Skip to content
Snippets Groups Projects
Unverified Commit 6b7acee0 authored by Alain Riou's avatar Alain Riou Committed by GitHub
Browse files

Re-upload broken images in README.md

parent 93fbf862
Branches
No related tags found
No related merge requests found
...@@ -6,7 +6,7 @@ This code is the implementation of the [PESTO paper](https://arxiv.org/abs/2309. ...@@ -6,7 +6,7 @@ This code is the implementation of the [PESTO paper](https://arxiv.org/abs/2309.
that has been accepted at [ISMIR 2023](https://ismir2023.ismir.net/). that has been accepted at [ISMIR 2023](https://ismir2023.ismir.net/).
**Disclaimer:** This repository contains minimal code and should be used for inference only. **Disclaimer:** This repository contains minimal code and should be used for inference only.
If you want full implementation details or want to use PESTO for research purposes, take a look at ~~[this repository](https://github.com/aRI0U/pesto-full)~~ (work in progress). If you want full implementation details or want to use PESTO for research purposes, take a look at ~~[this repository](https://github.com/aRI0U/pesto-full)~~ (coming soon!).
## Installation ## Installation
...@@ -59,7 +59,8 @@ Alternatively, one can save timesteps, pitch, confidence and activation outputs ...@@ -59,7 +59,8 @@ Alternatively, one can save timesteps, pitch, confidence and activation outputs
Finally, you can also visualize the pitch predictions by exporting them as a `png` file (you need `matplotlib` to be installed for PNG export). Here is an example: Finally, you can also visualize the pitch predictions by exporting them as a `png` file (you need `matplotlib` to be installed for PNG export). Here is an example:
![example f0](https://github.com/SonyCSLParis/pesto/assets/36546630/2ad82c86-136a-4125-bf47-ea1b93408022) ![example f0](https://github.com/SonyCSLParis/pesto/assets/36546630/5aa18c23-0154-4d2d-8021-2c23277b27a3)
Multiple formats can be specified after the `-e` option. Multiple formats can be specified after the `-e` option.
...@@ -81,7 +82,8 @@ Additionally, audio files can have any sampling rate; no resampling is required. ...@@ -81,7 +82,8 @@ Additionally, audio files can have any sampling rate; no resampling is required.
By default, the model returns a probability distribution over all pitch bins. By default, the model returns a probability distribution over all pitch bins.
To convert it to a proper pitch, by default, we use Argmax-Local Weighted Averaging as in CREPE: To convert it to a proper pitch, by default, we use Argmax-Local Weighted Averaging as in CREPE:
![image](https://github.com/SonyCSLParis/pesto/assets/36546630/7d06bf85-585c-401f-a3c2-f2fab90dd1a7) ![image](https://github.com/SonyCSLParis/pesto/assets/36546630/3138c33f-672a-477f-95a9-acaacf4418ab)
Alternatively, one can use basic argmax of weighted average with option `-r`/`--reduction`. Alternatively, one can use basic argmax of weighted average with option `-r`/`--reduction`.
...@@ -150,11 +152,11 @@ Note that batched predictions are available only from the Python API and not fro ...@@ -150,11 +152,11 @@ Note that batched predictions are available only from the Python API and not fro
## Performances ## Performances
On [MIR-1K]() and [MDB-stem-synth](), PESTO outperforms other self-supervised baselines. On [MIR-1K](https://zenodo.org/record/3532216#.ZG0kWhlBxhE) and [MDB-stem-synth](https://zenodo.org/records/1481172), PESTO outperforms other self-supervised baselines.
Its performances are close to CREPE's, which has 800x more parameters and was trained in a supervised way on a vast Its performances are close to CREPE's, which has 800x more parameters and was trained in a supervised way on a vast
dataset containing MIR-1K and MDB-stem-synth, among others. dataset containing MIR-1K and MDB-stem-synth, among others.
![image](https://github.com/SonyCSLParis/pesto/assets/36546630/9fbf15ef-7af9-4cd5-8832-f8fc24d43f25) ![image](https://github.com/SonyCSLParis/pesto/assets/36546630/d6ae0306-ba8b-465a-8ca7-f916479a0ba5)
## Speed benchmark ## Speed benchmark
...@@ -165,7 +167,8 @@ granularity of the predictions, which can be controlled with the `--step_size` p ...@@ -165,7 +167,8 @@ granularity of the predictions, which can be controlled with the `--step_size` p
Here is a speed comparison between CREPE and PESTO, averaged over 10 runs on the same machine. Here is a speed comparison between CREPE and PESTO, averaged over 10 runs on the same machine.
![speed](https://github.com/SonyCSLParis/pesto/assets/36546630/8353c93d-e79f-497d-a09e-d8762e9a5cbc) ![speed](https://github.com/SonyCSLParis/pesto/assets/36546630/612b1850-c2cf-4df1-9824-b8460a2f9148)
- Audio file: `wav` format, 2m51s - Audio file: `wav` format, 2m51s
- Hardware: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, 8 cores - Hardware: 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz, 8 cores
......
0% Loading or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment