The question lands in a space where ambition and legal reality rarely meet. A developer wants to build an audio-to-chord engine that can hear a dense jazz voicing or a passing altered dominant and name it correctly, with beat-aligned precision. That is a hard technical problem. But the harder problem is not the model architecture. It is the data. Public datasets are built for simpler tasks, and the gap between what they offer and what this project demands is not a small gap. It is a canyon.
Consider what the user is asking for: time-aligned audio, chord labels, beat and downbeat information, and a vocabulary that includes maj9, 6/9, m11, altered dominants, slash chords, and modal interchange. Public datasets like McGill Billboard or the standard MIR datasets are useful for basic major and minor detection, but they are not designed for the harmonic density of jazz or film scores. The annotations are often simplified, the repertoire is narrow, and the labeling conventions do not support the nuance required. The user has already noticed this. Good. That awareness is the first step. The next step is accepting that a serious model will require a serious investment in private data.
The realistic route is a hybrid approach: combine public datasets for pretraining and baseline performance, then layer in a privately licensed or hand-annotated corpus that covers the harmonic vocabulary you actually need. How many tracks? That is the question everyone wants a number for. The honest answer is that it depends on the complexity of the harmony and the consistency of the annotations. For jazz-influenced harmony, a few hundred accurately annotated tracks with beat-level alignment could be a meaningful start, but you would likely need over a thousand to approach something you could ship with confidence. The bottleneck is not the number of tracks alone. It is the quality of the labels and the coverage of chord types. A thousand tracks with sloppy labels will not help. Five hundred with meticulous, expert-level annotations will take you much further.
This is where the commercial landscape gets tricky. Services like iReal Pro, Hooktheory, Ultimate Guitar, and Chordify have the kind of chord data that looks tempting, but the licensing and annotation quality are not built for training commercial models. Scraping or reusing that data is not a viable path. It is a legal and ethical dead end. The user already knows this, and it is worth stating plainly: there is no shortcut around the rights question. If you want to ship a product, you need data you can defend in a commercial agreement. That means either licensing from a vendor that specializes in music data or commissioning your own annotations.
What should you study before spending money? Look at the MIR benchmark literature, particularly work on chord recognition evaluation and the challenges of evaluating models on complex harmony. Look at how companies like Melodrive or other AI music startups handle dataset licensing. Look at the metadata and annotation standards used in academic datasets, even if the content is too simple. That will tell you what a good label file looks like. Then talk to people who have actually trained chord-recognition models, not just published papers on them. They will tell you that the data work is 80 percent of the project, and the model is the last 20 percent.
Here is the concrete takeaway: do not wait for a perfect public dataset to appear. It will not. Start by defining your chord vocabulary precisely, then commission a small pilot set of 50 to 100 tracks with expert annotations. Test your model against that set. Measure where it fails. Then scale up. The cost of data is real, but the cost of building on shaky legal ground or weak annotations is far higher. The protecting your data story is a reminder that data handling and rights issues are not abstract concerns. They can take down a product. And if you think your evaluation set is solid, ask ChatGPT to analyze three datasets and see how easily errors slip through when the underlying data is weak. The model is only as good as the labels it learns from. Build the labels first.