# Spotify Audio Features Scraper (`musicae/spotify-audio-features-scraper`) Actor Get Spotify 🎛️ Audio Features (tempo, key, energy & more) for any track. ✅ 99% success rate. ⚡ Results in seconds. 📦 Clean output. - **URL**: https://apify.com/musicae/spotify-audio-features-scraper.md - **Developed by:** [Musicae](https://apify.com/musicae) (community) - **Categories:** Social media, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing $3.99 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## 🎛️ Spotify Audio Features Scraper **Get Spotify audio features for any track, restored after the November 2024 deprecation.** Tempo (BPM), key, energy, danceability, valence, acousticness and more, in the exact JSON shape developers already know. A drop-in alternative to Spotify's removed `audio-features` endpoint. No Spotify developer account needed. No API key, no OAuth. Just paste URLs, IDs, ISRCs, or search keywords and hit **Run**. --- ### ⚡ Why this scraper? In November 2024, Spotify **removed public access** to the `audio-features` (and `audio-analysis`) endpoints, breaking countless apps, playlists tools, and ML pipelines. This scraper **restores that access**: same fields, same value ranges, no migration headache. - 🎛️ **Full audio-features object**: tempo, key, mode, time signature, energy, danceability, valence, acousticness, instrumentalness, speechiness, liveness, loudness, duration. - 🔌 **Drop-in shape**: returns the exact `audio_features` JSON structure Spotify used to serve. Swap the source, keep your code. - 🧠 **ML embedding (optional)**: a normalized 12-feature vector per track, ready for similarity search, clustering, and recommendation models. - 🔀 **Flexible input**: Spotify URLs, track IDs, ISRCs, or plain search keywords, mix and match freely. - 📦 **Clean, bulk output**: one row per track as JSON, CSV, or Excel, no rate-limit juggling. --- ### 🏆 How it compares | Feature | This scraper | Spotify Web API | Other scrapers | |---|:---:|:---:|:---:| | 🎛️ Audio features | ✅ | ❌ Deprecated (Nov 2024) | ❌ Most can't | | 🔑 API key / OAuth required | ❌ None | ✅ Required | ⚠️ Varies | | 🏷️ ISRC input | ✅ | ❌ | ❌ | | 🔍 Search by keyword | ✅ | ✅ | ❌ Most don't | | 🧠 ML embedding vector | ✅ | ❌ | ❌ | | 📦 Bulk + JSON/CSV/Excel | ✅ | ⚠️ Manual | ⚠️ Varies | --- ### 📊 What data do you get? For every track, the full **audio-features** object plus a minimal track identity: | Feature | Description | Example | |---|---|---| | **Tempo** | BPM (beats per minute) | 122.746 | | **Key** | Musical key (0 = C, 1 = C#/Db, ..., 11 = B) | 2 | | **Mode** | Major (1) or Minor (0) | 1 | | **Time Signature** | Estimated time signature | 4 | | **Energy** | Intensity and activity (0.0 to 1.0) | 0.697 | | **Danceability** | How suitable for dancing (0.0 to 1.0) | 0.613 | | **Valence** | Musical positivity / mood (0.0 to 1.0) | 0.476 | | **Acousticness** | Confidence the track is acoustic (0.0 to 1.0) | 0.0194 | | **Instrumentalness** | Likelihood of no vocals (0.0 to 1.0) | 0.0 | | **Speechiness** | Presence of spoken words (0.0 to 1.0) | 0.133 | | **Liveness** | Probability of a live recording (0.0 to 1.0) | 0.332 | | **Loudness** | Overall loudness in decibels (dB) | -8.618 | | **Duration** | Track length in milliseconds | 320357 | Each result also includes the track's **name, Spotify ID, ISRC, popularity, cover art, URL and URI**, plus the standard `id`, `uri`, `track_href`, `analysis_url` and `type` fields, so it parses exactly like Spotify's original response. #### 🧠 ML Embedding (optional) Enable **Include ML Embedding Vector** to add a normalized 12-feature vector per track: ```json "embedding": { "id": "0DiWol3AO6WpXZgp0goxAV", "features": ["danceability", "energy", "acousticness", "instrumentalness", "liveness", "speechiness", "valence", "loudness", "tempo", "key", "mode", "time_signature"], "vector": [0.613, 0.697, 0.0194, 0.0, 0.332, 0.133, 0.476, 0.856367, 0.490984, 0.181818, 1.0, 0.571429] } ```` *** ### 🚀 How to use 1. **Enter your tracks**: Paste Spotify URLs, track IDs, ISRCs, or keywords in the Tracks field. 2. **Toggle the embedding**: Enable the ML vector if you need it for similarity/ML work. 3. **Run**: Click Start and get audio features back in seconds. #### ⚙️ Options | Option | Default | Description | |---|:---:|---| | 🧠 **Include ML Embedding Vector** | ⬜ Off | Add a normalized 12-feature vector per track | | 🔢 **Max results per keyword** | `10` | Cap the number of results per search keyword (URL/ID/ISRC always return one) | #### Input examples You can mix input types freely: ``` https://open.spotify.com/track/0DiWol3AO6WpXZgp0goxAV 49X0LAl6faAusYq02PRAY6 GBDUW0000053 Music Sounds Better With You ``` - 🔗 **URL**: Any `open.spotify.com/track/` link - 🆔 **ID**: The 22-character Spotify track ID - 🏷️ **ISRC**: International Standard Recording Code (e.g. `GBDUW0000053`) - 🔍 **Keyword**: A search term returning up to 50 results (configurable) *** ### 📤 Output Each result is a JSON object. Results are pushed to the **dataset** and saved as **individual JSON files** in the key-value store (e.g. `track_0DiWol3AO6WpXZgp0goxAV.json`). ```json { "name": "One More Time", "type": "track", "url": "https://open.spotify.com/track/0DiWol3AO6WpXZgp0goxAV", "image": "https://i.scdn.co/image/ab67616d0000b273...", "success": true, "result": "1/1", "mode": "url", "target": "https://open.spotify.com/track/0DiWol3AO6WpXZgp0goxAV", "error": null, "track": { "track_image": "https://i.scdn.co/image/ab67616d0000b273...", "track_name": "One More Time", "track_id": "0DiWol3AO6WpXZgp0goxAV", "track_url": "https://open.spotify.com/track/0DiWol3AO6WpXZgp0goxAV", "track_uri": "spotify:track:0DiWol3AO6WpXZgp0goxAV", "track_isrc": "GBDUW0000053", "track_popularity": 80 }, "audio_features": { "acousticness": 0.0194, "danceability": 0.613, "energy": 0.697, "instrumentalness": 0.0, "liveness": 0.332, "speechiness": 0.133, "valence": 0.476, "loudness": -8.618, "tempo": 122.746, "key": 2, "mode": 1, "time_signature": 4, "duration_ms": 320357, "id": "0DiWol3AO6WpXZgp0goxAV", "uri": "spotify:track:0DiWol3AO6WpXZgp0goxAV", "track_href": "https://api.spotify.com/v1/tracks/0DiWol3AO6WpXZgp0goxAV", "analysis_url": "https://api.spotify.com/v1/audio-analysis/0DiWol3AO6WpXZgp0goxAV", "type": "audio_features" } } ``` Available as **JSON**, **CSV**, **Excel**, or via the **Apify API** and **dataset exports**. *** ### 💡 Use cases - 🤖 **ML & recommendation pipelines**: Feed normalized audio features (or the embedding vector) straight into your models. - 📊 **Music analytics**: Compare tempo, energy and valence distributions across catalogs, genres, or playlists. - 🎶 **Playlist & mood tooling**: Build energy- or mood-based playlists from valence/energy/danceability. - 📈 **Trend research**: Track how the sonic profile of releases shifts over time. - 🛠️ **App migration**: Restore the `audio-features` data your app lost when Spotify deprecated the endpoint. - 🎓 **Academic research**: Collect structured audio-feature datasets for musicology or computational analysis. *** ### ❓ FAQ **Do I need a Spotify account or API key?** No. No developer account, no token, no OAuth, just paste your input and **Run**. **Spotify deprecated audio features in Nov 2024, how does this still work?** This scraper restores access to the same audio-feature data and returns it in Spotify's original `audio_features` JSON shape, so existing code keeps working. **What can I use as input?** Spotify track URLs, 22-character track IDs, ISRCs, or plain search keywords. Mix all four freely in one run. **Is the output the same shape as Spotify's old endpoint?** Yes. The `audio_features` object uses the same field names and value ranges, so it's a drop-in replacement. **What's the ML embedding for?** It's a normalized, fixed-order 12-feature vector per track, convenient for similarity search, clustering, and feeding recommendation/ML models without re-normalizing. **How many tracks can I process in one run?** As many as you like. Search keywords return up to 50 results each (configurable); URL/ID/ISRC inputs return exactly one each. **What formats can I export?** JSON, CSV, Excel, or via the Apify API and dataset exports. *** ### 🔌 Integrations Connect results to your existing workflow: - 📗 **Google Sheets**: Auto-export results to a spreadsheet - 🪝 **Webhooks**: Trigger downstream actions when a run completes - ⚡ **Zapier / Make**: Plug into 1000+ apps - 🛠️ **API**: Fetch results programmatically from any language - 🔄 **Scheduled runs**: Refresh your audio-feature datasets on a schedule # Actor input Schema ## `tracks` (type: `array`): One entry per line. Accepts Spotify track URLs (e.g. open.spotify.com/track/...), track IDs, ISRCs, or search keywords to find tracks. ## `includeEmbedding` (type: `boolean`): Include a normalized 12-feature embedding vector for each track, ready for similarity search, clustering, and machine-learning pipelines. ## `searchKeywordLimit` (type: `integer`): Cap the number of results returned per search keyword. Only applies when using keywords (URL, ID, and ISRC inputs always return one result each). ## `proxyConfiguration` (type: `object`): Select proxies to be used by the scraper. Apify Proxy is enabled by default. ## Actor input object example ```json { "tracks": [ "https://open.spotify.com/track/0DiWol3AO6WpXZgp0goxAV", "49X0LAl6faAusYq02PRAY6", "Music Sounds Better With You" ], "includeEmbedding": false, "searchKeywordLimit": 3, "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "tracks": [ "https://open.spotify.com/track/0DiWol3AO6WpXZgp0goxAV", "49X0LAl6faAusYq02PRAY6", "Music Sounds Better With You" ], "includeEmbedding": false, "searchKeywordLimit": 3, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("musicae/spotify-audio-features-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "tracks": [ "https://open.spotify.com/track/0DiWol3AO6WpXZgp0goxAV", "49X0LAl6faAusYq02PRAY6", "Music Sounds Better With You", ], "includeEmbedding": False, "searchKeywordLimit": 3, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("musicae/spotify-audio-features-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "tracks": [ "https://open.spotify.com/track/0DiWol3AO6WpXZgp0goxAV", "49X0LAl6faAusYq02PRAY6", "Music Sounds Better With You" ], "includeEmbedding": false, "searchKeywordLimit": 3, "proxyConfiguration": { "useApifyProxy": true } }' | apify call musicae/spotify-audio-features-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=musicae/spotify-audio-features-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Spotify Audio Features Scraper", "description": "Get Spotify 🎛️ Audio Features (tempo, key, energy & more) for any track. ✅ 99% success rate. ⚡ Results in seconds. 📦 Clean output.", "version": "1.0", "x-build-id": "vof4vZw1befUv8g3J" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/musicae~spotify-audio-features-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-musicae-spotify-audio-features-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/musicae~spotify-audio-features-scraper/runs": { "post": { "operationId": "runs-sync-musicae-spotify-audio-features-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/musicae~spotify-audio-features-scraper/run-sync": { "post": { "operationId": "run-sync-musicae-spotify-audio-features-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "tracks": { "title": "🔍 Spotify URLs, Track IDs, ISRCs, or Search Keywords", "type": "array", "description": "One entry per line. Accepts Spotify track URLs (e.g. open.spotify.com/track/...), track IDs, ISRCs, or search keywords to find tracks.", "items": { "type": "string" } }, "includeEmbedding": { "title": "🧠 Include ML Embedding Vector", "type": "boolean", "description": "Include a normalized 12-feature embedding vector for each track, ready for similarity search, clustering, and machine-learning pipelines.", "default": false }, "searchKeywordLimit": { "title": "Maximum results per search keyword", "minimum": 1, "maximum": 50, "type": "integer", "description": "Cap the number of results returned per search keyword. Only applies when using keywords (URL, ID, and ISRC inputs always return one result each).", "default": 3 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Select proxies to be used by the scraper. Apify Proxy is enabled by default." } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```