Music Data for Large Language Models LLM | 50,000 Music Files | Updated Weekly | Royalty Free Music | Pre-cleared for Generative AI
# | Filename |
Genre |
Subgenre |
Similar to |
Mood |
Instrument |
Attributes |
BPM |
Description |
---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx |
2 | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx |
3 | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx |
4 | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx |
5 | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx |
6 | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx |
7 | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx |
8 | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx |
9 | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx |
10 | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx |
... | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
Filename
|
Text | Tomorrow (66-BPM) | |
Genre
|
Text | Pop | |
Subgenre
|
Text | Pop Ballads | |
Similar to
|
Text | The Weeknd, Dua Lipa, Selena Gomez, Lauv, Justin Bieber, ... | |
Mood
|
Text | Sad, Contemplative, Hopeful | |
Instrument
|
Text | Synth, Piano, Drums, Bass, Samples | |
Attributes
|
Text | Atmospheric, Emotional, Minimal | |
BPM
|
Text | 66 | |
Description
|
Text | A chill and motivating synth pop ballad with hopeful over... |
Attribute | Type | Example | Mapping |
---|---|---|---|
Genre
|
Rap/Hip-Hop | ||
Subgenre
|
Alternative Hip-Hop | ||
Similar to
|
Big K.R.I.T., Tyler The Creator, Bun B, Freddie Gibbs, Ma... | ||
Mood
|
Chill, Carefree, Hopeful | ||
Instrument
|
Piano, Synth, Drums, 808 | ||
Attributes
|
Atmospheric, Bouncy, Fashion | ||
BPM
|
72 | ||
Description
|
A sexy and soulful modern alternative hip-hop track with ... |
Description
Country Coverage
History
Volume
50,000 | music tracks |
Pricing
License | Starts at |
---|---|
One-off purchase |
$500,000 / purchase |
Monthly License | Not available |
Yearly License | Not available |
Usage-based | Not available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Music Data for Large Language Models LLM 50,000 Music Files Updated Weekly Royalty Free Music Pre-cleared for Generative AI?
The number one music dataset in the world. 50,000 professional music track in all genres with human crafted metadata. All rights are cleared for use in machine learning and generative AI.
What is Music Data for Large Language Models LLM 50,000 Music Files Updated Weekly Royalty Free Music Pre-cleared for Generative AI used for?
This product has 3 key use cases. Soundsnap recommends using the data for Machine Learning (ML), Deep Learning, and Generative AI. Global businesses and organizations buy Machine Learning (ML) Data from Soundsnap to fuel their analytics and enrichment.
Who can use Music Data for Large Language Models LLM 50,000 Music Files Updated Weekly Royalty Free Music Pre-cleared for Generative AI?
This product is best suited if you’re a Enterprise looking for Machine Learning (ML) Data. Get in touch with Soundsnap to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Music Data for Large Language Models LLM 50,000 Music Files Updated Weekly Royalty Free Music Pre-cleared for Generative AI go?
This product has 10 years of historical coverage. It can be delivered on a on-demand basis.
Which countries does Music Data for Large Language Models LLM 50,000 Music Files Updated Weekly Royalty Free Music Pre-cleared for Generative AI cover?
This product includes data covering 249 countries like USA, China, Japan, Germany, and India. Soundsnap is headquartered in Cyprus.
How much does Music Data for Large Language Models LLM 50,000 Music Files Updated Weekly Royalty Free Music Pre-cleared for Generative AI cost?
Pricing for Music Data for Large Language Models LLM 50,000 Music Files Updated Weekly Royalty Free Music Pre-cleared for Generative AI starts at USD500,000 per purchase. Connect with Soundsnap to get a quote and arrange custom pricing models based on your data requirements.
How can I get Music Data for Large Language Models LLM 50,000 Music Files Updated Weekly Royalty Free Music Pre-cleared for Generative AI?
Businesses can buy Machine Learning (ML) Data from Soundsnap and get the data via S3 Bucket, REST API, and Streaming API. Depending on your data requirements and subscription budget, Soundsnap can deliver this product in .csv and .xls format.
What is the data quality of Music Data for Large Language Models LLM 50,000 Music Files Updated Weekly Royalty Free Music Pre-cleared for Generative AI?
Soundsnap has reported that this product has the following quality and accuracy assurances: 80% instrumental, 20% vocal. You can compare and assess the data quality of Soundsnap using Datarade’s data marketplace.
What are similar products to Music Data for Large Language Models LLM 50,000 Music Files Updated Weekly Royalty Free Music Pre-cleared for Generative AI?
This product has 3 related products. These alternatives include Music Data for Machine Learning (ML) 50,000 Music Files Updated Weekly Royalty Free Music Pre-cleared for Generative AI, Nexdata Large Language Model Data SFT Data Pre-training Data LLM Data Text AI & ML Training Data Natural Language Processing (NLP) Data, and FileMarket 20,000 photos AI Training Data Large Language Model (LLM) Data Machine Learning (ML) Data Deep Learning (DL) Data . You can compare the best Machine Learning (ML) Data providers and products via Datarade’s data marketplace and get the right data for your use case.