
--
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