Mureka V7.6 Recognize Song | AI Music Generation API on WaveSpeedAI

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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 $0.03 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

audio is required.
Pricing is fixed at $0.03 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=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 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' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done

Node.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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "audio": "https://interactive-examples.mdn.mozilla.net/media/cc0-audio/t-rex-roar.mp3"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  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 = task.get("urls", {}).get("get") or 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"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    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 via the official Mureka API. 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 production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. 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 $0.030 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?

Median end-to-end generation time on WaveSpeedAI is around 3 seconds per request, based on recent successful runs. Queue time varies with global demand; 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). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

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