https://github.com/openvpi/makediffsinger
Pipelines and tools to build your own DiffSinger dataset.
https://github.com/openvpi/makediffsinger
Last synced: about 1 year ago
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Pipelines and tools to build your own DiffSinger dataset.
- Host: GitHub
- URL: https://github.com/openvpi/makediffsinger
- Owner: openvpi
- License: bsd-3-clause
- Created: 2023-04-12T04:58:33.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-18T19:03:35.000Z (about 2 years ago)
- Last Synced: 2024-04-22T10:11:33.773Z (about 2 years ago)
- Language: Python
- Size: 339 KB
- Stars: 72
- Watchers: 4
- Forks: 18
- Open Issues: 3
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MakeDiffSinger
Pipelines and tools to build your own DiffSinger dataset.
For the recommended standard dataset making pipelines, see:
- acoustic-forced-alignment: make dataset from scratch with MFA for acoustic model training
- variance-temp-solution: temporary solution to extend acoustic datasets into variance datasets
For other useful pipelines and tools for making a dataset, welcome to raise issues or submit PRs.
## DiffSinger dataset structure
- dataset1/
- raw/
- wavs/
- recording1.wav
- recording2.wav
- ...
- transcriptions.csv
- dataset2/
- raw/
- wavs/
- ...
- transcriptions.csv
- ...
## Essential tools to process and label your datasets
Dataset tools now have their own repository: [dataset-tools](https://github.com/openvpi/dataset-tools).
There are mainly 3 components:
- AudioSlicer: Slice your recordings into short segments
- MinLabel: Label *.lab files containing word transcriptions for acoustic model training.
- SlurCutter: Edit MIDI sequence in *.ds files for variance model training.