# Mureka Music Transcription

> Transcribes generated or uploaded music into a downloadable archive that can include MusicXML, PDF sheet music, and timestamped lyrics.


## Overview

- **Endpoint**: `https://fal.run/mureka/api/song/transcribe`
- **Model ID**: `mureka/api/song/transcribe`
- **Category**: audio-to-text
- **Kind**: inference
**Tags**: music, transcription, sheet music, musicxml



## Pricing

Billing is calculated per transcribed song. Your request will cost **$0.20** per song. For **$1** you can transcribe **5 songs**.

For more details, see [fal.ai pricing](https://fal.ai/pricing).

## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:


- **`song_id`** (`string`, _optional_):
  Generated song ID from song_id in a /generate/song response. Provide exactly one audio source.

- **`upload_audio_id`** (`string`, _optional_):
  Audio ID returned by /files/upload with purpose='audio'. Provide exactly one audio source.

- **`audio_url`** (`string`, _optional_):
  Public HTTPS URL or data URI for an MP3, WAV, or M4A file between 10 seconds and 10 minutes and no larger than 100 MB. Provide exactly one audio source.

- **`title`** (`string`, _optional_):
  Optional title used for the generated transcription files.



**Required Parameters Example**:

```json
{}
```


### Output Schema

The API returns the following output format:

- **`zip_url`** (`string`, _required_):
  The generated transcription archive.



**Example Response**:

```json
{
  "zip_url": ""
}
```


## Usage Examples

### cURL

```bash
curl --request POST \
  --url https://fal.run/mureka/api/song/transcribe \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{}'
```

### Python

Ensure you have the Python client installed:

```bash
pip install fal-client
```

Then use the API client to make requests:

```python
import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "mureka/api/song/transcribe",
    arguments={},
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)
```

### JavaScript

Ensure you have the JavaScript client installed:

```bash
npm install --save @fal-ai/client
```

Then use the API client to make requests:

```javascript
import { fal } from "@fal-ai/client";

const result = await fal.subscribe("mureka/api/song/transcribe", {
  input: {},
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);
```


## Deploy your own model

To run your own weights, a fine-tune or a custom pipeline on the GPUs that serve this API, deploy it on fal Serverless. GPUs from H100 (80GB) to B300 (288GB), billed per second; an H100 is $4.50/h list, as low as $2.49/h. Prices, GPU specs and deploy steps: [fal Serverless](https://fal.ai/serverless.md).

## Additional Resources

### Documentation

- [Model Playground](https://fal.ai/models/mureka/api/song/transcribe)
- [API Documentation](https://fal.ai/models/mureka/api/song/transcribe/api)
- [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=mureka/api/song/transcribe)

### fal.ai Platform

- [Platform Documentation](https://fal.ai/docs/documentation)
- [Python Client](https://fal.ai/docs/api-reference/client-libraries/python)
- [JavaScript Client](https://fal.ai/docs/api-reference/client-libraries/javascript)

### Other agent-readable surfaces

This file covers one model. To find anything else:

- [Platform overview](https://fal.ai/llms.txt): Entry points and representative endpoint IDs
- [Documentation index](https://fal.ai/docs/llms.txt): Every documentation page
- [Full documentation text](https://fal.ai/docs/llms-full.txt): The whole documentation inlined
- [Pricing](https://fal.ai/pricing.md): Model API and Serverless GPU prices
- [fal Serverless](https://fal.ai/serverless.md): Deploy your own models: GPU prices, specs and steps
- Any other model: `https://fal.ai/models/<endpoint-id>/llms.txt`