We extract audio sample metadata, BPM, key signatures, pack details, and plugin catalogues from Splice. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Sample Metadata objects from splice.com. All fields typed and schema-versioned.
"sample_id": "spl_892341", "title": "KSHMR_kick_heavy_04.wav", "bpm": 128, "key_signature": "F# minor", "duration_ms": 450, "instrument": "Drums", "tags": "['kick', 'electronic', 'heavy', 'one-shot']", "preview_url": "https://cdn-1.splice.com/previews/892341.mp3"
| # | sample_id | title | bpm | key_signature | duration_ms | instrument |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sample Packs objects from splice.com. All fields typed and schema-versioned.
"pack_id": "pack_4591", "pack_name": "Sounds of KSHMR Vol. 3", "creator": "KSHMR", "genre": "EDM", "sample_count": 4120, "description": "The definitive EDM sample pack featuring thousands of kicks, synths, and vocals.", "release_date": "2024-02-15"
| # | pack_id | pack_name | creator | genre | sample_count | preset_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Plugins & Gear objects from splice.com. All fields typed and schema-versioned.
"plugin_id": "plug_102", "plugin_name": "Serum", "developer": "Xfer Records", "category": "Synthesizer", "price_monthly": 9.99, "retail_price": 189.0, "os_mac": true
| # | plugin_id | plugin_name | developer | category | format_vst | format_au |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Creator Profiles objects from splice.com. All fields typed and schema-versioned.
"creator_id": "cr_8841", "creator_name": "Oliver", "bio": "Los Angeles based production duo.", "follower_count": 45210, "pack_count": 4, "total_samples": 1850, "social_instagram": "weareoliver"
| # | creator_id | creator_name | bio | location | follower_count | following_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Presets & MIDI objects from splice.com. All fields typed and schema-versioned.
"item_id": "pr_9921", "item_name": "Bass_Wobble_Deep.fxp", "item_type": "preset", "synth_target": "Serum", "genre": "Dubstep", "creator": "Virtual Riot", "tags": "['bass', 'wobble', 'heavy', 'serum']"
| # | item_id | item_name | item_type | synth_target | genre | creator |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Splice scraper handles the platform's heavy React frontend and dynamic audio players: sample metadata, creator catalogues, plugin pricing, and tag taxonomies - with JavaScript rendering built in.
Extract BPM, key signature, duration, and instrument tags for millions of one-shots and loops.
Capture pack creator, description, artwork, genre, and total sample counts.
Monitor monthly pricing, retail costs, developer details, and OS compatibility for the gear catalogue.
Identify synth targets like Serum or Massive, patch names, and associated genre tags.
Extract full tag hierarchies to understand how Splice categorises instruments, moods, and genres.
Capture public CDN links for low-res sample previews, useful for AI audio analysis.
Scrape bio, social links, total packs, and follower metrics for top sound designers.
Track keyword rankings to see which samples and packs dominate specific genre searches.
Run one-off bulk exports or configure continuous pipelines at weekly cadences with change-detection diffing.
Brief in. Clean data out.
Provide genres, creator URLs, or keyword sets. We design the extraction schema together.
We configure Playwright crawlers, network interception, and proxy rotation for splice.com.
Schema validation, null-rate checks, and sample data reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Splice is a heavily dynamic SPA with complex audio player states. Here is how we extract structured data reliably.
Splice relies entirely on client-side rendering. We run full Playwright browser sessions to execute JavaScript, trigger lazy-loaded sample lists, and hydrate the DOM before extraction.
Instead of parsing complex DOM structures for every audio file, our pipeline intercepts backend XHR and Fetch requests, extracting clean, structured JSON metadata directly from the Splice API.
Samples often contain deeply nested tags. We reconstruct the parent-child relationships for instruments and genres, ensuring your database reflects the true taxonomy.
For massive sample catalogues, we maintain a hash index of last-seen values per pack. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, schema drift, and coverage drops, and respond before you notice.
ML teams use sample metadata, BPM, and tags to train generative audio models and classification algorithms.
Sample labels track pack popularity, genre trends, and release cadence to benchmark their own products.
Audio tech companies analyse plugin pricing, format adoption, and OS compatibility trends.
A&R and marketing teams identify trending sound designers and producers based on follower metrics and pack volume.
Music platforms map Splice's tag structures to normalise their own catalogues and improve internal search.
Producers and labels analyse keyword search volume and sample tags to predict upcoming genre shifts.
"Splice holds the definitive metadata schema for modern music production, but extracting BPM, key, and tags at scale requires specialized infrastructure."
Most teams underestimate the investment required: reliable Splice extraction demands full JavaScript rendering to handle the React frontend, network interception for audio preview URLs, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our splice.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and retry logic. Playwright handles JavaScript rendering and SPA navigation. Combined via scrapy-playwright middleware.
We bypass complex DOM parsing by intercepting the raw JSON payloads exchanged between the Splice React frontend and their backend APIs.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About splice.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available metadata from Splice is generally permissible. DataFlirt targets only public, non-authenticated sample metadata, plugin prices, and creator profiles. We do not circumvent authentication walls or extract private user data. Clients should review ToS and consult legal counsel for specific use cases.
No. We extract metadata and public CDN preview URLs (low-res MP3s). High-resolution WAV/AIFF downloads require paid user credits and authentication, which we do not circumvent.
Yes. BPM and key signatures are standard metadata fields extracted for every applicable sample, alongside duration, instrument, and genre tags.
We use Playwright to execute JavaScript, hydrate the DOM, and intercept backend API responses. This ensures we capture all data, even items hidden behind lazy-loading triggers.
Yes. We extract monthly Rent-to-Own pricing, full retail prices, developer names, and supported formats (VST, AU) for the entire plugin catalogue.
We typically configure Splice pipelines for weekly or daily cadences, depending on your needs. Change-detection diffs ensure you only process new or updated packs.
Yes. We provide a sample run of up to 500 samples or 10 packs as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off metadata dump for ML training or a continuous feed of new sample packs - we scope, build, and operate the pipeline. Tell us what you need.
