TensorBoard
File size: 5,277 Bytes
573027e
38f0a65
573027e
38f0a65
f8efefb
 
 
 
ad15daf
 
f8efefb
 
5258730
 
8471a16
 
5258730
8471a16
 
 
19833db
 
 
 
 
 
8471a16
 
5258730
8471a16
 
 
 
 
5258730
8471a16
 
 
58b0b89
 
 
 
 
 
5258730
 
8471a16
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
0e3f3c3
5258730
0e3f3c3
 
 
 
 
 
 
5258730
 
 
0e3f3c3
 
5258730
 
 
 
 
 
 
 
19833db
 
5258730
 
 
 
 
19833db
5258730
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f5729e9
5258730
f5729e9
8471a16
f5729e9
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
---
license: cc-by-nc-4.0
---

# RAVE Models

This is a collection of [RAVE](https://github.com/acids-ircam/RAVE) models trained by the [Intelligent Instruments Lab](https://iil.is) for various projects. 

For a full description see our blog post at: https://iil.is/news/ravemodels.

Most of these models are encoder-decoder only, no prior, and all use the `--causal` mode and are exported for streaming inference with [nn~](https://github.com/acids-ircam/nn_tilde), [NN.ar](https://github.com/elgiano/nn.ar) or [rave-supercollider](https://github.com/victor-shepardson/rave-supercollider).

## Musical Instruments

### guitar_iil_b2048_r48000_z16.ts

Dataset: [IILGuitarTimbre](https://github.com/Intelligent-Instruments-Lab/IILGuitarTimbre), a timbre-oriented collection of plucking, strumming, striking scraping and more recorded dry from an electric guitar.

Model: modified RAVE v1, 48kHz, block size 2048, 16 latent dimensions.

### sax_soprano_franziskaschroeder_b2048_r48000_z20.ts

Dataset: Soprano sax improvisation by [Franziska Schroeder](https://improvisationai.wordpress.com/).

Model: modified RAVE v1, 48kHz, block size 2048, 20 latent dimensions.

### organ_archive_b2048_r48000_z16.ts  

Dataset: various recordings of organ music sourced from archive.org. Small amounts of voice and other instruments were included, and vinyl record noises are prominent.

Model: modified RAVE v1, 48kHz, block size 2048, 16 latent dimensions.

### organ_bach_b2048_sr48000_z16.ts

Dataset: various recordings of J.S. Bach music for church organ.

Model: modified RAVE v1, 48kHz, block size 2048, 16 latent dimensions.

### mrp_strengjavera_b2048_r44100_z16.ts

Dataset: magnetic resonator piano controlled by artificial life, as part of generative installation Strengjavera by Jack Armitage premiered at AIMC 2023. See [paper](https://aimc2023.pubpub.org/pub/83k6upv8) and [Zenodo](https://zenodo.org/records/8329855) for citation.

Model: RAVE v3, 44.1kHz, block size 2048, 16 latent dimensions.

## Voice

### voice_vocalset_b2048_r48000_z16.ts

Dataset: [VocalSet](https://zenodo.org/record/1193957) singing voice dataset.

Model: modified RAVE v1, 48kHz, block size 2048, 16 latent dimensions.

### voice_hifitts_b2048_r48000_z16.ts

Dataset: [Hi-Fi TTS](http://arxiv.org/abs/2104.01497) audiobooks dataset.

Model: modified RAVE v1, 48kHz, block size 2048, 16 latent dimensions.

### voice_jvs_b2048_r44100_z16.ts

Dataset: [Hi-Fi TTS](http://arxiv.org/abs/2104.01497) speaker 9017 (John Van Stan).

Model: RAVE v3, 44.1kHz, block size 2048, 16 latent dimensions.

### voice_vctk_b2048_r44100_z16.ts

Dataset: [CSTR VCTK Corpus](https://datashare.ed.ac.uk/handle/10283/3443) multispeaker read speech dataset.

Model: RAVE v3, 44.1kHz, block size 2048, 22 latent dimensions.

## Birds

### birds_motherbird_b2048_r48000_z16.ts

This model of bird sounds was curated by Manuel Cherep, Jessica Shand and Jack Armitage for their piece Motherbird, performed at TENOR 2023 in Boston, May 2023.

Dataset: bird sounds.

Model: RAVE v1, 48kHz, block size 2048, 16 latent dimensions.

### birds_pluma_b2048_r48000_z12.ts

This model of bird sounds was curated by Giacomo Lepri for his instrument *[Pluma](http://www.giacomolepri.com/pluma)*

Dataset: bird sounds.

Model: modified RAVE v1, 48kHz, block size 2048, 12 latent dimensions.

## *Pond Brain* Marine Sounds

These models of marine sounds were trained for [Jenna Sutela](https://jennasutela.com/)'s *Pond Brain* installations at [Copenhagen Contemporary](https://copenhagencontemporary.org/en/yet-it-moves-read-online/) and the [Helsinki Biennial](https://helsinkibiennaali.fi/en/artist/jenna-sutela/)

Caution: these decoders sometimes produce a loud chirp on first initialization.

### water_pondbrain_b2048_r48000_z16.ts

Dataset: water recordings from freesound.org.
<details>
<summary>list of freesound users</summary>
inspectorj, inchadney, aesqe, vonfleisch, javetakami, atomediadesign, kolezan, zabuhailo, zaziesound, repdac3, al_sub, lgarrett, uzbazur, lydmakeren, frenkfurth, edo333, boredtoinsanity, owl, kaydinhamby, tliedes, ilmari_freesound, manoslindos, l3ardoc, alexbuk, s-light
</details>

Model: modified RAVE v1, 48kHz, block size 2048, 16 latent dimensions.

### humpbacks_pondbrain_b2048_r48000_z20.ts

Dataset: humpback whale recordings from the [Watkins database](https://cis.whoi.edu/science/B/whalesounds/index.cfm), [MBARI](https://freesound.org/people/MBARI_MARS/), and BBC.

Model: modified RAVE v1, 48kHz, block size 2048, 20 latent dimensions.

### marinemammals_pondbrain_b2048_r48000_z20.ts

Dataset: various marine mammal sounds from [NOAA](https://www.fisheries.noaa.gov/national/science-data/sounds-ocean-mammals), the [Watkins database](https://cis.whoi.edu/science/B/whalesounds/index.cfm), freesound users `felixblume` and `geraldfiebig`, and sound effects databases.

Model: modified RAVE v1, 48kHz, block size 2048, 20 latent dimensions.


## *Thales* magnets_b2048_r48000_z8.ts

Dataset: One hour recording of magnets of different dimensions hitting each other or scratching wooden and metallic surfaces. Used for [Thales](https://iil.is/pdf/2023_nime_privato_et_al_thales.pdf), a musical instrument based on magnets 

Model: RAVE v1, 48Khz, block size 2048, 8 latent dimensions.