Mureka AI V7.6 Recognize Song is a fast AI music recognition model that analyzes and recognizes songs. Ready-to-use REST inference API for song identification, music analysis, audio metadata workflows, catalog matching, content moderation, and professional music processing with simple integration, no coldstarts, and affordable pricing.
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}$0.015per run·~66 / $1
ExamplesView all
Related Models
README
Mureka AI V7.6 Recognize Song
Mureka AI V7.6 Recognize Song is a music recognition model for analyzing uploaded audio and identifying song-related information from the input track. It is suitable for song recognition, music metadata workflows, catalog matching, and other audio analysis tasks.
Why Choose This?
-
Song recognition workflow Analyze an uploaded audio track and return recognized song information.
-
Simple audio input Upload a single audio file and run recognition without additional configuration.
-
Useful for metadata workflows Suitable for music identification, catalog management, and audio analysis pipelines.
-
Fast API integration Easy to integrate into music tools, media workflows, and content analysis systems.
-
Fixed pricing Uses a simple flat price per recognition request.
Parameters
| Parameter | Required | Description |
|---|---|---|
| audio | Yes | Input audio track to analyze and recognize. |
How to Use
- Upload your audio — provide the song or music clip you want to analyze.
- Submit — run the recognition request.
- Review the result — use the returned song recognition output in your workflow.
Example Use Case
Upload a music clip to identify the song and support catalog lookup or metadata verification workflows.
Pricing
Just $0.015 per request.
Best Use Cases
- Song identification — Recognize songs from uploaded audio clips.
- Music catalog workflows — Match tracks against library or metadata systems.
- Audio analysis pipelines — Use recognition as part of larger music-processing workflows.
- Content management — Verify or enrich music-related metadata.
- Media operations — Support ingestion, tagging, and review processes for music assets.
Pro Tips
- Upload clear audio for better recognition results.
- Use the cleanest source clip available when possible.
- Tracks with clear vocals and strong musical detail are generally easier to recognize.
- Avoid heavily distorted, noisy, or low-quality uploads when accuracy matters.
Notes
audiois required.- Pricing is fixed at $0.015 per request.
- If the uploaded song has unclear or indistinct lyrics, the model may be unable to extract lyric-related information accurately.
- Very noisy, low-quality, or heavily mixed audio may reduce recognition quality.
Related Models
- Mureka AI Stem Song — Process songs into stem-based outputs for remixing, editing, and production workflows.
- Mureka AI V8 Generate Song — Generate full songs from lyrics and optional reference inputs.
- Mureka AI V9 Generate BGM — Generate background music tracks from prompts.
Mureka v7.6 Recognize Song API — Quick start
Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/mureka-ai/mureka-v7.6/recognize-song with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Mureka v7.6 Recognize Song below.
HTTP example
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3"
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/mureka-ai/mureka-v7.6/recognize-song" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $WAVESPEED_API_KEY" \
-d "$REQUEST_BODY")
TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
printf 'Submission response did not contain a prediction id
' >&2
exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
# 2. Poll until the prediction finishes.
while true; do
RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
-H "Authorization: Bearer $WAVESPEED_API_KEY")
RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
case "$STATUS" in
completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
*) sleep 2 ;;
esac
doneNode.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/mureka-ai/mureka-v7.6/recognize-song";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');
async function requestJson(url, options = {}) {
const response = await fetch(url, options);
if (!response.ok) throw new Error(await response.text());
return response.json();
}
// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
method: "POST",
headers: {
"Authorization": `Bearer ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;
// 2. Poll until the prediction finishes.
while (true) {
const resultBody = await requestJson(resultUrl, {
headers: { "Authorization": `Bearer ${apiKey}` },
});
const result = resultBody.data ?? resultBody;
if (result.status === "completed") {
console.log(result.outputs);
break;
}
if (["failed", "cancelled", "timeout", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
await new Promise(resolve => setTimeout(resolve, 2000));
}Python example
import json
import os
import time
from urllib.request import Request, urlopen
api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
"audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3"
}
def request_json(url, data=None):
request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
with urlopen(request) as response:
return json.load(response)
# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/mureka-ai/mureka-v7.6/recognize-song", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
raise RuntimeError("Submission response did not contain a prediction id")
result_url = f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"
# 2. Poll until the prediction finishes.
while True:
result_body = request_json(result_url)
result = result_body.get("data", result_body)
status = result.get("status")
if status == "completed":
print(result.get("outputs", []))
break
if status in {"failed", "cancelled", "timeout", "deleted"}:
raise RuntimeError(result)
time.sleep(2)Mureka v7.6 Recognize Song API — Frequently asked questions
What is the Mureka v7.6 Recognize Song API?
Mureka v7.6 Recognize Song is a Mureka Ai model for audio generation, exposed as a REST API on WaveSpeedAI. Mureka AI V7.6 Recognize Song is a fast AI music recognition model that analyzes and recognizes songs. Ready-to-use REST inference API for song identification, music analysis, audio metadata workflows, catalog matching, content moderation, and professional music processing with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.
How do I call the Mureka v7.6 Recognize Song API?
POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/mureka-ai/mureka-ai-mureka-v7.6-recognize-song.
How much does Mureka v7.6 Recognize Song cost per run?
Mureka v7.6 Recognize Song starts at $0.015 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.
What inputs does Mureka v7.6 Recognize Song accept?
Key inputs: `audio`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/mureka-ai/mureka-ai-mureka-v7.6-recognize-song.
How long does Mureka v7.6 Recognize Song take to generate?
Reported generation time on WaveSpeedAI is around 6 seconds per request. This is an estimate, not a latency guarantee; queue time and input settings can change the total wait. live status is visible in the prediction record.
Can I use Mureka v7.6 Recognize Song outputs commercially?
Commercial usage rights depend on the model's license, set by its provider (Mureka Ai). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.
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