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Beat_this detects beat positions in an audio file and returns timestamps paired with beat numbers. The easiest way to try Xavriley’s Beat_this model on Replicate is in its Playground; you can also call it through Replicate’s API or run the open-source CPJKU implementation locally.
What Beat_this does—and what it does not
Beat_this is a beat tracker: it identifies when beats occur in supplied audio. Its output is a sequence of timestamp and beat-number pairs, which can help with beat-aligned analysis or editing. The project also supports downbeat annotations in its local workflows.
It is not a full transcription system, a music-production tool, or a general song-understanding service. Its beat positions should be checked against the audio, particularly when the music has unusual rhythmic behavior or belongs to an underrepresented genre.
Try it in Replicate’s Playground
- Open the Beat_this model page and select the Playground.
- Provide an audio file using the input control. The model accepts audio through the hosted interface; for API use, Replicate also documents URL and uploaded-file options.
- Review the inputs. The current schema shows
constant_tempoanduse_dbn, both defaulting totrue. - Run the prediction, then inspect the returned timestamp-and-beat-number output. Confirm the actual representation for the model version and client you use before relying on it in another application.
The Replicate page snapshot accessed in 2026 estimates about $0.040 per run (roughly 25 runs per dollar), with cost varying by input. It estimates completion typically within three minutes, but says prediction time varies significantly. These are estimates, not fixed price or runtime guarantees.
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Call the model from Python
For an application or repeatable workflow, Replicate’s API requires an API token and a client library or HTTP request. The example below pins a model version and sets both options explicitly. Replace the example URL with an audio file URL accessible to the service, or use the client’s local-file upload support.
import replicate
output = replicate.run(
"xavriley/beat_this:26142842c6dc94673820f0a9762214fa7109015d966031d8e6137bef4fd14323",
input={
"audio": "https://example.com/your-audio.mp3",
"constant_tempo": True,
"use_dbn": False,
},
)
print(output)Replicate’s API documentation covers authentication, client setup, and supported file inputs, including URLs, uploaded local files, and data URIs for small files. A version-pinned call helps make the model version explicit; check that version’s input schema and returned output rather than assuming every version behaves identically.
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Understand the two settings
constant_tempo
This option controls whether the model assumes a constant tempo. The hosted schema currently defaults it to true. If the recording changes tempo, consider whether that assumption fits the material; changing the setting alone does not guarantee a better result.
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This option controls Madmom DBN postprocessing and currently defaults to true on the hosted page. The research paper is specifically about beat tracking without DBN postprocessing, so set use_dbn to false if you want that no-DBN configuration. The hosted default and the paper’s configuration are therefore not the same; verify the schema of the exact version you call.
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Reading the output
The model page illustrates tab-separated pairs of timestamps and beat numbers. The local project can produce .beats files and supports beat and downbeat annotations. Output details can differ by workflow, so inspect a real response before writing code that parses it.
Run the open-source implementation locally
The CPJKU project provides a Python package, command-line tool, and Python API. Local inference is useful for processing a collection of files or experimenting without sending each run to a hosted endpoint, but it requires installing the project’s dependencies and using your own computer’s resources.
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- Follow the installation and usage instructions in the official Beat_this repository. The repository lists PyTorch 2.0 or later and additional modules.
- For non-WAV files, make sure ffmpeg or another supported torchaudio backend is available.
- Run the
beat_thiscommand-line program on an audio path or directory. The CLI writes.beatsoutput. - For a Python workflow, use the repository’s API to retrieve beats and downbeats or save a
.beatsfile for tools such as Sonic Visualiser.
The implementation uses the first available GPU by default and falls back to CPU if CUDA is unavailable; the repository documents how to force CPU or select a GPU. DBN postprocessing is optional locally and requires installing madmom. Local execution avoids Replicate’s per-run charge, but the sources do not quantify setup, compute, or electricity costs.
Choose hosted or local execution
| Route | Best suited to | Trade-offs |
|---|---|---|
| Replicate Playground or API | A quick first run or adding beat detection to an application | Less local setup; remote file/API workflow; hosted runs are billed per prediction, with a variable estimate on the model page; pin a version for reproducibility. |
| CPJKU implementation | Batch processing, experimentation, or keeping inference on your machine | Requires Python dependencies and local compute; GPU can be used when available; DBN postprocessing requires an additional dependency. |
Know the limitations and reuse conditions
The authors’ 2024 paper, Beat this! Accurate beat tracking without DBN postprocessing, describes training across multiple datasets, including solo instrument recordings, music with time-signature changes, and classical music with substantial tempo variation. Those research descriptions do not mean every research option is exposed by the hosted wrapper. The paper also cautions that the system can fail on difficult and underrepresented genres and performs worse on continuity metrics.
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The repository warns that evaluation on files from training datasets can produce unfairly good results. Published evaluation scores should not be treated as a promise about your own recordings. Listen to results in context before using them downstream.
The repository says its code and published model weights use the MIT license. It separately notes that some training files may be copyrighted or subject to limited Creative Commons terms. The code-and-weights license does not clear rights in training audio or grant permission to process music you do not have the right to use.
The paper was submitted to arXiv on July 31, 2024, and states that it was accepted at the 25th ISMIR conference in 2024. Read the paper and abstract for the authors’ description of the method and its limitations.
Quick Recap
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