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| # Release notes for Demucs | |
| ## V4.1.0a1, TBD | |
| Get models list | |
| Check segment of HTDemucs inside BagOfModels | |
| Added api.py to be called from another program | |
| Use api in separate.py | |
| Added `--other-method`: method to get `no_{STEM}`, add up all the other stems (add), original track substract the specific stem (minus), and discard (none) | |
| Added type `HTDemucs` to type alias `AnyModel`. | |
| ## V4.0.1, 8th of September 2023 | |
| **From this version, Python 3.7 is no longer supported. This is not a problem since the latest PyTorch 2.0.0 no longer support it either.** | |
| Various improvements by @CarlGao4. Support for `segment` param inside of HTDemucs | |
| model. | |
| Made diffq an optional dependency, with an error message if not installed. | |
| Added output format flac (Free Lossless Audio Codec) | |
| Will use CPU for complex numbers, when using MPS device (all other computations are performed by mps). | |
| Optimize codes to save memory | |
| Allow changing preset of MP3 | |
| ## V4.0.0, 7th of December 2022 | |
| Adding hybrid transformer Demucs model. | |
| Added support for [Torchaudio implementation of HDemucs](https://pytorch.org/audio/main/tutorials/hybrid_demucs_tutorial.html), thanks @skim0514. | |
| Added experimental 6 sources model `htdemucs_6s` (`drums`, `bass`, `other`, `vocals`, `piano`, `guitar`). | |
| ## V3.0.6, 16th of November 2022 | |
| Option to customize output path of stems (@CarlGao4) | |
| Fixed bug in pad1d leading to failure sometimes. | |
| ## V3.0.5, 17th of August 2022 | |
| Added `--segment` flag to customize the segment length and use less memory (thanks @CarlGao4). | |
| Fix reflect padding bug on small inputs. | |
| Compatible with pyTorch 1.12 | |
| ## V3.0.4, 24th of February 2022 | |
| Added option to split into two stems (i.e. vocals, vs. non vocals), thanks to @CarlGao4. | |
| Added `--float32`, `--int24` and `--clip-mode` options to customize how output stems are saved. | |
| ## V3.0.3, 2nd of December 2021 | |
| Fix bug in weights used for different sources. Thanks @keunwoochoi for the report and fix. | |
| Improving drastically memory usage on GPU for long files. Thanks a lot @famzah for providing this. | |
| Adding multithread evaluation on CPU (`-j` option). | |
| (v3.0.2 had a bug with the CPU pool and is skipped.) | |
| ## V3.0.1, 12th of November 2021 | |
| Release of Demucs v3, featuring hybrid domain separation and much more. | |
| This drops support for Conv-Tasnet and training on the non HQ MusDB dataset. | |
| There is no version 3.0.0 because I messed up. | |
| ## V2.0.2, 26th of May 2021 | |
| - Fix in Tasnet (PR #178) | |
| - Use ffmpeg in priority when available instead of torchaudio to avoid small shift in MP3 data. | |
| - other minor fixes | |
| ## v2.0.1, 11th of May 2021 | |
| MusDB HQ support added. Custom wav dataset support added. | |
| Minor changes: issue with padding of mp3 and torchaudio reading, in order to limit that, | |
| Demucs now uses ffmpeg in priority and fallback to torchaudio. | |
| Replaced pre-trained demucs model with one trained on more recent codebase. | |
| ## v2.0.0, 28th of April 2021 | |
| This is a big release, with at lof of breaking changes. You will likely | |
| need to install Demucs from scratch. | |
| - Demucs now supports on the fly resampling by a factor of 2. | |
| This improves SDR almost 0.3 points. | |
| - Random scaling of each source added (From Uhlich et al. 2017). | |
| - Random pitch and tempo augmentation addded, from [Cohen-Hadria et al. 2019]. | |
| - With extra augmentation, the best performing Demucs model now has only 64 channels | |
| instead of 100, so model size goes from 2.4GB to 1GB. Also SDR is up from 5.6 SDR to 6.3 when trained only on MusDB. | |
| - Quantized model using [DiffQ](https://github.com/facebookresearch/diffq) has been added. Model size is 150MB, no loss in quality as far as I, or the metrics, | |
| can say. | |
| - Pretrained models are now using the TorchHub interface. | |
| - Overlap mode for separation, to limit inconsitencies at | |
| frame boundaries, with linear transition over the overlap. Overlap is currently | |
| at 25%. Not that this is only done for separation, not training, because | |
| I added that quite late to the code. For Conv-TasNet this can improve | |
| SDR quite a bit (+0.3 points, to 6.0). | |
| - PyPI hosting, for separation, not training! | |