Uploaded March 2021 | Updated September 2026, 2 weeks ago
AI (audio) companies face a difficult dilemma: balancing resources allocated to research and production. Building a solution to production standards is different from exploring new algorithms and model architectures.
When I talk to AI audio companies, they usually have these types of questions:
- What processes can we implement to run production and R&D effectively in parallel?
- How can we ensure that the wider team is all on the same page?
- How can we promote flexibility and fast iterations in R&D, while ensuring - high-quality code in production?
In this video, I share a few tips you can use to address all of the problems above.
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Interested in hiring me as a consultant/freelancer?
valeriovelardo.com/
Join The Sound Of AI Slack community:
valeriovelardo.com/the-sound-of-ai-community
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Connect with Valerio on Linkedin:
linkedin.com/in/valeriovelardo
Follow Valerio on Twitter:
twitter.com/musikalkemist
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Content
0:00 Intro
1:13 Create 2 separate teams
3:13 How to assign people to the teams
4:31 Production and research team coding approaches
6:13 Management workflows
9:06 Solving communication issues between teams
10:49 Rotating talent across teams
11:50 Consulting info + outro
AI (audio) companies face a difficult dilemma: balancing resources allocated to research and production. Building a solution to production standards is different from exploring new algorithms and model architectures.
When I talk to AI audio companies, they usually have these types of questions:
- What processes can we implement to run production and R&D effectively in parallel?
- How can we ensure that the wider team is all on the same page?
- How can we promote flexibility and fast iterations in R&D, while ensuring - high-quality code in production?
In this video, I share a few tips you can use to address all of the problems above.
===============================
Interested in hiring me as a consultant/freelancer?
valeriovelardo.com/
Join The Sound Of AI Slack community:
valeriovelardo.com/the-sound-of-ai-community
Follow Valerio on Facebook:
facebook.com/TheSoundOfAI
Connect with Valerio on Linkedin:
linkedin.com/in/valeriovelardo
Follow Valerio on Twitter:
twitter.com/musikalkemist
===============================
Content
0:00 Intro
1:13 Create 2 separate teams
3:13 How to assign people to the teams
4:31 Production and research team coding approaches
6:13 Management workflows
9:06 Solving communication issues between teams
10:49 Rotating talent across teams
11:50 Consulting info + outro
![MusicLM Generates Music From Text [Paper Breakdown]](https://i.ytimg.com/vi/eaqO5uT0G9Q/mqdefault.jpg "MusicLM Generates Music From Text [Paper Breakdown]
MusicLM has taken the Music AI community by storm. The model published by Google is a step ahead towards text-based music generation in the audio realm. By leveraging a clever combination of deep learning base models, MusicLM generates convincing short music clips with good audio fidelity.
Join The Sound of AI Slack Community:
https://valeriovelardo.com/the-sound-of-ai-community/
MusicLM paper:
https://arxiv.org/abs/2301.11325
MusicLM demo:
https://google-research.github.io/seanet/musiclm/examples/
Music AI talent recruitment:
https://thesoundofai.com/
Interested in hiring me as a consultant/freelancer?
https://thesoundofai.com/consulting.html
The Sound of AI Academy:
https://the-sound-of-ai-academy.teachable.com/
Advanced Python Programming:
https://the-sound-of-ai-academy.teachable.com/p/advanced-python-programming
Connect with Valerio on Linkedin:
https://www.linkedin.com/in/valeriovelardo
Follow Valerio on Facebook:
https://www.facebook.com/TheSoundOfAI
Follow Valerio on Twitter:
https://twitter.com/musikalkemist
Content:
0:00 Intro
0:45 Text-to-music
2:15 MusicLM demo
3:31 Riffusion and Mubert AI
4:37 MusicLM architecture
6:08 Components overview
7:40 SoundStream
8:20 w2v-BERT
8:51 MuLan
10:51 Training
16:18 Inference
19:52 Experiments
21:52 Limitations
24:40 Thoughts on research procedure")
