diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..eca3302375352e71b0547294ea0688d76cfa5160 Binary files /dev/null and b/.DS_Store differ diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000000000000000000000000000000000000..f049d4c53173cc44e0d0755b874d108891a5bfc5 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. Representation of +a project may be further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at . All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000000000000000000000000000000000..f14f4af3376097c8b0f76f48d2c91c31165ae0db --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,23 @@ +# Contributing to Demucs + +## Pull Requests + +In order to accept your pull request, we need you to submit a CLA. You only need +to do this once to work on any of Facebook's open source projects. + +Complete your CLA here: + +Demucs is the implementation of a research paper. +Therefore, we do not plan on accepting many pull requests for new features. +We certainly welcome them for bug fixes. + + +## Issues + +We use GitHub issues to track public bugs. Please ensure your description is +clear and has sufficient instructions to be able to reproduce the issue. + + +## License +By contributing to this repository, you agree that your contributions will be licensed +under the LICENSE file in the root directory of this source tree. diff --git a/Demucs.ipynb b/Demucs.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7f36b92a0cc269634c8d8393e73fdceffed5ffd9 --- /dev/null +++ b/Demucs.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Be9yoh-ILfRr" + }, + "source": [ + "# [*Colab code for Demucs*](https://github.com/facebookresearch/demucs/)\n", + "\n", + "Original version by marlluslustosa **https://github.com/marlluslustosa/demucs/blob/master/Demucs.ipynb**\n", + "\n", + "However, now things are much simpler with Demucs v2, so this might not be so useful. There is now a Colab version:\n", + "https://colab.research.google.com/drive/1jCegIzLIuqqcM85uVs3WCeAJiSoYq3oh?usp=sharing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 12277, + "status": "ok", + "timestamp": 1583778134659, + "user": { + "displayName": "Marllus Lustosa", + "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GgLl2RbW64ZyWz3Y8IBku0zhHCMnt7fz7fEl0LTdA=s64", + "userId": "14811735256675200480" + }, + "user_tz": 180 + }, + "id": "kOjIPLlzhPfn", + "outputId": "c75f17ec-b576-4105-bc5b-c2ac9c1018a3" + }, + "outputs": [], + "source": [ + "!pip install demucs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Y1BdlzOQi3y7" + }, + "source": [ + "\n", + "\n", + "---\n", + "\n", + "\n", + "# **Here begins the code for separating the audio source (model pretrained)**\n", + "###**- Upload your song to demucs/ folder and edit YOUR-SONG-PATH.mp3**\n", + "\n", + "\n", + "---\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": {}, + "colab_type": "code", + "id": "5lYOzKKCKAbJ" + }, + "outputs": [], + "source": [ + "!python3 -m demucs.separate test.mp3" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "authorship_tag": "ABX9TyM9xpVr1M86NRcjtQ7g9tCx", + "collapsed_sections": [], + "name": "Demucs.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..b96dcb0480a0b0be0727976e5202a1e7b23edc3f --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) Facebook, Inc. and its affiliates. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000000000000000000000000000000000000..e83b1e9ab473a583a426e66cc3127b0b4eefa79f --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,6 @@ +include *.md +include LICENSE +include setup.cfg +incude demucs.png +include requirements.txt +recursive-include docs *.md diff --git a/Makefile b/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..1b77c148d75db403a36e4e2983197c9cd5e6b334 --- /dev/null +++ b/Makefile @@ -0,0 +1,19 @@ +default: tests + +all: linter tests docs dist + +linter: + flake8 demucs + +tests: + python3 -m demucs.separate -n demucs_unittest test.mp3 + python3 -m demucs.separate -n demucs_unittest --mp3 test.mp3 + +dist: + python3 setup.py sdist + +clean: + rm -r dist build *.egg-info + + +.PHONY: linter tests dist diff --git a/README.md b/README.md index 349788c29ef7b88fe8ebe7896cba5d0435596460..ad18047c6a07de9b9cffd57998c02ff3836a2ac8 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,379 @@ ---- -title: Demucs Playground -emoji: 🌍 -colorFrom: green -colorTo: yellow -sdk: gradio -sdk_version: 3.12.0 -app_file: app.py -pinned: false -license: mit ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# Music Source Separation in the Waveform Domain + +![tests badge](https://github.com/facebookresearch/demucs/workflows/tests/badge.svg) +![linter badge](https://github.com/facebookresearch/demucs/workflows/linter/badge.svg) + +**Branch was rename to main**: Run `git pull && git checkout main` to switch to the new branch. + +**Demucs was just updated!**: much better SDR, smaller models, more data augmentation and PyPI support. + +**For the initial version of Demucs:** [Go this commit][original_demucs]. +If you are experiencing issues and want the old Demucs back, please fill an issue, and then you can get back to the v1 with +`git checkout v1`. + +We provide an implementation of Demucs and Conv-Tasnet for music source separation on the [MusDB][musdb] dataset. +They can separate drums, bass and vocals from the rest with state-of-the-art results, surpassing previous waveform or spectrogram based methods. +The architecture and results obtained are detailed in our paper +[Music Source Separation in the waveform domain][demucs_arxiv]. + +Demucs is based on U-Net convolutional architecture inspired by [Wave-U-Net][waveunet] and +[SING][sing], with GLUs, a BiLSTM between the encoder and decoder, specific initialization of weights +and transposed convolutions in the decoder. + +[Conv-Tasnet](https://arxiv.org/abs/1809.07454) +is a separation model developed for speech which predicts a mask on a learnt over-complete linear representation +using a purely convolutional model with stride of 1 and dilated convolutional blocks. +We reused the code from the [kaituoxu/Conv-TasNet][tasnet] +repository and added support for multiple audio channels. + + +Demucs achieves a state-of-the-art SDR performance of 6.3 when trained only on MusDB. +Conv-Tasnet achieves an SDR of 5.7, to be compared with the best performing spectrogram domain model [D3Net][d3net] +with an average SDR of 6. +Unlike Conv-Tasnet, Demucs reacts positively to pitch/tempo shift augmentation (+0.5 SDR). However, Demucs +still suffers from leakage from other sources, in particular between the vocals and other sources, which is less of a problem +for Conv-Tasnet. When trained with 150 extra tracks, Demucs reaches an SDR of 6.8, and even surpasses the IRM oracle +for the bass source (7.6 against 7.1 for the oracle). +See [our paper][demucs_arxiv] Section 6 for more details or listen to our +[audio samples][audio] . + +

+Schema representing the structure of Demucs, + with a convolutional encoder, a BiLSTM, and a decoder based on transposed convolutions.

+ + +## Important news if you are already using Demucs + +See the [release notes](./docs/release.md) for more details. + +- 11/05/2021: Adding support for MusDB-HQ and arbitrary wav set, for the MDX challenge. For more information +on joining the challenge with Demucs see [the Demucs MDX instructions](docs/mdx.md) +- 28/04/2021: **Demucs v2**, with extra augmentation and DiffQ based quantization. + **EVERYTHING WILL BREAK**, please restart from scratch following the instructions hereafter. + This version also adds overlap between prediction frames, with linear transition from one to the next, + which should prevent sudden changes at frame boundaries. Also, Demucs is now on PyPI, so for separation + only, installation is as easy as `pip install demucs` :) +- 13/04/2020: **Demucs released under MIT**: We are happy to release Demucs under the MIT licence. + We hope that this will broaden the impact of this research to new applications. + + +## Comparison with other models + +An audio comparison of Demucs and Conv-Tasnet with other state-of-the-art methods such as [Wave-U-Net][waveunet], [OpenUnmix][openunmix] or +[MMDenseLSTM][mmdenselstm] is available on [the audio comparison page][audio]. +We provide hereafter a summary of the different metrics presented in the paper. +You can also compare [Spleeter][spleeter], Open-Unmix, Demucs and Conv-Tasnet on one of my favorite +songs on our [soundcloud playlist][soundcloud]. + +### Comparison of accuracy + +`Overall SDR` is the mean of the SDR for each of the 4 sources, `MOS Quality` is a rating from 1 to 5 +of the naturalness and absence of artifacts given by human listeners (5 = no artifacts), `MOS Contamination` +is a rating from 1 to 5 with 5 being zero contamination by other sources. We refer the reader to our [paper][demucs_arxiv], Section 5 and 6, +for more details. + +| Model | Domain | Extra data? | Overall SDR | MOS Quality | MOS Contamination | +| ------------- |-------------| -----:|------:|----:|----:| +| [Open-Unmix][openunmix] | spectrogram | no | 5.3 | 3.0 | 3.3 | +| [D3Net][d3net] | spectrogram | no | 6.0 | - | - | +| [Wave-U-Net][waveunet] | waveform | no | 3.2 | - | - | +| Demucs (this) | waveform | no | **6.3** | **3.2** | 3.3 | +| Conv-Tasnet (this) | waveform | no | 5.7 | 2.9 | **3.4** | +| Demucs (this) | waveform | 150 songs | **6.8** | - | - | +| Conv-Tasnet (this) | waveform | 150 songs | 6.3 | - | - | +| [MMDenseLSTM][mmdenselstm] | spectrogram | 804 songs | 6.0 | - | - | +| [D3Net][d3net] | spectrogram | 1.5k songs | 6.7 | - | - | +| [Spleeter][spleeter] | spectrogram | 25k songs | 5.9 | - | - | + + + +## Requirements + +You will need at least Python 3.7. See `requirements.txt` for requirements for separation only, +and `environment-[cpu|cuda].yml` if you want to train a new model. + +### For Windows users + +Everytime you see `python3`, replace it with `python.exe`. You should always run commands from the +Anaconda console. + +### For musicians + +If you just want to use Demucs to separate tracks, you can install it with + + python3 -m pip -U install demucs + +Advanced OS support are provided on the following page, **you must read the page for your OS before posting an issues**: +- **If you are using Windows:** [Windows support](docs/windows.md). +- **If you are using MAC OS X:** [Mac OS X support](docs/mac.md). +- **If you are using Linux:** [Linux support](docs/linux.md). + +### For machine learning scientists + +If you have anaconda installed, you can run from the root of this repository: + + conda env update -f environment-cpu.yml # if you don't have GPUs + conda env update -f environment-cuda.yml # if you have GPUs + conda activate demucs + pip install -e . + +This will create a `demucs` environment with all the dependencies installed. + + +You will also need to install [soundstretch/soundtouch](https://www.surina.net/soundtouch/soundstretch.html): on Mac OSX you can do `brew install sound-touch`, +and on Ubuntu `sudo apt-get install soundstretch`. This is used for the +pitch/tempo augmentation. + +### Running in Docker + +Thanks to @xserrat, there is now a Docker image definition ready for using Demucs. This can ensure all libraries are correctly installed without interfering with the host OS. See his repo [Docker Facebook Demucs](https://github.com/xserrat/docker-facebook-demucs) for more information. + + +### Running from Colab + +I made a Colab to easily separate track with Demucs. Note that +transfer speeds with Colab are a bit slow for large media files, +but it will allow you to use Demucs without installing anything. + +[Demucs on Google Colab](https://colab.research.google.com/drive/1jCegIzLIuqqcM85uVs3WCeAJiSoYq3oh?usp=sharing) + +## Separating tracks + +In order to try Demucs or Conv-Tasnet on your tracks, simply run from the root of this repository + +```bash +python3 -m demucs.separate PATH_TO_AUDIO_FILE_1 [PATH_TO_AUDIO_FILE_2 ...] # for Demucs +python3 -m demucs.separate --mp3 PATH_TO_AUDIO_FILE_1 --mp3-bitrate BITRATE # output files saved as MP3 +python3 -m demucs.separate -n tasnet PATH_TO_AUDIO_FILE_1 ... # for Conv-Tasnet +``` + +If you have a GPU, but you run out of memory, please add `-d cpu` to the command line. See the section hereafter for more details on the memory requirements for GPU acceleration. + +Separated tracks are stored in the `separated/MODEL_NAME/TRACK_NAME` folder. There you will find four stereo wav files sampled at 44.1 kHz: `drums.wav`, `bass.wav`, +`other.wav`, `vocals.wav` (or `.mp3` if you used the `--mp3` option). + +All audio formats supported by `torchaudio` can be processed (i.e. wav, mp3, flac, ogg/vorbis etc.). +Audio is resampled on the fly if necessary. +The output will be a wave file, either in int16 format or float32 (if `--float32` is passed). +You can pass `--mp3` to save as mp3 instead, and set the bitrate with `--mp3-bitrate` (default is 320kbps). + +Other pre-trained models can be selected with the `-n` flag. +The list of pre-trained models is: +- `demucs`: Demucs trained on MusDB, +- `demucs_quantized`: Quantized Demucs with [diffq](https://github.com/facebookresearch/diffq), + this is much smaller (150MB instead of 1GB) and quality should be exactly the same. Let me know if you disagree. + As a result, this is the one used by default. +- `demucs_extra`: Demucs trained with extra training data, +- `demucs48_hq`: Demucs with 48 initial hidden channels, trained on [MusDB-HQ](https://zenodo.org/record/3338373), + used as a baseline for the [Music Demixing Challenge 2021](https://www.aicrowd.com/challenges/music-demixing-challenge-ismir-2021), +- `tasnet`: Conv-Tasnet trained on MusDB, +- `tasnet_extra`: Conv-Tasnet trained with extra training data. + + +The `--shifts=SHIFTS` performs multiple predictions with random shifts (a.k.a the *shift trick*) of the input and average them. This makes prediction `SHIFTS` times +slower but improves the accuracy of Demucs by 0.2 points of SDR. +It has limited impact on Conv-Tasnet as the model is by nature almost time equivariant. +The value of 10 was used on the original paper, although 5 yields mostly the same gain. +It is deactivated by default but it does make vocals a bit smoother. + +The `--overlap` option controls the amount of overlap between prediction windows (for Demucs one window is 10 seconds). +Default is 0.25 (i.e. 25%) which is probably fine. + + +### Memory requirements for GPU acceleration + +If you want to use GPU acceleration, you will need at least 8GB of RAM on your GPU for `demucs` and 4GB for `tasnet`. Sorry, the code for demucs is not super optimized for memory! If you do not have enough memory on your GPU, simply add `-d cpu` to the command line to use the CPU. With Demucs, processing time should be roughly equal to the duration of the track. + + +## Examining the results from the paper experiments + +The metrics for our experiments are stored in the `results` folder. In particular +`museval` json evaluations are stored in `results/evals/EXPERIMENT NAME/results`. +You can aggregate and display the results using +```bash +python3 valid_table.py -p # show valid loss, aggregated with multiple random seeds +python3 result_table.py -p # show SDR on test set, aggregated with multiple random seeds +python3 result_table.py -p SIR # also SAR, ISR, show other metrics +``` +The `std` column shows the standard deviation divided by the square root of the number of runs. + +## Training Demucs and evaluating on the MusDB dataset + +If you want to train Demucs from scratch, you will need a copy of the MusDB dataset. +It can be obtained on the [MusDB website][musdb]. +To start training on a single GPU or CPU, use: +```bash +python3 -m demucs -b 4 --musdb MUSDB_PATH # Demucs +python3 -m demucs -b 4 --musdb MUSDB_PATH --tasnet --samples=80000 --split_valid # Conv-Tasnet +``` +The `-b 4` flag will set the batch size to 4. The default is 4 and will crash on a single GPU. +Demucs was trained on 8 V100 with 32GB of RAM. +The default parameters (batch size, number of channels etc) +might not be suitable for 16GB GPUs. +To train on all available GPUs, use: +```bash +python3 run.py --musdb MUSDB_PATH [EXTRA_FLAGS] +``` + +This will launch one process per GPU and report the output of the first one. When interrupting +such a run, it is possible some of the children processes are not killed properly, be mindful of that. +If you want to use only some of the available GPUs, export the `CUDA_VISIBLE_DEVICES` variable to +select those. + +To see all the possible options, use `python3 -m demucs --help`. + + +### MusDB HQ + +To train on MusDB HQ, use the following flags: + +```bash +python3 -m demucs -b 4 --musdb MUSDB_HQ_PATH --is_wav [...] +``` + +### Custom wav dataset + +You can trained on a custom wav dataset using the following command. +At the moment, you still need to pass the MusDB path for evaluation, and the model +must use the standard sources (bass, drums, other, vocals). However, it should be relatively +easy to fork the code to support different patterns. + +```bash +python3 -m demucs -b 4 --wav PATH_TO_WAV_DATASET [...] +``` + +The folder `PATH_TO_WAV_DATASET` should contain two sub-directories : `train` and `valid`. Each of those +should contain one folder per track. Each track folder must contain one file for each source (`drums.wav`, `bass.wav`, `other.wav`, `vocals.wav`) and one file for the mixture (`mixture.wav`). + +By default, the custom wav dataset will replace MusDB. To concatenate it with MusDB, pass `--concat` (if you are using musdbhq, dont forget to pass `--is_wav`). + +### Fine tuning + +You can fine tune from one of the pre-trained models listed in the [Separating tracks Section](#separating-tracks) +by passing the `--init=PRETRAINED_NAME`, i.e. for Demucs or ConvTasnet: + +```bash +python3 -m demucs -b 4 --musdb MUSDB_PATH --init demucs # Demucs +python3 -m demucs -b 4 --musdb MUSDB_PATH --tasnet --samples=80000 --split_valid --init tasnet # Conv-Tasnet +``` + +### About checkpointing + +Demucs will automatically generate an experiment name from the command line flags you provided. +It will checkpoint after every epoch. If a checkpoint already exist for the combination of flags +you provided, it will be automatically used. In order to ignore/delete a previous checkpoint, +run with the `-R` flag. +The optimizer state, the latest model and the best model on valid are stored. At the end of each +epoch, the checkpoint will erase the one from the previous epoch. +By default, checkpoints are stored in the `./checkpoints` folder. This can be changed using the +`--checkpoints CHECKPOINT_FOLDER` flag. + +Not all options will impact the name of the experiment. For instance `--workers` is not +shown in the name, therefore, changing this parameter will not impact the checkpoint file +used. Refer to [parser.py](demucs/parser.py) for more details. + + +### Test set evaluations + +Test set evaluations computed with [museval][museval] will be stored under +`evals/EXPERIMENT NAME/results`. The experiment name +is the first thing printed when running `python3 run.py` or `python3 -m demucs`. If you used +the flag `--save`, there will also be a folder `evals/EXPERIMENT NAME/wavs` containing +all the extracted waveforms. + + +#### Running on a cluster + +If you have a cluster available with Slurm, you can set the `run_slurm.py` as the target of a +slurm job, using as many nodes as you want and a single task per node. `run_slurm.py` will +create one process per GPU and run in a distributed manner. Multinode training is supported. + +### Extracting Raw audio for faster loading + +We observed that loading from compressed mp4 audio lead to unreliable speed, sometimes reducing by +a factor of 2 the number of iterations per second. It is possible to extract all data +to raw PCM f32e format. If you wish to store the raw data under `RAW_PATH`, run the following +command first: + +```bash +python3 -m demucs.raw [--workers=10] MUSDB_PATH RAW_PATH +``` + +You can then train using the `--raw RAW_PATH` flag, for instance: +```bash +python3 run.py --raw RAW_PATH --musdb MUSDB_PATH +``` +You still need to provide the path to the MusDB dataset as we always load the test set +from the original MusDB. + + +### Results reproduction +To reproduce the performance of the main Demucs model in our paper: +```bash +# Extract raw waveforms. This is optional +python3 -m demucs.data MUSDB_PATH RAW_PATH +export DEMUCS_RAW=RAW_PATH +# Train models with default parameters and multiple seeds +python3 run.py --seed 42 # for Demucs +python3 run.py --seed 42 --tasnet --X=10 --samples=80000 --epochs=180 --split_valid # for Conv-Tasnet +# Repeat for --seed = 43, 44, 45 and 46 +``` + +You can visualize the results aggregated on multiple seeds using +```bash +python3 valid_table.py # compare validation losses +python3 result_table.py # compare test SDR +python3 result_table.py SIR # compare test SIR, also available ISR, and SAR +``` + +You can look at our exploration file [dora.py](dora.py) to see the exact flags +for all experiments (grid search and ablation study). If you have a Slurm cluster, +you can also try adapting it to run on your own. + + +### Environment variables + +If you do not want to always specify the path to MUSDB, you can export the following variables: +```bash +export DEMUCS_MUSDB=PATH TO MUSDB +# Optionally, if you extracted raw pcm data +# export DEMUCS_RAW=PATH TO RAW PCM +``` + +## How to cite + +``` +@article{defossez2019music, + title={Music Source Separation in the Waveform Domain}, + author={D{\'e}fossez, Alexandre and Usunier, Nicolas and Bottou, L{\'e}on and Bach, Francis}, + journal={arXiv preprint arXiv:1911.13254}, + year={2019} +} +``` + +## License + +Demucs is released under the MIT license as found in the [LICENSE](LICENSE) file. + +The file `demucs/tasnet.py` is adapted from the [kaituoxu/Conv-TasNet][tasnet] repository. +It was originally released under the MIT License updated to support multiple audio channels. + + +[nsynth]: https://magenta.tensorflow.org/datasets/nsynth +[sing_nips]: https://research.fb.com/publications/sing-symbol-to-instrument-neural-generator +[sing]: https://github.com/facebookresearch/SING +[waveunet]: https://github.com/f90/Wave-U-Net +[musdb]: https://sigsep.github.io/datasets/musdb.html +[museval]: https://github.com/sigsep/sigsep-mus-eval/ +[openunmix]: https://github.com/sigsep/open-unmix-pytorch +[mmdenselstm]: https://arxiv.org/abs/1805.02410 +[demucs_arxiv]: https://hal.archives-ouvertes.fr/hal-02379796/document +[musevalpth]: museval_torch.py +[tasnet]: https://github.com/kaituoxu/Conv-TasNet +[audio]: https://ai.honu.io/papers/demucs/index.html +[spleeter]: https://github.com/deezer/spleeter +[soundcloud]: https://soundcloud.com/voyageri/sets/source-separation-in-the-waveform-domain +[original_demucs]: https://github.com/facebookresearch/demucs/tree/dcee007a350467abc3295dfe267034460f9ffa4e +[diffq]: https://github.com/facebookresearch/diffq +[d3net]: https://arxiv.org/abs/2010.01733 diff --git a/baselines/.DS_Store b/baselines/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..9b5b2d6b47c9387d45918adfbbdcf8c23f12f941 Binary files /dev/null and b/baselines/.DS_Store differ diff --git a/baselines/IRM2/test/AM Contra - 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In MMDenseLSTM, some of dense-blocks, typically in low scale, equip a LSTM layer, as shown in Figure 1. Each LSTM layer is one layer bi-directional LSTM with maximum 128 cells. +For each instrument, a MMDeseLSTM is +trained to predict the target instrument amplitude from the mixture +amplitude in the STFT domain (frame size: 4096, hop size: 1024). The raw +output of each network is then combined by a multichannel Wiener filter as +described in [2] where we estimate the power spectral densities and spatial +covariance matrices from the DNN outputs. +More details, experiments and analysis is described in a paper which is submitted to elsewhere. + +We used the `train` part of `musdb` and an internal database with 804 songs as development set and randomly chose 48 songs as validation set, which is used to +perform early stopping and hyperparameter selection (growth rate, +number of layers). + + +Figure 1. MDenseLSTM architecture (scale=3). A LSTM layer is added to the bottleneck DenseBlock of MDenseNet[1]. + +## References +1. N. Takahashi and Y. Mitsufuji: Multi-scale multi-band DenseNets for audio source separation, Proc. WASPAA, 2017 +2. A. A. Nugraha, A. Liutkus, and E. Vincent. "Multichannel music separation with deep neural networks." EUSIPCO, 2016. +3. N. Takahashi, N. Goswami and Y. 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For the same model applied to singing voice separation, see STL1 submission. + +We use the Wave-U-Net, an adaptation of the U-Net architecture to the one-dimensional time domain to perform end-to-end audio source separation. Through a series of downsampling and upsampling blocks, which involve convolutions combined with a down-/upsampling process, features are computed on multiple scales/levels of abstraction and time resolution, and combined to make a prediction. Training is done on 75 MUSDB training set songs, validation with early stopping on the remaining 25 MUSDB songs, both from the training set. Training loss is the MSE on the raw audio source outputs. + +A paper with more details, experiments and analysis is currently under review elsewhere. + + + +## References + +- Liutkus, Antoine and Fitzgerald, Derry and Rafii, Zafar: Scalable audio separation with light kernel additive modelling. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015 +- Ronneberger, O. and Fischer, P. and Brox, T.: U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015 +- Jansson, Andreas and Humphrey, Eric J. and Montecchio, Nicola and Bittner, Rachel and Kumar, Aparna and Weyde, Tillman: Singing Voice Separation with Deep U-Net Convolutional Networks. Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2017. +- [Stoller, Daniel and Ewert, Sebastian and Dixon, Simon. Wave-U-Net: A multi-scale neural network for end-to-end source separation. 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" + "To download the MusDB dataset, see https://sigsep.github.io/datasets/musdb.html.", + file=sys.stderr) + sys.exit(1) + + eval_folder = args.evals / name + eval_folder.mkdir(exist_ok=True, parents=True) + args.logs.mkdir(exist_ok=True) + metrics_path = args.logs / f"{name}.json" + eval_folder.mkdir(exist_ok=True, parents=True) + args.checkpoints.mkdir(exist_ok=True, parents=True) + args.models.mkdir(exist_ok=True, parents=True) + + if args.device is None: + device = "cpu" + if th.cuda.is_available(): + device = "cuda" + else: + device = args.device + + th.manual_seed(args.seed) + # Prevents too many threads to be started when running `museval` as it can be quite + # inefficient on NUMA architectures. + os.environ["OMP_NUM_THREADS"] = "1" + os.environ["MKL_NUM_THREADS"] = "1" + + if args.world_size > 1: + if device != "cuda" and args.rank == 0: + print("Error: distributed training is only available with cuda device", file=sys.stderr) + sys.exit(1) + th.cuda.set_device(args.rank % th.cuda.device_count()) + distributed.init_process_group(backend="nccl", + init_method="tcp://" + args.master, + rank=args.rank, + world_size=args.world_size) + + checkpoint = args.checkpoints / f"{name}.th" + checkpoint_tmp = args.checkpoints / f"{name}.th.tmp" + if args.restart and checkpoint.exists() and args.rank == 0: + checkpoint.unlink() + + if args.test or args.test_pretrained: + args.epochs = 1 + args.repeat = 0 + if args.test: + model = load_model(args.models / args.test) + else: + model = load_pretrained(args.test_pretrained) + elif args.tasnet: + model = ConvTasNet(audio_channels=args.audio_channels, + samplerate=args.samplerate, X=args.X, + segment_length=4 * args.samples, + sources=SOURCES) + else: + model = Demucs( + audio_channels=args.audio_channels, + channels=args.channels, + context=args.context, + depth=args.depth, + glu=args.glu, + growth=args.growth, + kernel_size=args.kernel_size, + lstm_layers=args.lstm_layers, + rescale=args.rescale, + rewrite=args.rewrite, + stride=args.conv_stride, + resample=args.resample, + normalize=args.normalize, + samplerate=args.samplerate, + segment_length=4 * args.samples, + sources=SOURCES, + ) + model.to(device) + if args.init: + model.load_state_dict(load_pretrained(args.init).state_dict()) + + if args.show: + print(model) + size = sizeof_fmt(4 * sum(p.numel() for p in model.parameters())) + print(f"Model size {size}") + return + + try: + saved = th.load(checkpoint, map_location='cpu') + except IOError: + saved = SavedState() + + optimizer = th.optim.Adam(model.parameters(), lr=args.lr) + + quantizer = None + quantizer = get_quantizer(model, args, optimizer) + + if saved.last_state is not None: + model.load_state_dict(saved.last_state, strict=False) + if saved.optimizer is not None: + optimizer.load_state_dict(saved.optimizer) + + model_name = f"{name}.th" + if args.save_model: + if args.rank == 0: + model.to("cpu") + assert saved.best_state is not None, "model needs to train for 1 epoch at least." + model.load_state_dict(saved.best_state) + save_model(model, quantizer, args, args.models / model_name) + return + elif args.save_state: + model_name = f"{args.save_state}.th" + if args.rank == 0: + model.to("cpu") + model.load_state_dict(saved.best_state) + state = get_state(model, quantizer) + save_state(state, args.models / model_name) + return + + if args.rank == 0: + done = args.logs / f"{name}.done" + if done.exists(): + done.unlink() + + augment = [Shift(args.data_stride)] + if args.augment: + augment += [FlipSign(), FlipChannels(), Scale(), + Remix(group_size=args.remix_group_size)] + augment = nn.Sequential(*augment).to(device) + print("Agumentation pipeline:", augment) + + if args.mse: + criterion = nn.MSELoss() + else: + criterion = nn.L1Loss() + + # Setting number of samples so that all convolution windows are full. + # Prevents hard to debug mistake with the prediction being shifted compared + # to the input mixture. + samples = model.valid_length(args.samples) + print(f"Number of training samples adjusted to {samples}") + samples = samples + args.data_stride + if args.repitch: + # We need a bit more audio samples, to account for potential + # tempo change. + samples = math.ceil(samples / (1 - 0.01 * args.max_tempo)) + + args.metadata.mkdir(exist_ok=True, parents=True) + if args.raw: + train_set = Rawset(args.raw / "train", + samples=samples, + channels=args.audio_channels, + streams=range(1, len(model.sources) + 1), + stride=args.data_stride) + + valid_set = Rawset(args.raw / "valid", channels=args.audio_channels) + elif args.wav: + train_set, valid_set = get_wav_datasets(args, samples, model.sources) + + if args.concat: + if args.is_wav: + mus_train, mus_valid = get_musdb_wav_datasets(args, samples, model.sources) + else: + mus_train, mus_valid = get_compressed_datasets(args, samples) + train_set = ConcatDataset([train_set, mus_train]) + valid_set = ConcatDataset([valid_set, mus_valid]) + elif args.is_wav: + train_set, valid_set = get_musdb_wav_datasets(args, samples, model.sources) + else: + train_set, valid_set = get_compressed_datasets(args, samples) + print("Train set and valid set sizes", len(train_set), len(valid_set)) + + if args.repitch: + train_set = RepitchedWrapper( + train_set, + proba=args.repitch, + max_tempo=args.max_tempo) + + best_loss = float("inf") + for epoch, metrics in enumerate(saved.metrics): + print(f"Epoch {epoch:03d}: " + f"train={metrics['train']:.8f} " + f"valid={metrics['valid']:.8f} " + f"best={metrics['best']:.4f} " + f"ms={metrics.get('true_model_size', 0):.2f}MB " + f"cms={metrics.get('compressed_model_size', 0):.2f}MB " + f"duration={human_seconds(metrics['duration'])}") + best_loss = metrics['best'] + + if args.world_size > 1: + dmodel = DistributedDataParallel(model, + device_ids=[th.cuda.current_device()], + output_device=th.cuda.current_device()) + else: + dmodel = model + + for epoch in range(len(saved.metrics), args.epochs): + begin = time.time() + model.train() + train_loss, model_size = train_model( + epoch, train_set, dmodel, criterion, optimizer, augment, + quantizer=quantizer, + batch_size=args.batch_size, + device=device, + repeat=args.repeat, + seed=args.seed, + diffq=args.diffq, + workers=args.workers, + world_size=args.world_size) + model.eval() + valid_loss = validate_model( + epoch, valid_set, model, criterion, + device=device, + rank=args.rank, + split=args.split_valid, + overlap=args.overlap, + world_size=args.world_size) + + ms = 0 + cms = 0 + if quantizer and args.rank == 0: + ms = quantizer.true_model_size() + cms = quantizer.compressed_model_size(num_workers=min(40, args.world_size * 10)) + + duration = time.time() - begin + if valid_loss < best_loss and ms <= args.ms_target: + best_loss = valid_loss + saved.best_state = { + key: value.to("cpu").clone() + for key, value in model.state_dict().items() + } + + saved.metrics.append({ + "train": train_loss, + "valid": valid_loss, + "best": best_loss, + "duration": duration, + "model_size": model_size, + "true_model_size": ms, + "compressed_model_size": cms, + }) + if args.rank == 0: + json.dump(saved.metrics, open(metrics_path, "w")) + + saved.last_state = model.state_dict() + saved.optimizer = optimizer.state_dict() + if args.rank == 0 and not args.test: + th.save(saved, checkpoint_tmp) + checkpoint_tmp.rename(checkpoint) + + print(f"Epoch {epoch:03d}: " + f"train={train_loss:.8f} valid={valid_loss:.8f} best={best_loss:.4f} ms={ms:.2f}MB " + f"cms={cms:.2f}MB " + f"duration={human_seconds(duration)}") + + if args.world_size > 1: + distributed.barrier() + + del dmodel + model.load_state_dict(saved.best_state) + if args.eval_cpu: + device = "cpu" + model.to(device) + model.eval() + evaluate(model, args.musdb, eval_folder, + is_wav=args.is_wav, + rank=args.rank, + world_size=args.world_size, + device=device, + save=args.save, + split=args.split_valid, + shifts=args.shifts, + overlap=args.overlap, + workers=args.eval_workers) + model.to("cpu") + if args.rank == 0: + if not (args.test or args.test_pretrained): + save_model(model, quantizer, args, args.models / model_name) + print("done") + done.write_text("done") + + +if __name__ == "__main__": + main() diff --git a/demucs/audio.py b/demucs/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..b29f156e4afb5fbda32c35777022caeadf50d711 --- /dev/null +++ b/demucs/audio.py @@ -0,0 +1,172 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +import json +import subprocess as sp +from pathlib import Path + +import julius +import numpy as np +import torch + +from .utils import temp_filenames + + +def _read_info(path): + stdout_data = sp.check_output([ + 'ffprobe', "-loglevel", "panic", + str(path), '-print_format', 'json', '-show_format', '-show_streams' + ]) + return json.loads(stdout_data.decode('utf-8')) + + +class AudioFile: + """ + Allows to read audio from any format supported by ffmpeg, as well as resampling or + converting to mono on the fly. See :method:`read` for more details. + """ + def __init__(self, path: Path): + self.path = Path(path) + self._info = None + + def __repr__(self): + features = [("path", self.path)] + features.append(("samplerate", self.samplerate())) + features.append(("channels", self.channels())) + features.append(("streams", len(self))) + features_str = ", ".join(f"{name}={value}" for name, value in features) + return f"AudioFile({features_str})" + + @property + def info(self): + if self._info is None: + self._info = _read_info(self.path) + return self._info + + @property + def duration(self): + return float(self.info['format']['duration']) + + @property + def _audio_streams(self): + return [ + index for index, stream in enumerate(self.info["streams"]) + if stream["codec_type"] == "audio" + ] + + def __len__(self): + return len(self._audio_streams) + + def channels(self, stream=0): + return int(self.info['streams'][self._audio_streams[stream]]['channels']) + + def samplerate(self, stream=0): + return int(self.info['streams'][self._audio_streams[stream]]['sample_rate']) + + def read(self, + seek_time=None, + duration=None, + streams=slice(None), + samplerate=None, + channels=None, + temp_folder=None): + """ + Slightly more efficient implementation than stempeg, + in particular, this will extract all stems at once + rather than having to loop over one file multiple times + for each stream. + + Args: + seek_time (float): seek time in seconds or None if no seeking is needed. + duration (float): duration in seconds to extract or None to extract until the end. + streams (slice, int or list): streams to extract, can be a single int, a list or + a slice. If it is a slice or list, the output will be of size [S, C, T] + with S the number of streams, C the number of channels and T the number of samples. + If it is an int, the output will be [C, T]. + samplerate (int): if provided, will resample on the fly. If None, no resampling will + be done. Original sampling rate can be obtained with :method:`samplerate`. + channels (int): if 1, will convert to mono. We do not rely on ffmpeg for that + as ffmpeg automatically scale by +3dB to conserve volume when playing on speakers. + See https://sound.stackexchange.com/a/42710. + Our definition of mono is simply the average of the two channels. Any other + value will be ignored. + temp_folder (str or Path or None): temporary folder to use for decoding. + + + """ + streams = np.array(range(len(self)))[streams] + single = not isinstance(streams, np.ndarray) + if single: + streams = [streams] + + if duration is None: + target_size = None + query_duration = None + else: + target_size = int((samplerate or self.samplerate()) * duration) + query_duration = float((target_size + 1) / (samplerate or self.samplerate())) + + with temp_filenames(len(streams)) as filenames: + command = ['ffmpeg', '-y'] + command += ['-loglevel', 'panic'] + if seek_time: + command += ['-ss', str(seek_time)] + command += ['-i', str(self.path)] + for stream, filename in zip(streams, filenames): + command += ['-map', f'0:{self._audio_streams[stream]}'] + if query_duration is not None: + command += ['-t', str(query_duration)] + command += ['-threads', '1'] + command += ['-f', 'f32le'] + if samplerate is not None: + command += ['-ar', str(samplerate)] + command += [filename] + + sp.run(command, check=True) + wavs = [] + for filename in filenames: + wav = np.fromfile(filename, dtype=np.float32) + wav = torch.from_numpy(wav) + wav = wav.view(-1, self.channels()).t() + if channels is not None: + wav = convert_audio_channels(wav, channels) + if target_size is not None: + wav = wav[..., :target_size] + wavs.append(wav) + wav = torch.stack(wavs, dim=0) + if single: + wav = wav[0] + return wav + + +def convert_audio_channels(wav, channels=2): + """Convert audio to the given number of channels.""" + *shape, src_channels, length = wav.shape + if src_channels == channels: + pass + elif channels == 1: + # Case 1: + # The caller asked 1-channel audio, but the stream have multiple + # channels, downmix all channels. + wav = wav.mean(dim=-2, keepdim=True) + elif src_channels == 1: + # Case 2: + # The caller asked for multiple channels, but the input file have + # one single channel, replicate the audio over all channels. + wav = wav.expand(*shape, channels, length) + elif src_channels >= channels: + # Case 3: + # The caller asked for multiple channels, and the input file have + # more channels than requested. In that case return the first channels. + wav = wav[..., :channels, :] + else: + # Case 4: What is a reasonable choice here? + raise ValueError('The audio file has less channels than requested but is not mono.') + return wav + + +def convert_audio(wav, from_samplerate, to_samplerate, channels): + wav = convert_audio_channels(wav, channels) + return julius.resample_frac(wav, from_samplerate, to_samplerate) diff --git a/demucs/augment.py b/demucs/augment.py new file mode 100644 index 0000000000000000000000000000000000000000..bb36d3298d89470f306316322e7587187819c94b --- /dev/null +++ b/demucs/augment.py @@ -0,0 +1,106 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import random +import torch as th +from torch import nn + + +class Shift(nn.Module): + """ + Randomly shift audio in time by up to `shift` samples. + """ + def __init__(self, shift=8192): + super().__init__() + self.shift = shift + + def forward(self, wav): + batch, sources, channels, time = wav.size() + length = time - self.shift + if self.shift > 0: + if not self.training: + wav = wav[..., :length] + else: + offsets = th.randint(self.shift, [batch, sources, 1, 1], device=wav.device) + offsets = offsets.expand(-1, -1, channels, -1) + indexes = th.arange(length, device=wav.device) + wav = wav.gather(3, indexes + offsets) + return wav + + +class FlipChannels(nn.Module): + """ + Flip left-right channels. + """ + def forward(self, wav): + batch, sources, channels, time = wav.size() + if self.training and wav.size(2) == 2: + left = th.randint(2, (batch, sources, 1, 1), device=wav.device) + left = left.expand(-1, -1, -1, time) + right = 1 - left + wav = th.cat([wav.gather(2, left), wav.gather(2, right)], dim=2) + return wav + + +class FlipSign(nn.Module): + """ + Random sign flip. + """ + def forward(self, wav): + batch, sources, channels, time = wav.size() + if self.training: + signs = th.randint(2, (batch, sources, 1, 1), device=wav.device, dtype=th.float32) + wav = wav * (2 * signs - 1) + return wav + + +class Remix(nn.Module): + """ + Shuffle sources to make new mixes. + """ + def __init__(self, group_size=4): + """ + Shuffle sources within one batch. + Each batch is divided into groups of size `group_size` and shuffling is done within + each group separatly. This allow to keep the same probability distribution no matter + the number of GPUs. Without this grouping, using more GPUs would lead to a higher + probability of keeping two sources from the same track together which can impact + performance. + """ + super().__init__() + self.group_size = group_size + + def forward(self, wav): + batch, streams, channels, time = wav.size() + device = wav.device + + if self.training: + group_size = self.group_size or batch + if batch % group_size != 0: + raise ValueError(f"Batch size {batch} must be divisible by group size {group_size}") + groups = batch // group_size + wav = wav.view(groups, group_size, streams, channels, time) + permutations = th.argsort(th.rand(groups, group_size, streams, 1, 1, device=device), + dim=1) + wav = wav.gather(1, permutations.expand(-1, -1, -1, channels, time)) + wav = wav.view(batch, streams, channels, time) + return wav + + +class Scale(nn.Module): + def __init__(self, proba=1., min=0.25, max=1.25): + super().__init__() + self.proba = proba + self.min = min + self.max = max + + def forward(self, wav): + batch, streams, channels, time = wav.size() + device = wav.device + if self.training and random.random() < self.proba: + scales = th.empty(batch, streams, 1, 1, device=device).uniform_(self.min, self.max) + wav *= scales + return wav diff --git a/demucs/compressed.py b/demucs/compressed.py new file mode 100644 index 0000000000000000000000000000000000000000..eb8fbb75463ba71ca86729b22baebf24598ade57 --- /dev/null +++ b/demucs/compressed.py @@ -0,0 +1,115 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import json +from fractions import Fraction +from concurrent import futures + +import musdb +from torch import distributed + +from .audio import AudioFile + + +def get_musdb_tracks(root, *args, **kwargs): + mus = musdb.DB(root, *args, **kwargs) + return {track.name: track.path for track in mus} + + +class StemsSet: + def __init__(self, tracks, metadata, duration=None, stride=1, + samplerate=44100, channels=2, streams=slice(None)): + + self.metadata = [] + for name, path in tracks.items(): + meta = dict(metadata[name]) + meta["path"] = path + meta["name"] = name + self.metadata.append(meta) + if duration is not None and meta["duration"] < duration: + raise ValueError(f"Track {name} duration is too small {meta['duration']}") + self.metadata.sort(key=lambda x: x["name"]) + self.duration = duration + self.stride = stride + self.channels = channels + self.samplerate = samplerate + self.streams = streams + + def __len__(self): + return sum(self._examples_count(m) for m in self.metadata) + + def _examples_count(self, meta): + if self.duration is None: + return 1 + else: + return int((meta["duration"] - self.duration) // self.stride + 1) + + def track_metadata(self, index): + for meta in self.metadata: + examples = self._examples_count(meta) + if index >= examples: + index -= examples + continue + return meta + + def __getitem__(self, index): + for meta in self.metadata: + examples = self._examples_count(meta) + if index >= examples: + index -= examples + continue + streams = AudioFile(meta["path"]).read(seek_time=index * self.stride, + duration=self.duration, + channels=self.channels, + samplerate=self.samplerate, + streams=self.streams) + return (streams - meta["mean"]) / meta["std"] + + +def _get_track_metadata(path): + # use mono at 44kHz as reference. For any other settings data won't be perfectly + # normalized but it should be good enough. + audio = AudioFile(path) + mix = audio.read(streams=0, channels=1, samplerate=44100) + return {"duration": audio.duration, "std": mix.std().item(), "mean": mix.mean().item()} + + +def _build_metadata(tracks, workers=10): + pendings = [] + with futures.ProcessPoolExecutor(workers) as pool: + for name, path in tracks.items(): + pendings.append((name, pool.submit(_get_track_metadata, path))) + return {name: p.result() for name, p in pendings} + + +def _build_musdb_metadata(path, musdb, workers): + tracks = get_musdb_tracks(musdb) + metadata = _build_metadata(tracks, workers) + path.parent.mkdir(exist_ok=True, parents=True) + json.dump(metadata, open(path, "w")) + + +def get_compressed_datasets(args, samples): + metadata_file = args.metadata / "musdb.json" + if not metadata_file.is_file() and args.rank == 0: + _build_musdb_metadata(metadata_file, args.musdb, args.workers) + if args.world_size > 1: + distributed.barrier() + metadata = json.load(open(metadata_file)) + duration = Fraction(samples, args.samplerate) + stride = Fraction(args.data_stride, args.samplerate) + train_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="train"), + metadata, + duration=duration, + stride=stride, + streams=slice(1, None), + samplerate=args.samplerate, + channels=args.audio_channels) + valid_set = StemsSet(get_musdb_tracks(args.musdb, subsets=["train"], split="valid"), + metadata, + samplerate=args.samplerate, + channels=args.audio_channels) + return train_set, valid_set diff --git a/demucs/model.py b/demucs/model.py new file mode 100644 index 0000000000000000000000000000000000000000..e9d932f4d014f7b95b394d2e24ed5edc379ded8d --- /dev/null +++ b/demucs/model.py @@ -0,0 +1,202 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import math + +import julius +from torch import nn + +from .utils import capture_init, center_trim + + +class BLSTM(nn.Module): + def __init__(self, dim, layers=1): + super().__init__() + self.lstm = nn.LSTM(bidirectional=True, num_layers=layers, hidden_size=dim, input_size=dim) + self.linear = nn.Linear(2 * dim, dim) + + def forward(self, x): + x = x.permute(2, 0, 1) + x = self.lstm(x)[0] + x = self.linear(x) + x = x.permute(1, 2, 0) + return x + + +def rescale_conv(conv, reference): + std = conv.weight.std().detach() + scale = (std / reference)**0.5 + conv.weight.data /= scale + if conv.bias is not None: + conv.bias.data /= scale + + +def rescale_module(module, reference): + for sub in module.modules(): + if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d)): + rescale_conv(sub, reference) + + +class Demucs(nn.Module): + @capture_init + def __init__(self, + sources, + audio_channels=2, + channels=64, + depth=6, + rewrite=True, + glu=True, + rescale=0.1, + resample=True, + kernel_size=8, + stride=4, + growth=2., + lstm_layers=2, + context=3, + normalize=False, + samplerate=44100, + segment_length=4 * 10 * 44100): + """ + Args: + sources (list[str]): list of source names + audio_channels (int): stereo or mono + channels (int): first convolution channels + depth (int): number of encoder/decoder layers + rewrite (bool): add 1x1 convolution to each encoder layer + and a convolution to each decoder layer. + For the decoder layer, `context` gives the kernel size. + glu (bool): use glu instead of ReLU + resample_input (bool): upsample x2 the input and downsample /2 the output. + rescale (int): rescale initial weights of convolutions + to get their standard deviation closer to `rescale` + kernel_size (int): kernel size for convolutions + stride (int): stride for convolutions + growth (float): multiply (resp divide) number of channels by that + for each layer of the encoder (resp decoder) + lstm_layers (int): number of lstm layers, 0 = no lstm + context (int): kernel size of the convolution in the + decoder before the transposed convolution. If > 1, + will provide some context from neighboring time + steps. + samplerate (int): stored as meta information for easing + future evaluations of the model. + segment_length (int): stored as meta information for easing + future evaluations of the model. Length of the segments on which + the model was trained. + """ + + super().__init__() + self.audio_channels = audio_channels + self.sources = sources + self.kernel_size = kernel_size + self.context = context + self.stride = stride + self.depth = depth + self.resample = resample + self.channels = channels + self.normalize = normalize + self.samplerate = samplerate + self.segment_length = segment_length + + self.encoder = nn.ModuleList() + self.decoder = nn.ModuleList() + + if glu: + activation = nn.GLU(dim=1) + ch_scale = 2 + else: + activation = nn.ReLU() + ch_scale = 1 + in_channels = audio_channels + for index in range(depth): + encode = [] + encode += [nn.Conv1d(in_channels, channels, kernel_size, stride), nn.ReLU()] + if rewrite: + encode += [nn.Conv1d(channels, ch_scale * channels, 1), activation] + self.encoder.append(nn.Sequential(*encode)) + + decode = [] + if index > 0: + out_channels = in_channels + else: + out_channels = len(self.sources) * audio_channels + if rewrite: + decode += [nn.Conv1d(channels, ch_scale * channels, context), activation] + decode += [nn.ConvTranspose1d(channels, out_channels, kernel_size, stride)] + if index > 0: + decode.append(nn.ReLU()) + self.decoder.insert(0, nn.Sequential(*decode)) + in_channels = channels + channels = int(growth * channels) + + channels = in_channels + + if lstm_layers: + self.lstm = BLSTM(channels, lstm_layers) + else: + self.lstm = None + + if rescale: + rescale_module(self, reference=rescale) + + def valid_length(self, length): + """ + Return the nearest valid length to use with the model so that + there is no time steps left over in a convolutions, e.g. for all + layers, size of the input - kernel_size % stride = 0. + + If the mixture has a valid length, the estimated sources + will have exactly the same length when context = 1. If context > 1, + the two signals can be center trimmed to match. + + For training, extracts should have a valid length.For evaluation + on full tracks we recommend passing `pad = True` to :method:`forward`. + """ + if self.resample: + length *= 2 + for _ in range(self.depth): + length = math.ceil((length - self.kernel_size) / self.stride) + 1 + length = max(1, length) + length += self.context - 1 + for _ in range(self.depth): + length = (length - 1) * self.stride + self.kernel_size + + if self.resample: + length = math.ceil(length / 2) + return int(length) + + def forward(self, mix): + x = mix + + if self.normalize: + mono = mix.mean(dim=1, keepdim=True) + mean = mono.mean(dim=-1, keepdim=True) + std = mono.std(dim=-1, keepdim=True) + else: + mean = 0 + std = 1 + + x = (x - mean) / (1e-5 + std) + + if self.resample: + x = julius.resample_frac(x, 1, 2) + + saved = [] + for encode in self.encoder: + x = encode(x) + saved.append(x) + if self.lstm: + x = self.lstm(x) + for decode in self.decoder: + skip = center_trim(saved.pop(-1), x) + x = x + skip + x = decode(x) + + if self.resample: + x = julius.resample_frac(x, 2, 1) + x = x * std + mean + x = x.view(x.size(0), len(self.sources), self.audio_channels, x.size(-1)) + return x diff --git a/demucs/parser.py b/demucs/parser.py new file mode 100644 index 0000000000000000000000000000000000000000..9e49ec6a20594045c8acdf0bcc2265f37820cb33 --- /dev/null +++ b/demucs/parser.py @@ -0,0 +1,248 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +from pathlib import Path + + +def get_parser(): + parser = argparse.ArgumentParser("demucs", description="Train and evaluate Demucs.") + default_raw = None + default_musdb = None + if 'DEMUCS_RAW' in os.environ: + default_raw = Path(os.environ['DEMUCS_RAW']) + if 'DEMUCS_MUSDB' in os.environ: + default_musdb = Path(os.environ['DEMUCS_MUSDB']) + parser.add_argument( + "--raw", + type=Path, + default=default_raw, + help="Path to raw audio, can be faster, see python3 -m demucs.raw to extract.") + parser.add_argument("--no_raw", action="store_const", const=None, dest="raw") + parser.add_argument("-m", + "--musdb", + type=Path, + default=default_musdb, + help="Path to musdb root") + parser.add_argument("--is_wav", action="store_true", + help="Indicate that the MusDB dataset is in wav format (i.e. MusDB-HQ).") + parser.add_argument("--metadata", type=Path, default=Path("metadata/"), + help="Folder where metadata information is stored.") + parser.add_argument("--wav", type=Path, + help="Path to a wav dataset. This should contain a 'train' and a 'valid' " + "subfolder.") + parser.add_argument("--concat", action='store_true', + help="Concat MusDB and wav dataset when provided.") + parser.add_argument("--samplerate", type=int, default=44100) + parser.add_argument("--audio_channels", type=int, default=2) + parser.add_argument("--samples", + default=44100 * 10, + type=int, + help="number of samples to feed in") + parser.add_argument("--data_stride", + default=44100, + type=int, + help="Stride for chunks, shorter = longer epochs") + parser.add_argument("-w", "--workers", default=10, type=int, help="Loader workers") + parser.add_argument("--eval_workers", default=2, type=int, help="Final evaluation workers") + parser.add_argument("-d", + "--device", + help="Device to train on, default is cuda if available else cpu") + parser.add_argument("--eval_cpu", action="store_true", help="Eval on test will be run on cpu.") + parser.add_argument("--dummy", help="Dummy parameter, useful to create a new checkpoint file") + parser.add_argument("--test", help="Just run the test pipeline + one validation. " + "This should be a filename relative to the models/ folder.") + parser.add_argument("--test_pretrained", help="Just run the test pipeline + one validation, " + "on a pretrained model. ") + + parser.add_argument("--rank", default=0, type=int) + parser.add_argument("--world_size", default=1, type=int) + parser.add_argument("--master") + + parser.add_argument("--checkpoints", + type=Path, + default=Path("checkpoints"), + help="Folder where to store checkpoints etc") + parser.add_argument("--evals", + type=Path, + default=Path("evals"), + help="Folder where to store evals and waveforms") + parser.add_argument("--save", + action="store_true", + help="Save estimated for the test set waveforms") + parser.add_argument("--logs", + type=Path, + default=Path("logs"), + help="Folder where to store logs") + parser.add_argument("--models", + type=Path, + default=Path("models"), + help="Folder where to store trained models") + parser.add_argument("-R", + "--restart", + action='store_true', + help='Restart training, ignoring previous run') + + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("-e", "--epochs", type=int, default=180, help="Number of epochs") + parser.add_argument("-r", + "--repeat", + type=int, + default=2, + help="Repeat the train set, longer epochs") + parser.add_argument("-b", "--batch_size", type=int, default=64) + parser.add_argument("--lr", type=float, default=3e-4) + parser.add_argument("--mse", action="store_true", help="Use MSE instead of L1") + parser.add_argument("--init", help="Initialize from a pre-trained model.") + + # Augmentation options + parser.add_argument("--no_augment", + action="store_false", + dest="augment", + default=True, + help="No basic data augmentation.") + parser.add_argument("--repitch", type=float, default=0.2, + help="Probability to do tempo/pitch change") + parser.add_argument("--max_tempo", type=float, default=12, + help="Maximum relative tempo change in %% when using repitch.") + + parser.add_argument("--remix_group_size", + type=int, + default=4, + help="Shuffle sources using group of this size. Useful to somewhat " + "replicate multi-gpu training " + "on less GPUs.") + parser.add_argument("--shifts", + type=int, + default=10, + help="Number of random shifts used for the shift trick.") + parser.add_argument("--overlap", + type=float, + default=0.25, + help="Overlap when --split_valid is passed.") + + # See model.py for doc + parser.add_argument("--growth", + type=float, + default=2., + help="Number of channels between two layers will increase by this factor") + parser.add_argument("--depth", + type=int, + default=6, + help="Number of layers for the encoder and decoder") + parser.add_argument("--lstm_layers", type=int, default=2, help="Number of layers for the LSTM") + parser.add_argument("--channels", + type=int, + default=64, + help="Number of channels for the first encoder layer") + parser.add_argument("--kernel_size", + type=int, + default=8, + help="Kernel size for the (transposed) convolutions") + parser.add_argument("--conv_stride", + type=int, + default=4, + help="Stride for the (transposed) convolutions") + parser.add_argument("--context", + type=int, + default=3, + help="Context size for the decoder convolutions " + "before the transposed convolutions") + parser.add_argument("--rescale", + type=float, + default=0.1, + help="Initial weight rescale reference") + parser.add_argument("--no_resample", action="store_false", + default=True, dest="resample", + help="No Resampling of the input/output x2") + parser.add_argument("--no_glu", + action="store_false", + default=True, + dest="glu", + help="Replace all GLUs by ReLUs") + parser.add_argument("--no_rewrite", + action="store_false", + default=True, + dest="rewrite", + help="No 1x1 rewrite convolutions") + parser.add_argument("--normalize", action="store_true") + parser.add_argument("--no_norm_wav", action="store_false", dest='norm_wav', default=True) + + # Tasnet options + parser.add_argument("--tasnet", action="store_true") + parser.add_argument("--split_valid", + action="store_true", + help="Predict chunks by chunks for valid and test. Required for tasnet") + parser.add_argument("--X", type=int, default=8) + + # Other options + parser.add_argument("--show", + action="store_true", + help="Show model architecture, size and exit") + parser.add_argument("--save_model", action="store_true", + help="Skip traning, just save final model " + "for the current checkpoint value.") + parser.add_argument("--save_state", + help="Skip training, just save state " + "for the current checkpoint value. You should " + "provide a model name as argument.") + parser.add_argument("--half", action="store_true", + help="When saving the model, uses half precision.") + + # Quantization options + parser.add_argument("--q-min-size", type=float, default=1, + help="Only quantize layers over this size (in MB)") + parser.add_argument( + "--qat", type=int, help="If provided, use QAT training with that many bits.") + + parser.add_argument("--diffq", type=float, default=0) + parser.add_argument( + "--ms-target", type=float, default=162, + help="Model size target in MB, when using DiffQ. Best model will be kept " + "only if it is smaller than this target.") + + return parser + + +def get_name(parser, args): + """ + Return the name of an experiment given the args. Some parameters are ignored, + for instance --workers, as they do not impact the final result. + """ + ignore_args = set([ + "checkpoints", + "deterministic", + "eval", + "evals", + "eval_cpu", + "eval_workers", + "logs", + "master", + "rank", + "restart", + "save", + "save_model", + "save_state", + "show", + "workers", + "world_size", + ]) + parts = [] + name_args = dict(args.__dict__) + for name, value in name_args.items(): + if name in ignore_args: + continue + if value != parser.get_default(name): + if isinstance(value, Path): + parts.append(f"{name}={value.name}") + else: + parts.append(f"{name}={value}") + if parts: + name = " ".join(parts) + else: + name = "default" + return name diff --git a/demucs/pretrained.py b/demucs/pretrained.py new file mode 100644 index 0000000000000000000000000000000000000000..6aac5db100cc7a9084af96d2cd083f0c8fac473c --- /dev/null +++ b/demucs/pretrained.py @@ -0,0 +1,107 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# author: adefossez + +import logging + +from diffq import DiffQuantizer +import torch.hub + +from .model import Demucs +from .tasnet import ConvTasNet +from .utils import set_state + +logger = logging.getLogger(__name__) +ROOT = "https://dl.fbaipublicfiles.com/demucs/v3.0/" + +PRETRAINED_MODELS = { + 'demucs': 'e07c671f', + 'demucs48_hq': '28a1282c', + 'demucs_extra': '3646af93', + 'demucs_quantized': '07afea75', + 'tasnet': 'beb46fac', + 'tasnet_extra': 'df3777b2', + 'demucs_unittest': '09ebc15f', +} + +SOURCES = ["drums", "bass", "other", "vocals"] + + +def get_url(name): + sig = PRETRAINED_MODELS[name] + return ROOT + name + "-" + sig[:8] + ".th" + + +def is_pretrained(name): + return name in PRETRAINED_MODELS + + +def load_pretrained(name): + if name == "demucs": + return demucs(pretrained=True) + elif name == "demucs48_hq": + return demucs(pretrained=True, hq=True, channels=48) + elif name == "demucs_extra": + return demucs(pretrained=True, extra=True) + elif name == "demucs_quantized": + return demucs(pretrained=True, quantized=True) + elif name == "demucs_unittest": + return demucs_unittest(pretrained=True) + elif name == "tasnet": + return tasnet(pretrained=True) + elif name == "tasnet_extra": + return tasnet(pretrained=True, extra=True) + else: + raise ValueError(f"Invalid pretrained name {name}") + + +def _load_state(name, model, quantizer=None): + url = get_url(name) + state = torch.hub.load_state_dict_from_url(url, map_location='cpu', check_hash=True) + set_state(model, quantizer, state) + if quantizer: + quantizer.detach() + + +def demucs_unittest(pretrained=True): + model = Demucs(channels=4, sources=SOURCES) + if pretrained: + _load_state('demucs_unittest', model) + return model + + +def demucs(pretrained=True, extra=False, quantized=False, hq=False, channels=64): + if not pretrained and (extra or quantized or hq): + raise ValueError("if extra or quantized is True, pretrained must be True.") + model = Demucs(sources=SOURCES, channels=channels) + if pretrained: + name = 'demucs' + if channels != 64: + name += str(channels) + quantizer = None + if sum([extra, quantized, hq]) > 1: + raise ValueError("Only one of extra, quantized, hq, can be True.") + if quantized: + quantizer = DiffQuantizer(model, group_size=8, min_size=1) + name += '_quantized' + if extra: + name += '_extra' + if hq: + name += '_hq' + _load_state(name, model, quantizer) + return model + + +def tasnet(pretrained=True, extra=False): + if not pretrained and extra: + raise ValueError("if extra is True, pretrained must be True.") + model = ConvTasNet(X=10, sources=SOURCES) + if pretrained: + name = 'tasnet' + if extra: + name = 'tasnet_extra' + _load_state(name, model) + return model diff --git a/demucs/raw.py b/demucs/raw.py new file mode 100644 index 0000000000000000000000000000000000000000..d4941ad2d7ed858f490db441f5b46b12bd61ad78 --- /dev/null +++ b/demucs/raw.py @@ -0,0 +1,173 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import os +from collections import defaultdict, namedtuple +from pathlib import Path + +import musdb +import numpy as np +import torch as th +import tqdm +from torch.utils.data import DataLoader + +from .audio import AudioFile + +ChunkInfo = namedtuple("ChunkInfo", ["file_index", "offset", "local_index"]) + + +class Rawset: + """ + Dataset of raw, normalized, float32 audio files + """ + def __init__(self, path, samples=None, stride=None, channels=2, streams=None): + self.path = Path(path) + self.channels = channels + self.samples = samples + if stride is None: + stride = samples if samples is not None else 0 + self.stride = stride + entries = defaultdict(list) + for root, folders, files in os.walk(self.path, followlinks=True): + folders.sort() + files.sort() + for file in files: + if file.endswith(".raw"): + path = Path(root) / file + name, stream = path.stem.rsplit('.', 1) + entries[(path.parent.relative_to(self.path), name)].append(int(stream)) + + self._entries = list(entries.keys()) + + sizes = [] + self._lengths = [] + ref_streams = sorted(entries[self._entries[0]]) + assert ref_streams == list(range(len(ref_streams))) + if streams is None: + self.streams = ref_streams + else: + self.streams = streams + for entry in sorted(entries.keys()): + streams = entries[entry] + assert sorted(streams) == ref_streams + file = self._path(*entry) + length = file.stat().st_size // (4 * channels) + if samples is None: + sizes.append(1) + else: + if length < samples: + self._entries.remove(entry) + continue + sizes.append((length - samples) // stride + 1) + self._lengths.append(length) + if not sizes: + raise ValueError(f"Empty dataset {self.path}") + self._cumulative_sizes = np.cumsum(sizes) + self._sizes = sizes + + def __len__(self): + return self._cumulative_sizes[-1] + + @property + def total_length(self): + return sum(self._lengths) + + def chunk_info(self, index): + file_index = np.searchsorted(self._cumulative_sizes, index, side='right') + if file_index == 0: + local_index = index + else: + local_index = index - self._cumulative_sizes[file_index - 1] + return ChunkInfo(offset=local_index * self.stride, + file_index=file_index, + local_index=local_index) + + def _path(self, folder, name, stream=0): + return self.path / folder / (name + f'.{stream}.raw') + + def __getitem__(self, index): + chunk = self.chunk_info(index) + entry = self._entries[chunk.file_index] + + length = self.samples or self._lengths[chunk.file_index] + streams = [] + to_read = length * self.channels * 4 + for stream_index, stream in enumerate(self.streams): + offset = chunk.offset * 4 * self.channels + file = open(self._path(*entry, stream=stream), 'rb') + file.seek(offset) + content = file.read(to_read) + assert len(content) == to_read + content = np.frombuffer(content, dtype=np.float32) + content = content.copy() # make writable + streams.append(th.from_numpy(content).view(length, self.channels).t()) + return th.stack(streams, dim=0) + + def name(self, index): + chunk = self.chunk_info(index) + folder, name = self._entries[chunk.file_index] + return folder / name + + +class MusDBSet: + def __init__(self, mus, streams=slice(None), samplerate=44100, channels=2): + self.mus = mus + self.streams = streams + self.samplerate = samplerate + self.channels = channels + + def __len__(self): + return len(self.mus.tracks) + + def __getitem__(self, index): + track = self.mus.tracks[index] + return (track.name, AudioFile(track.path).read(channels=self.channels, + seek_time=0, + streams=self.streams, + samplerate=self.samplerate)) + + +def build_raw(mus, destination, normalize, workers, samplerate, channels): + destination.mkdir(parents=True, exist_ok=True) + loader = DataLoader(MusDBSet(mus, channels=channels, samplerate=samplerate), + batch_size=1, + num_workers=workers, + collate_fn=lambda x: x[0]) + for name, streams in tqdm.tqdm(loader): + if normalize: + ref = streams[0].mean(dim=0) # use mono mixture as reference + streams = (streams - ref.mean()) / ref.std() + for index, stream in enumerate(streams): + open(destination / (name + f'.{index}.raw'), "wb").write(stream.t().numpy().tobytes()) + + +def main(): + parser = argparse.ArgumentParser('rawset') + parser.add_argument('--workers', type=int, default=10) + parser.add_argument('--samplerate', type=int, default=44100) + parser.add_argument('--channels', type=int, default=2) + parser.add_argument('musdb', type=Path) + parser.add_argument('destination', type=Path) + + args = parser.parse_args() + + build_raw(musdb.DB(root=args.musdb, subsets=["train"], split="train"), + args.destination / "train", + normalize=True, + channels=args.channels, + samplerate=args.samplerate, + workers=args.workers) + build_raw(musdb.DB(root=args.musdb, subsets=["train"], split="valid"), + args.destination / "valid", + normalize=True, + samplerate=args.samplerate, + channels=args.channels, + workers=args.workers) + + +if __name__ == "__main__": + main() diff --git a/demucs/repitch.py b/demucs/repitch.py new file mode 100644 index 0000000000000000000000000000000000000000..8846ab2d951a024c95067f66a113968500442828 --- /dev/null +++ b/demucs/repitch.py @@ -0,0 +1,96 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import io +import random +import subprocess as sp +import tempfile + +import numpy as np +import torch +from scipy.io import wavfile + + +def i16_pcm(wav): + if wav.dtype == np.int16: + return wav + return (wav * 2**15).clamp_(-2**15, 2**15 - 1).short() + + +def f32_pcm(wav): + if wav.dtype == np.float: + return wav + return wav.float() / 2**15 + + +class RepitchedWrapper: + """ + Wrap a dataset to apply online change of pitch / tempo. + """ + def __init__(self, dataset, proba=0.2, max_pitch=2, max_tempo=12, tempo_std=5, vocals=[3]): + self.dataset = dataset + self.proba = proba + self.max_pitch = max_pitch + self.max_tempo = max_tempo + self.tempo_std = tempo_std + self.vocals = vocals + + def __len__(self): + return len(self.dataset) + + def __getitem__(self, index): + streams = self.dataset[index] + in_length = streams.shape[-1] + out_length = int((1 - 0.01 * self.max_tempo) * in_length) + + if random.random() < self.proba: + delta_pitch = random.randint(-self.max_pitch, self.max_pitch) + delta_tempo = random.gauss(0, self.tempo_std) + delta_tempo = min(max(-self.max_tempo, delta_tempo), self.max_tempo) + outs = [] + for idx, stream in enumerate(streams): + stream = repitch( + stream, + delta_pitch, + delta_tempo, + voice=idx in self.vocals) + outs.append(stream[:, :out_length]) + streams = torch.stack(outs) + else: + streams = streams[..., :out_length] + return streams + + +def repitch(wav, pitch, tempo, voice=False, quick=False, samplerate=44100): + """ + tempo is a relative delta in percentage, so tempo=10 means tempo at 110%! + pitch is in semi tones. + Requires `soundstretch` to be installed, see + https://www.surina.net/soundtouch/soundstretch.html + """ + outfile = tempfile.NamedTemporaryFile(suffix=".wav") + in_ = io.BytesIO() + wavfile.write(in_, samplerate, i16_pcm(wav).t().numpy()) + command = [ + "soundstretch", + "stdin", + outfile.name, + f"-pitch={pitch}", + f"-tempo={tempo:.6f}", + ] + if quick: + command += ["-quick"] + if voice: + command += ["-speech"] + try: + sp.run(command, capture_output=True, input=in_.getvalue(), check=True) + except sp.CalledProcessError as error: + raise RuntimeError(f"Could not change bpm because {error.stderr.decode('utf-8')}") + sr, wav = wavfile.read(outfile.name) + wav = wav.copy() + wav = f32_pcm(torch.from_numpy(wav).t()) + assert sr == samplerate + return wav diff --git a/demucs/separate.py b/demucs/separate.py new file mode 100755 index 0000000000000000000000000000000000000000..3fc7af9e711978b3e21398aa6f1deb9ae87dd370 --- /dev/null +++ b/demucs/separate.py @@ -0,0 +1,185 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import sys +from pathlib import Path +import subprocess + +import julius +import torch as th +import torchaudio as ta + +from .audio import AudioFile, convert_audio_channels +from .pretrained import is_pretrained, load_pretrained +from .utils import apply_model, load_model + + +def load_track(track, device, audio_channels, samplerate): + errors = {} + wav = None + + try: + wav = AudioFile(track).read( + streams=0, + samplerate=samplerate, + channels=audio_channels).to(device) + except FileNotFoundError: + errors['ffmpeg'] = 'Ffmpeg is not installed.' + except subprocess.CalledProcessError: + errors['ffmpeg'] = 'FFmpeg could not read the file.' + + if wav is None: + try: + wav, sr = ta.load(str(track)) + except RuntimeError as err: + errors['torchaudio'] = err.args[0] + else: + wav = convert_audio_channels(wav, audio_channels) + wav = wav.to(device) + wav = julius.resample_frac(wav, sr, samplerate) + + if wav is None: + print(f"Could not load file {track}. " + "Maybe it is not a supported file format? ") + for backend, error in errors.items(): + print(f"When trying to load using {backend}, got the following error: {error}") + sys.exit(1) + return wav + + +def encode_mp3(wav, path, bitrate=320, samplerate=44100, channels=2, verbose=False): + try: + import lameenc + except ImportError: + print("Failed to call lame encoder. Maybe it is not installed? " + "On windows, run `python.exe -m pip install -U lameenc`, " + "on OSX/Linux, run `python3 -m pip install -U lameenc`, " + "then try again.", file=sys.stderr) + sys.exit(1) + encoder = lameenc.Encoder() + encoder.set_bit_rate(bitrate) + encoder.set_in_sample_rate(samplerate) + encoder.set_channels(channels) + encoder.set_quality(2) # 2-highest, 7-fastest + if not verbose: + encoder.silence() + wav = wav.transpose(0, 1).numpy() + mp3_data = encoder.encode(wav.tobytes()) + mp3_data += encoder.flush() + with open(path, "wb") as f: + f.write(mp3_data) + + +def main(): + parser = argparse.ArgumentParser("demucs.separate", + description="Separate the sources for the given tracks") + parser.add_argument("tracks", nargs='+', type=Path, default=[], help='Path to tracks') + parser.add_argument("-n", + "--name", + default="demucs_quantized", + help="Model name. See README.md for the list of pretrained models. " + "Default is demucs_quantized.") + parser.add_argument("-v", "--verbose", action="store_true") + parser.add_argument("-o", + "--out", + type=Path, + default=Path("separated"), + help="Folder where to put extracted tracks. A subfolder " + "with the model name will be created.") + parser.add_argument("--models", + type=Path, + default=Path("models"), + help="Path to trained models. " + "Also used to store downloaded pretrained models") + parser.add_argument("-d", + "--device", + default="cuda" if th.cuda.is_available() else "cpu", + help="Device to use, default is cuda if available else cpu") + parser.add_argument("--shifts", + default=0, + type=int, + help="Number of random shifts for equivariant stabilization." + "Increase separation time but improves quality for Demucs. 10 was used " + "in the original paper.") + parser.add_argument("--overlap", + default=0.25, + type=float, + help="Overlap between the splits.") + parser.add_argument("--no-split", + action="store_false", + dest="split", + default=True, + help="Doesn't split audio in chunks. This can use large amounts of memory.") + parser.add_argument("--float32", + action="store_true", + help="Convert the output wavefile to use pcm f32 format instead of s16. " + "This should not make a difference if you just plan on listening to the " + "audio but might be needed to compute exactly metrics like SDR etc.") + parser.add_argument("--int16", + action="store_false", + dest="float32", + help="Opposite of --float32, here for compatibility.") + parser.add_argument("--mp3", action="store_true", + help="Convert the output wavs to mp3.") + parser.add_argument("--mp3-bitrate", + default=320, + type=int, + help="Bitrate of converted mp3.") + + args = parser.parse_args() + name = args.name + ".th" + model_path = args.models / name + if model_path.is_file(): + model = load_model(model_path) + else: + if is_pretrained(args.name): + model = load_pretrained(args.name) + else: + print(f"No pre-trained model {args.name}", file=sys.stderr) + sys.exit(1) + model.to(args.device) + + out = args.out / args.name + out.mkdir(parents=True, exist_ok=True) + print(f"Separated tracks will be stored in {out.resolve()}") + for track in args.tracks: + if not track.exists(): + print( + f"File {track} does not exist. If the path contains spaces, " + "please try again after surrounding the entire path with quotes \"\".", + file=sys.stderr) + continue + print(f"Separating track {track}") + wav = load_track(track, args.device, model.audio_channels, model.samplerate) + + ref = wav.mean(0) + wav = (wav - ref.mean()) / ref.std() + sources = apply_model(model, wav, shifts=args.shifts, split=args.split, + overlap=args.overlap, progress=True) + sources = sources * ref.std() + ref.mean() + + track_folder = out / track.name.rsplit(".", 1)[0] + track_folder.mkdir(exist_ok=True) + for source, name in zip(sources, model.sources): + source = source / max(1.01 * source.abs().max(), 1) + if args.mp3 or not args.float32: + source = (source * 2**15).clamp_(-2**15, 2**15 - 1).short() + source = source.cpu() + stem = str(track_folder / name) + if args.mp3: + encode_mp3(source, stem + ".mp3", + bitrate=args.mp3_bitrate, + samplerate=model.samplerate, + channels=model.audio_channels, + verbose=args.verbose) + else: + wavname = str(track_folder / f"{name}.wav") + ta.save(wavname, source, sample_rate=model.samplerate) + + +if __name__ == "__main__": + main() diff --git a/demucs/tasnet.py b/demucs/tasnet.py new file mode 100644 index 0000000000000000000000000000000000000000..ecc1257925ea8f4fbe389ddd6d73ce9fdf45f6d4 --- /dev/null +++ b/demucs/tasnet.py @@ -0,0 +1,452 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# +# Created on 2018/12 +# Author: Kaituo XU +# Modified on 2019/11 by Alexandre Defossez, added support for multiple output channels +# Here is the original license: +# The MIT License (MIT) +# +# Copyright (c) 2018 Kaituo XU +# +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: +# +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .utils import capture_init + +EPS = 1e-8 + + +def overlap_and_add(signal, frame_step): + outer_dimensions = signal.size()[:-2] + frames, frame_length = signal.size()[-2:] + + subframe_length = math.gcd(frame_length, frame_step) # gcd=Greatest Common Divisor + subframe_step = frame_step // subframe_length + subframes_per_frame = frame_length // subframe_length + output_size = frame_step * (frames - 1) + frame_length + output_subframes = output_size // subframe_length + + subframe_signal = signal.view(*outer_dimensions, -1, subframe_length) + + frame = torch.arange(0, output_subframes, + device=signal.device).unfold(0, subframes_per_frame, subframe_step) + frame = frame.long() # signal may in GPU or CPU + frame = frame.contiguous().view(-1) + + result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length) + result.index_add_(-2, frame, subframe_signal) + result = result.view(*outer_dimensions, -1) + return result + + +class ConvTasNet(nn.Module): + @capture_init + def __init__(self, + sources, + N=256, + L=20, + B=256, + H=512, + P=3, + X=8, + R=4, + audio_channels=2, + norm_type="gLN", + causal=False, + mask_nonlinear='relu', + samplerate=44100, + segment_length=44100 * 2 * 4): + """ + Args: + sources: list of sources + N: Number of filters in autoencoder + L: Length of the filters (in samples) + B: Number of channels in bottleneck 1 × 1-conv block + H: Number of channels in convolutional blocks + P: Kernel size in convolutional blocks + X: Number of convolutional blocks in each repeat + R: Number of repeats + norm_type: BN, gLN, cLN + causal: causal or non-causal + mask_nonlinear: use which non-linear function to generate mask + """ + super(ConvTasNet, self).__init__() + # Hyper-parameter + self.sources = sources + self.C = len(sources) + self.N, self.L, self.B, self.H, self.P, self.X, self.R = N, L, B, H, P, X, R + self.norm_type = norm_type + self.causal = causal + self.mask_nonlinear = mask_nonlinear + self.audio_channels = audio_channels + self.samplerate = samplerate + self.segment_length = segment_length + # Components + self.encoder = Encoder(L, N, audio_channels) + self.separator = TemporalConvNet( + N, B, H, P, X, R, self.C, norm_type, causal, mask_nonlinear) + self.decoder = Decoder(N, L, audio_channels) + # init + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_normal_(p) + + def valid_length(self, length): + return length + + def forward(self, mixture): + """ + Args: + mixture: [M, T], M is batch size, T is #samples + Returns: + est_source: [M, C, T] + """ + mixture_w = self.encoder(mixture) + est_mask = self.separator(mixture_w) + est_source = self.decoder(mixture_w, est_mask) + + # T changed after conv1d in encoder, fix it here + T_origin = mixture.size(-1) + T_conv = est_source.size(-1) + est_source = F.pad(est_source, (0, T_origin - T_conv)) + return est_source + + +class Encoder(nn.Module): + """Estimation of the nonnegative mixture weight by a 1-D conv layer. + """ + def __init__(self, L, N, audio_channels): + super(Encoder, self).__init__() + # Hyper-parameter + self.L, self.N = L, N + # Components + # 50% overlap + self.conv1d_U = nn.Conv1d(audio_channels, N, kernel_size=L, stride=L // 2, bias=False) + + def forward(self, mixture): + """ + Args: + mixture: [M, T], M is batch size, T is #samples + Returns: + mixture_w: [M, N, K], where K = (T-L)/(L/2)+1 = 2T/L-1 + """ + mixture_w = F.relu(self.conv1d_U(mixture)) # [M, N, K] + return mixture_w + + +class Decoder(nn.Module): + def __init__(self, N, L, audio_channels): + super(Decoder, self).__init__() + # Hyper-parameter + self.N, self.L = N, L + self.audio_channels = audio_channels + # Components + self.basis_signals = nn.Linear(N, audio_channels * L, bias=False) + + def forward(self, mixture_w, est_mask): + """ + Args: + mixture_w: [M, N, K] + est_mask: [M, C, N, K] + Returns: + est_source: [M, C, T] + """ + # D = W * M + source_w = torch.unsqueeze(mixture_w, 1) * est_mask # [M, C, N, K] + source_w = torch.transpose(source_w, 2, 3) # [M, C, K, N] + # S = DV + est_source = self.basis_signals(source_w) # [M, C, K, ac * L] + m, c, k, _ = est_source.size() + est_source = est_source.view(m, c, k, self.audio_channels, -1).transpose(2, 3).contiguous() + est_source = overlap_and_add(est_source, self.L // 2) # M x C x ac x T + return est_source + + +class TemporalConvNet(nn.Module): + def __init__(self, N, B, H, P, X, R, C, norm_type="gLN", causal=False, mask_nonlinear='relu'): + """ + Args: + N: Number of filters in autoencoder + B: Number of channels in bottleneck 1 × 1-conv block + H: Number of channels in convolutional blocks + P: Kernel size in convolutional blocks + X: Number of convolutional blocks in each repeat + R: Number of repeats + C: Number of speakers + norm_type: BN, gLN, cLN + causal: causal or non-causal + mask_nonlinear: use which non-linear function to generate mask + """ + super(TemporalConvNet, self).__init__() + # Hyper-parameter + self.C = C + self.mask_nonlinear = mask_nonlinear + # Components + # [M, N, K] -> [M, N, K] + layer_norm = ChannelwiseLayerNorm(N) + # [M, N, K] -> [M, B, K] + bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False) + # [M, B, K] -> [M, B, K] + repeats = [] + for r in range(R): + blocks = [] + for x in range(X): + dilation = 2**x + padding = (P - 1) * dilation if causal else (P - 1) * dilation // 2 + blocks += [ + TemporalBlock(B, + H, + P, + stride=1, + padding=padding, + dilation=dilation, + norm_type=norm_type, + causal=causal) + ] + repeats += [nn.Sequential(*blocks)] + temporal_conv_net = nn.Sequential(*repeats) + # [M, B, K] -> [M, C*N, K] + mask_conv1x1 = nn.Conv1d(B, C * N, 1, bias=False) + # Put together + self.network = nn.Sequential(layer_norm, bottleneck_conv1x1, temporal_conv_net, + mask_conv1x1) + + def forward(self, mixture_w): + """ + Keep this API same with TasNet + Args: + mixture_w: [M, N, K], M is batch size + returns: + est_mask: [M, C, N, K] + """ + M, N, K = mixture_w.size() + score = self.network(mixture_w) # [M, N, K] -> [M, C*N, K] + score = score.view(M, self.C, N, K) # [M, C*N, K] -> [M, C, N, K] + if self.mask_nonlinear == 'softmax': + est_mask = F.softmax(score, dim=1) + elif self.mask_nonlinear == 'relu': + est_mask = F.relu(score) + else: + raise ValueError("Unsupported mask non-linear function") + return est_mask + + +class TemporalBlock(nn.Module): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + norm_type="gLN", + causal=False): + super(TemporalBlock, self).__init__() + # [M, B, K] -> [M, H, K] + conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False) + prelu = nn.PReLU() + norm = chose_norm(norm_type, out_channels) + # [M, H, K] -> [M, B, K] + dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size, stride, padding, + dilation, norm_type, causal) + # Put together + self.net = nn.Sequential(conv1x1, prelu, norm, dsconv) + + def forward(self, x): + """ + Args: + x: [M, B, K] + Returns: + [M, B, K] + """ + residual = x + out = self.net(x) + # TODO: when P = 3 here works fine, but when P = 2 maybe need to pad? + return out + residual # look like w/o F.relu is better than w/ F.relu + # return F.relu(out + residual) + + +class DepthwiseSeparableConv(nn.Module): + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride, + padding, + dilation, + norm_type="gLN", + causal=False): + super(DepthwiseSeparableConv, self).__init__() + # Use `groups` option to implement depthwise convolution + # [M, H, K] -> [M, H, K] + depthwise_conv = nn.Conv1d(in_channels, + in_channels, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=in_channels, + bias=False) + if causal: + chomp = Chomp1d(padding) + prelu = nn.PReLU() + norm = chose_norm(norm_type, in_channels) + # [M, H, K] -> [M, B, K] + pointwise_conv = nn.Conv1d(in_channels, out_channels, 1, bias=False) + # Put together + if causal: + self.net = nn.Sequential(depthwise_conv, chomp, prelu, norm, pointwise_conv) + else: + self.net = nn.Sequential(depthwise_conv, prelu, norm, pointwise_conv) + + def forward(self, x): + """ + Args: + x: [M, H, K] + Returns: + result: [M, B, K] + """ + return self.net(x) + + +class Chomp1d(nn.Module): + """To ensure the output length is the same as the input. + """ + def __init__(self, chomp_size): + super(Chomp1d, self).__init__() + self.chomp_size = chomp_size + + def forward(self, x): + """ + Args: + x: [M, H, Kpad] + Returns: + [M, H, K] + """ + return x[:, :, :-self.chomp_size].contiguous() + + +def chose_norm(norm_type, channel_size): + """The input of normlization will be (M, C, K), where M is batch size, + C is channel size and K is sequence length. + """ + if norm_type == "gLN": + return GlobalLayerNorm(channel_size) + elif norm_type == "cLN": + return ChannelwiseLayerNorm(channel_size) + elif norm_type == "id": + return nn.Identity() + else: # norm_type == "BN": + # Given input (M, C, K), nn.BatchNorm1d(C) will accumulate statics + # along M and K, so this BN usage is right. + return nn.BatchNorm1d(channel_size) + + +# TODO: Use nn.LayerNorm to impl cLN to speed up +class ChannelwiseLayerNorm(nn.Module): + """Channel-wise Layer Normalization (cLN)""" + def __init__(self, channel_size): + super(ChannelwiseLayerNorm, self).__init__() + self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1] + self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1] + self.reset_parameters() + + def reset_parameters(self): + self.gamma.data.fill_(1) + self.beta.data.zero_() + + def forward(self, y): + """ + Args: + y: [M, N, K], M is batch size, N is channel size, K is length + Returns: + cLN_y: [M, N, K] + """ + mean = torch.mean(y, dim=1, keepdim=True) # [M, 1, K] + var = torch.var(y, dim=1, keepdim=True, unbiased=False) # [M, 1, K] + cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta + return cLN_y + + +class GlobalLayerNorm(nn.Module): + """Global Layer Normalization (gLN)""" + def __init__(self, channel_size): + super(GlobalLayerNorm, self).__init__() + self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1] + self.beta = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1] + self.reset_parameters() + + def reset_parameters(self): + self.gamma.data.fill_(1) + self.beta.data.zero_() + + def forward(self, y): + """ + Args: + y: [M, N, K], M is batch size, N is channel size, K is length + Returns: + gLN_y: [M, N, K] + """ + # TODO: in torch 1.0, torch.mean() support dim list + mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True) # [M, 1, 1] + var = (torch.pow(y - mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True) + gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta + return gLN_y + + +if __name__ == "__main__": + torch.manual_seed(123) + M, N, L, T = 2, 3, 4, 12 + K = 2 * T // L - 1 + B, H, P, X, R, C, norm_type, causal = 2, 3, 3, 3, 2, 2, "gLN", False + mixture = torch.randint(3, (M, T)) + # test Encoder + encoder = Encoder(L, N) + encoder.conv1d_U.weight.data = torch.randint(2, encoder.conv1d_U.weight.size()) + mixture_w = encoder(mixture) + print('mixture', mixture) + print('U', encoder.conv1d_U.weight) + print('mixture_w', mixture_w) + print('mixture_w size', mixture_w.size()) + + # test TemporalConvNet + separator = TemporalConvNet(N, B, H, P, X, R, C, norm_type=norm_type, causal=causal) + est_mask = separator(mixture_w) + print('est_mask', est_mask) + + # test Decoder + decoder = Decoder(N, L) + est_mask = torch.randint(2, (B, K, C, N)) + est_source = decoder(mixture_w, est_mask) + print('est_source', est_source) + + # test Conv-TasNet + conv_tasnet = ConvTasNet(N, L, B, H, P, X, R, C, norm_type=norm_type) + est_source = conv_tasnet(mixture) + print('est_source', est_source) + print('est_source size', est_source.size()) diff --git a/demucs/test.py b/demucs/test.py new file mode 100755 index 0000000000000000000000000000000000000000..4140914ddbff3543b4056ca0cb1b5e887434a40a --- /dev/null +++ b/demucs/test.py @@ -0,0 +1,109 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import gzip +import sys +from concurrent import futures + +import musdb +import museval +import torch as th +import tqdm +from scipy.io import wavfile +from torch import distributed + +from .audio import convert_audio +from .utils import apply_model + + +def evaluate(model, + musdb_path, + eval_folder, + workers=2, + device="cpu", + rank=0, + save=False, + shifts=0, + split=False, + overlap=0.25, + is_wav=False, + world_size=1): + """ + Evaluate model using museval. Run the model + on a single GPU, the bottleneck being the call to museval. + """ + + output_dir = eval_folder / "results" + output_dir.mkdir(exist_ok=True, parents=True) + json_folder = eval_folder / "results/test" + json_folder.mkdir(exist_ok=True, parents=True) + + # we load tracks from the original musdb set + test_set = musdb.DB(musdb_path, subsets=["test"], is_wav=is_wav) + src_rate = 44100 # hardcoded for now... + + for p in model.parameters(): + p.requires_grad = False + p.grad = None + + pendings = [] + with futures.ProcessPoolExecutor(workers or 1) as pool: + for index in tqdm.tqdm(range(rank, len(test_set), world_size), file=sys.stdout): + track = test_set.tracks[index] + + out = json_folder / f"{track.name}.json.gz" + if out.exists(): + continue + + mix = th.from_numpy(track.audio).t().float() + ref = mix.mean(dim=0) # mono mixture + mix = (mix - ref.mean()) / ref.std() + mix = convert_audio(mix, src_rate, model.samplerate, model.audio_channels) + estimates = apply_model(model, mix.to(device), + shifts=shifts, split=split, overlap=overlap) + estimates = estimates * ref.std() + ref.mean() + + estimates = estimates.transpose(1, 2) + references = th.stack( + [th.from_numpy(track.targets[name].audio).t() for name in model.sources]) + references = convert_audio(references, src_rate, + model.samplerate, model.audio_channels) + references = references.transpose(1, 2).numpy() + estimates = estimates.cpu().numpy() + win = int(1. * model.samplerate) + hop = int(1. * model.samplerate) + if save: + folder = eval_folder / "wav/test" / track.name + folder.mkdir(exist_ok=True, parents=True) + for name, estimate in zip(model.sources, estimates): + wavfile.write(str(folder / (name + ".wav")), 44100, estimate) + + if workers: + pendings.append((track.name, pool.submit( + museval.evaluate, references, estimates, win=win, hop=hop))) + else: + pendings.append((track.name, museval.evaluate( + references, estimates, win=win, hop=hop))) + del references, mix, estimates, track + + for track_name, pending in tqdm.tqdm(pendings, file=sys.stdout): + if workers: + pending = pending.result() + sdr, isr, sir, sar = pending + track_store = museval.TrackStore(win=44100, hop=44100, track_name=track_name) + for idx, target in enumerate(model.sources): + values = { + "SDR": sdr[idx].tolist(), + "SIR": sir[idx].tolist(), + "ISR": isr[idx].tolist(), + "SAR": sar[idx].tolist() + } + + track_store.add_target(target_name=target, values=values) + json_path = json_folder / f"{track_name}.json.gz" + gzip.open(json_path, "w").write(track_store.json.encode('utf-8')) + if world_size > 1: + distributed.barrier() diff --git a/demucs/train.py b/demucs/train.py new file mode 100644 index 0000000000000000000000000000000000000000..6bd221279dc986a6df1a8d7b4d4444bb822a1cb3 --- /dev/null +++ b/demucs/train.py @@ -0,0 +1,127 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import sys + +import tqdm +from torch.utils.data import DataLoader +from torch.utils.data.distributed import DistributedSampler + +from .utils import apply_model, average_metric, center_trim + + +def train_model(epoch, + dataset, + model, + criterion, + optimizer, + augment, + quantizer=None, + diffq=0, + repeat=1, + device="cpu", + seed=None, + workers=4, + world_size=1, + batch_size=16): + + if world_size > 1: + sampler = DistributedSampler(dataset) + sampler_epoch = epoch * repeat + if seed is not None: + sampler_epoch += seed * 1000 + sampler.set_epoch(sampler_epoch) + batch_size //= world_size + loader = DataLoader(dataset, batch_size=batch_size, sampler=sampler, num_workers=workers) + else: + loader = DataLoader(dataset, batch_size=batch_size, num_workers=workers, shuffle=True) + current_loss = 0 + model_size = 0 + for repetition in range(repeat): + tq = tqdm.tqdm(loader, + ncols=120, + desc=f"[{epoch:03d}] train ({repetition + 1}/{repeat})", + leave=False, + file=sys.stdout, + unit=" batch") + total_loss = 0 + for idx, sources in enumerate(tq): + if len(sources) < batch_size: + # skip uncomplete batch for augment.Remix to work properly + continue + sources = sources.to(device) + sources = augment(sources) + mix = sources.sum(dim=1) + + estimates = model(mix) + sources = center_trim(sources, estimates) + loss = criterion(estimates, sources) + model_size = 0 + if quantizer is not None: + model_size = quantizer.model_size() + + train_loss = loss + diffq * model_size + train_loss.backward() + grad_norm = 0 + for p in model.parameters(): + if p.grad is not None: + grad_norm += p.grad.data.norm()**2 + grad_norm = grad_norm**0.5 + optimizer.step() + optimizer.zero_grad() + + if quantizer is not None: + model_size = model_size.item() + + total_loss += loss.item() + current_loss = total_loss / (1 + idx) + tq.set_postfix(loss=f"{current_loss:.4f}", ms=f"{model_size:.2f}", + grad=f"{grad_norm:.5f}") + + # free some space before next round + del sources, mix, estimates, loss, train_loss + + if world_size > 1: + sampler.epoch += 1 + + if world_size > 1: + current_loss = average_metric(current_loss) + return current_loss, model_size + + +def validate_model(epoch, + dataset, + model, + criterion, + device="cpu", + rank=0, + world_size=1, + shifts=0, + overlap=0.25, + split=False): + indexes = range(rank, len(dataset), world_size) + tq = tqdm.tqdm(indexes, + ncols=120, + desc=f"[{epoch:03d}] valid", + leave=False, + file=sys.stdout, + unit=" track") + current_loss = 0 + for index in tq: + streams = dataset[index] + # first five minutes to avoid OOM on --upsample models + streams = streams[..., :15_000_000] + streams = streams.to(device) + sources = streams[1:] + mix = streams[0] + estimates = apply_model(model, mix, shifts=shifts, split=split, overlap=overlap) + loss = criterion(estimates, sources) + current_loss += loss.item() / len(indexes) + del estimates, streams, sources + + if world_size > 1: + current_loss = average_metric(current_loss, len(indexes)) + return current_loss diff --git a/demucs/utils.py b/demucs/utils.py new file mode 100755 index 0000000000000000000000000000000000000000..f63f8e31deba4238eb146fee78539fce03b7e2a3 --- /dev/null +++ b/demucs/utils.py @@ -0,0 +1,324 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import errno +import functools +import hashlib +import inspect +import io +import os +import random +import socket +import tempfile +import warnings +import zlib +from contextlib import contextmanager + +from diffq import UniformQuantizer, DiffQuantizer +import torch as th +import tqdm +from torch import distributed +from torch.nn import functional as F + + +def center_trim(tensor, reference): + """ + Center trim `tensor` with respect to `reference`, along the last dimension. + `reference` can also be a number, representing the length to trim to. + If the size difference != 0 mod 2, the extra sample is removed on the right side. + """ + if hasattr(reference, "size"): + reference = reference.size(-1) + delta = tensor.size(-1) - reference + if delta < 0: + raise ValueError("tensor must be larger than reference. " f"Delta is {delta}.") + if delta: + tensor = tensor[..., delta // 2:-(delta - delta // 2)] + return tensor + + +def average_metric(metric, count=1.): + """ + Average `metric` which should be a float across all hosts. `count` should be + the weight for this particular host (i.e. number of examples). + """ + metric = th.tensor([count, count * metric], dtype=th.float32, device='cuda') + distributed.all_reduce(metric, op=distributed.ReduceOp.SUM) + return metric[1].item() / metric[0].item() + + +def free_port(host='', low=20000, high=40000): + """ + Return a port number that is most likely free. + This could suffer from a race condition although + it should be quite rare. + """ + sock = socket.socket() + while True: + port = random.randint(low, high) + try: + sock.bind((host, port)) + except OSError as error: + if error.errno == errno.EADDRINUSE: + continue + raise + return port + + +def sizeof_fmt(num, suffix='B'): + """ + Given `num` bytes, return human readable size. + Taken from https://stackoverflow.com/a/1094933 + """ + for unit in ['', 'Ki', 'Mi', 'Gi', 'Ti', 'Pi', 'Ei', 'Zi']: + if abs(num) < 1024.0: + return "%3.1f%s%s" % (num, unit, suffix) + num /= 1024.0 + return "%.1f%s%s" % (num, 'Yi', suffix) + + +def human_seconds(seconds, display='.2f'): + """ + Given `seconds` seconds, return human readable duration. + """ + value = seconds * 1e6 + ratios = [1e3, 1e3, 60, 60, 24] + names = ['us', 'ms', 's', 'min', 'hrs', 'days'] + last = names.pop(0) + for name, ratio in zip(names, ratios): + if value / ratio < 0.3: + break + value /= ratio + last = name + return f"{format(value, display)} {last}" + + +class TensorChunk: + def __init__(self, tensor, offset=0, length=None): + total_length = tensor.shape[-1] + assert offset >= 0 + assert offset < total_length + + if length is None: + length = total_length - offset + else: + length = min(total_length - offset, length) + + self.tensor = tensor + self.offset = offset + self.length = length + self.device = tensor.device + + @property + def shape(self): + shape = list(self.tensor.shape) + shape[-1] = self.length + return shape + + def padded(self, target_length): + delta = target_length - self.length + total_length = self.tensor.shape[-1] + assert delta >= 0 + + start = self.offset - delta // 2 + end = start + target_length + + correct_start = max(0, start) + correct_end = min(total_length, end) + + pad_left = correct_start - start + pad_right = end - correct_end + + out = F.pad(self.tensor[..., correct_start:correct_end], (pad_left, pad_right)) + assert out.shape[-1] == target_length + return out + + +def tensor_chunk(tensor_or_chunk): + if isinstance(tensor_or_chunk, TensorChunk): + return tensor_or_chunk + else: + assert isinstance(tensor_or_chunk, th.Tensor) + return TensorChunk(tensor_or_chunk) + + +def apply_model(model, mix, shifts=None, split=False, + overlap=0.25, transition_power=1., progress=False): + """ + Apply model to a given mixture. + + Args: + shifts (int): if > 0, will shift in time `mix` by a random amount between 0 and 0.5 sec + and apply the oppositve shift to the output. This is repeated `shifts` time and + all predictions are averaged. This effectively makes the model time equivariant + and improves SDR by up to 0.2 points. + split (bool): if True, the input will be broken down in 8 seconds extracts + and predictions will be performed individually on each and concatenated. + Useful for model with large memory footprint like Tasnet. + progress (bool): if True, show a progress bar (requires split=True) + """ + assert transition_power >= 1, "transition_power < 1 leads to weird behavior." + device = mix.device + channels, length = mix.shape + if split: + out = th.zeros(len(model.sources), channels, length, device=device) + sum_weight = th.zeros(length, device=device) + segment = model.segment_length + stride = int((1 - overlap) * segment) + offsets = range(0, length, stride) + scale = stride / model.samplerate + if progress: + offsets = tqdm.tqdm(offsets, unit_scale=scale, ncols=120, unit='seconds') + # We start from a triangle shaped weight, with maximal weight in the middle + # of the segment. Then we normalize and take to the power `transition_power`. + # Large values of transition power will lead to sharper transitions. + weight = th.cat([th.arange(1, segment // 2 + 1), + th.arange(segment - segment // 2, 0, -1)]).to(device) + assert len(weight) == segment + # If the overlap < 50%, this will translate to linear transition when + # transition_power is 1. + weight = (weight / weight.max())**transition_power + for offset in offsets: + chunk = TensorChunk(mix, offset, segment) + chunk_out = apply_model(model, chunk, shifts=shifts) + chunk_length = chunk_out.shape[-1] + out[..., offset:offset + segment] += weight[:chunk_length] * chunk_out + sum_weight[offset:offset + segment] += weight[:chunk_length] + offset += segment + assert sum_weight.min() > 0 + out /= sum_weight + return out + elif shifts: + max_shift = int(0.5 * model.samplerate) + mix = tensor_chunk(mix) + padded_mix = mix.padded(length + 2 * max_shift) + out = 0 + for _ in range(shifts): + offset = random.randint(0, max_shift) + shifted = TensorChunk(padded_mix, offset, length + max_shift - offset) + shifted_out = apply_model(model, shifted) + out += shifted_out[..., max_shift - offset:] + out /= shifts + return out + else: + valid_length = model.valid_length(length) + mix = tensor_chunk(mix) + padded_mix = mix.padded(valid_length) + with th.no_grad(): + out = model(padded_mix.unsqueeze(0))[0] + return center_trim(out, length) + + +@contextmanager +def temp_filenames(count, delete=True): + names = [] + try: + for _ in range(count): + names.append(tempfile.NamedTemporaryFile(delete=False).name) + yield names + finally: + if delete: + for name in names: + os.unlink(name) + + +def get_quantizer(model, args, optimizer=None): + quantizer = None + if args.diffq: + quantizer = DiffQuantizer( + model, min_size=args.q_min_size, group_size=8) + if optimizer is not None: + quantizer.setup_optimizer(optimizer) + elif args.qat: + quantizer = UniformQuantizer( + model, bits=args.qat, min_size=args.q_min_size) + return quantizer + + +def load_model(path, strict=False): + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + load_from = path + package = th.load(load_from, 'cpu') + + klass = package["klass"] + args = package["args"] + kwargs = package["kwargs"] + + if strict: + model = klass(*args, **kwargs) + else: + sig = inspect.signature(klass) + for key in list(kwargs): + if key not in sig.parameters: + warnings.warn("Dropping inexistant parameter " + key) + del kwargs[key] + model = klass(*args, **kwargs) + + state = package["state"] + training_args = package["training_args"] + quantizer = get_quantizer(model, training_args) + + set_state(model, quantizer, state) + return model + + +def get_state(model, quantizer, half=False): + if quantizer is None: + dtype = th.half if half else None + state = {k: p.data.to(device='cpu', dtype=dtype) for k, p in model.state_dict().items()} + else: + state = quantizer.get_quantized_state() + buf = io.BytesIO() + th.save(state, buf) + state = {'compressed': zlib.compress(buf.getvalue())} + return state + + +def set_state(model, quantizer, state): + if quantizer is None: + model.load_state_dict(state) + else: + buf = io.BytesIO(zlib.decompress(state["compressed"])) + state = th.load(buf, "cpu") + quantizer.restore_quantized_state(state) + + return state + + +def save_state(state, path): + buf = io.BytesIO() + th.save(state, buf) + sig = hashlib.sha256(buf.getvalue()).hexdigest()[:8] + + path = path.parent / (path.stem + "-" + sig + path.suffix) + path.write_bytes(buf.getvalue()) + + +def save_model(model, quantizer, training_args, path): + args, kwargs = model._init_args_kwargs + klass = model.__class__ + + state = get_state(model, quantizer, half=training_args.half) + + save_to = path + package = { + 'klass': klass, + 'args': args, + 'kwargs': kwargs, + 'state': state, + 'training_args': training_args, + } + th.save(package, save_to) + + +def capture_init(init): + @functools.wraps(init) + def __init__(self, *args, **kwargs): + self._init_args_kwargs = (args, kwargs) + init(self, *args, **kwargs) + + return __init__ diff --git a/demucs/wav.py b/demucs/wav.py new file mode 100644 index 0000000000000000000000000000000000000000..42533151ce8d17c3f65e22525bc8136be5577a47 --- /dev/null +++ b/demucs/wav.py @@ -0,0 +1,179 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +from collections import OrderedDict +import hashlib +import math +import json +from pathlib import Path +import os + +import julius +import torch as th +from torch import distributed +import torchaudio as ta +from torch.nn import functional as F + +from .audio import convert_audio_channels +from .compressed import get_musdb_tracks + +MIXTURE = "mixture" +EXT = ".wav" + + +def _track_metadata(track, sources): + track_length = None + track_samplerate = None + for source in sources + [MIXTURE]: + file = track / f"{source}{EXT}" + info = ta.info(str(file)) + length = info.num_frames + if track_length is None: + track_length = length + track_samplerate = info.sample_rate + elif track_length != length: + raise ValueError( + f"Invalid length for file {file}: " + f"expecting {track_length} but got {length}.") + elif info.sample_rate != track_samplerate: + raise ValueError( + f"Invalid sample rate for file {file}: " + f"expecting {track_samplerate} but got {info.sample_rate}.") + if source == MIXTURE: + wav, _ = ta.load(str(file)) + wav = wav.mean(0) + mean = wav.mean().item() + std = wav.std().item() + + return {"length": length, "mean": mean, "std": std, "samplerate": track_samplerate} + + +def _build_metadata(path, sources): + meta = {} + path = Path(path) + for root, folders, files in os.walk(path, followlinks=True): + root = Path(root) + if root.name.startswith('.') or folders or root == path: + continue + name = str(root.relative_to(path)) + meta[name] = _track_metadata(root, sources) + return meta + + +class Wavset: + def __init__( + self, + root, metadata, sources, + length=None, stride=None, normalize=True, + samplerate=44100, channels=2): + """ + Waveset (or mp3 set for that matter). Can be used to train + with arbitrary sources. Each track should be one folder inside of `path`. + The folder should contain files named `{source}.{ext}`. + Files will be grouped according to `sources` (each source is a list of + filenames). + + Sample rate and channels will be converted on the fly. + + `length` is the sample size to extract (in samples, not duration). + `stride` is how many samples to move by between each example. + """ + self.root = Path(root) + self.metadata = OrderedDict(metadata) + self.length = length + self.stride = stride or length + self.normalize = normalize + self.sources = sources + self.channels = channels + self.samplerate = samplerate + self.num_examples = [] + for name, meta in self.metadata.items(): + track_length = int(self.samplerate * meta['length'] / meta['samplerate']) + if length is None or track_length < length: + examples = 1 + else: + examples = int(math.ceil((track_length - self.length) / self.stride) + 1) + self.num_examples.append(examples) + + def __len__(self): + return sum(self.num_examples) + + def get_file(self, name, source): + return self.root / name / f"{source}{EXT}" + + def __getitem__(self, index): + for name, examples in zip(self.metadata, self.num_examples): + if index >= examples: + index -= examples + continue + meta = self.metadata[name] + num_frames = -1 + offset = 0 + if self.length is not None: + offset = int(math.ceil( + meta['samplerate'] * self.stride * index / self.samplerate)) + num_frames = int(math.ceil( + meta['samplerate'] * self.length / self.samplerate)) + wavs = [] + for source in self.sources: + file = self.get_file(name, source) + wav, _ = ta.load(str(file), frame_offset=offset, num_frames=num_frames) + wav = convert_audio_channels(wav, self.channels) + wavs.append(wav) + + example = th.stack(wavs) + example = julius.resample_frac(example, meta['samplerate'], self.samplerate) + if self.normalize: + example = (example - meta['mean']) / meta['std'] + if self.length: + example = example[..., :self.length] + example = F.pad(example, (0, self.length - example.shape[-1])) + return example + + +def get_wav_datasets(args, samples, sources): + sig = hashlib.sha1(str(args.wav).encode()).hexdigest()[:8] + metadata_file = args.metadata / (sig + ".json") + train_path = args.wav / "train" + valid_path = args.wav / "valid" + if not metadata_file.is_file() and args.rank == 0: + train = _build_metadata(train_path, sources) + valid = _build_metadata(valid_path, sources) + json.dump([train, valid], open(metadata_file, "w")) + if args.world_size > 1: + distributed.barrier() + train, valid = json.load(open(metadata_file)) + train_set = Wavset(train_path, train, sources, + length=samples, stride=args.data_stride, + samplerate=args.samplerate, channels=args.audio_channels, + normalize=args.norm_wav) + valid_set = Wavset(valid_path, valid, [MIXTURE] + sources, + samplerate=args.samplerate, channels=args.audio_channels, + normalize=args.norm_wav) + return train_set, valid_set + + +def get_musdb_wav_datasets(args, samples, sources): + metadata_file = args.metadata / "musdb_wav.json" + root = args.musdb / "train" + if not metadata_file.is_file() and args.rank == 0: + metadata = _build_metadata(root, sources) + json.dump(metadata, open(metadata_file, "w")) + if args.world_size > 1: + distributed.barrier() + metadata = json.load(open(metadata_file)) + + train_tracks = get_musdb_tracks(args.musdb, is_wav=True, subsets=["train"], split="train") + metadata_train = {name: meta for name, meta in metadata.items() if name in train_tracks} + metadata_valid = {name: meta for name, meta in metadata.items() if name not in train_tracks} + train_set = Wavset(root, metadata_train, sources, + length=samples, stride=args.data_stride, + samplerate=args.samplerate, channels=args.audio_channels, + normalize=args.norm_wav) + valid_set = Wavset(root, metadata_valid, [MIXTURE] + sources, + samplerate=args.samplerate, channels=args.audio_channels, + normalize=args.norm_wav) + return train_set, valid_set diff --git a/docs/linux.md b/docs/linux.md new file mode 100644 index 0000000000000000000000000000000000000000..65a4f6da29acb3be457422d466ace9c5be68db94 --- /dev/null +++ b/docs/linux.md @@ -0,0 +1,23 @@ +# Linux support for Demucs + +If your distribution has at least Python 3.7, and you just wish to separate +tracks with Demucs, not train it, you can just run + +```bash +pip3 install --user -U demucs +# Then anytime you want to use demucs, just do +python3 -m demucs.separate -d cpu PATH_TO_AUDIO_FILE_1 +``` + +If Python is too old, or you want to be able to train, I recommend [installing Miniconda][miniconda], with Python 3.7 or more. + +```bash +conda activate +python3 install -U demucs +# Then anytime you want to use demucs, first do conda activate, then +python3 -m demucs.separate -d cpu PATH_TO_AUDIO_FILE_1 +``` + +Of course, you can also use a specific env for Demucs. + +[miniconda]: https://docs.conda.io/en/latest/miniconda.html#linux-installers \ No newline at end of file diff --git a/docs/mac.md b/docs/mac.md new file mode 100644 index 0000000000000000000000000000000000000000..bff4bd9491f73d9b3910e42965b266e9555ff9cb --- /dev/null +++ b/docs/mac.md @@ -0,0 +1,24 @@ +# Mac OS X support for Demucs + +If you have a sufficiently recent version of OS X, you can just run + +```bash +python3 -m pip install --user -U demucs +# Then anytime you want to use demucs, just do +python3 -m demucs.separate -d cpu PATH_TO_AUDIO_FILE_1 +``` + +If you do not already have Anaconda installed or much experience with the terminal on Mac OS X here are some detailed instructions: + +1. Download [Anaconda 3.8 (or more recent) 64 bits for MacOS][anaconda]: +2. Open [Anaconda Prompt in MacOSX][prompt] +3. Follow these commands: +```bash +conda activate +pip3 install -U demucs +# Then anytime you want to use demucs, first do conda activate, then +python3 -m demucs.separate -d cpu PATH_TO_AUDIO_FILE_1 +``` + +[anaconda]: https://www.anaconda.com/distribution/#download-section +[prompt]: https://docs.anaconda.com/anaconda/user-guide/getting-started/#open-nav-mac \ No newline at end of file diff --git a/docs/mdx.md b/docs/mdx.md new file mode 100644 index 0000000000000000000000000000000000000000..2a20f9cb0012cf9c92112091ccb883275980c1d4 --- /dev/null +++ b/docs/mdx.md @@ -0,0 +1,73 @@ +# Music DemiXing challenge (MDX) + +If you want to use Demucs for the [MDX challenge](https://www.aicrowd.com/challenges/music-demixing-challenge-ismir-2021), +please follow the instructions hereafter + +## Installing Demucs + +Follow the instructions from the [main README](https://github.com/facebookresearch/demucs#requirements) +in order to setup Demucs using Anaconda. You will need the full setup up for training, including soundstretch. + +## Getting MusDB-HQ + +Download [MusDB-HQ](https://zenodo.org/record/3338373) to some folder and unzip it. + +## Training Demucs + +Train Demucs (you might need to change the batch size depending on the number of GPUs available). +It seems 48 channels is enough to get the best performance on MusDB-HQ, and training will faster +and less memory demanding. In any case, the 64 channels versions is timing out on the challenge. +```bash +./run.py --channels=48 --batch_size 64 --musdb=PATH_TO_MUSDB --is_wav [EXTRA_FLAGS] +``` + +### Post training + +Once the training is completed, a new model file will be exported in `models/`. + +You can look at the SDR on the MusDB dataset using `python result_table.py`. + + +### Evaluate and export a model before training is over + +If you want to export a model before training is complete, use the following command: +```bash +python -m demucs [ALL EXACT TRAINING FLAGS] --save_model +``` +You can also pass the `--half` flag, in order to save weights in half precision. This will divide the model size by 2 and won't impact SDR. + +Once this is done, you can partially evaluate a model with +```bash +./run.py --test NAME_OF_MODEL.th --musdb=PATH_TO_MUSDB --is_wav +``` + +**Note:** `NAME_OF_MODEL.th` is given relative to the models folder (given by `--models`, defaults to `models/`), so don't include it in the name. + + +### Training smaller models + +If you want to quickly test idea, I would recommend training a 16 kHz model, and testing if things work there or not, before training the full 44kHz model. You can train one of those with +```bash +./run.py --channels=32 --samplerate 16000 --samples 160000 --data_stride 16000 --depth=5 --batch_size 64 --repitch=0 --musdb=PATH_TO_MUSDB --is_wav [EXTRA_FLAGS] +``` +(repitch must be turned off, because things will break at 16kHz). + +## Submitting your model + +1. Git clone [the Music Demixing Challenge - Starter Kit - Demucs Edition](https://github.com/adefossez/music-demixing-challenge-starter-kit). +2. Inside the starter kit, create a `models/` folder and copy over the trained model from the Demucs repo (renaming +it for instance `my_model.th`) +3. Inside the `test_demuc.py` file, change the function `prediction_setup`: comment the loading +of the pre-trained model, and uncomment the code to load your own model. +4. Edit the file `aicrowd.json` with your username. +5. Install [git-lfs](https://git-lfs.github.com/). Then run + +```bash +git lfs install +git add models/ +git add -u . +git commit -m "My Demucs submission" +``` +6. Follow the [submission instructions](https://github.com/AIcrowd/music-demixing-challenge-starter-kit/blob/master/docs/SUBMISSION.md). + +Best of luck 🤞 diff --git a/docs/release.md b/docs/release.md new file mode 100644 index 0000000000000000000000000000000000000000..32c259e359d4323ad266758988c29e468048644d --- /dev/null +++ b/docs/release.md @@ -0,0 +1,37 @@ +# Release notes for Demucs + +## 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! diff --git a/docs/windows.md b/docs/windows.md new file mode 100644 index 0000000000000000000000000000000000000000..ac889c6c706f53201ebf0eafb78c85256bee8a38 --- /dev/null +++ b/docs/windows.md @@ -0,0 +1,47 @@ +# Windows support for Demucs + +If you are using Windows, replace `python3` by `python.exe` in all the commands provided hereafter :) + +Parts of the code are untested on Windows (in particular, training a new model). If you don't have much experience with Anaconda, python or the shell, here are more detailed instructions. Note that **Demucs is not supported on 32bits systems** (as Pytorch is not available there). + +- First install Anaconda with **Python 3.7** or more recent, which you can find [here][install]. +- Start the [Anaconda prompt][prompt]. +- Type in the following commands: + +```bash +cd %HOMEPATH% +git clone -b main --single-branch https://github.com/facebookresearch/demucs ./demucs +cd ./demucs +conda env update -f environment-cpu.yml +conda activate demucs +python.exe -m demucs.separate -d cpu "PATH_TO_AUDIO_FILE_1" ["PATH_TO_AUDIO_FILE_2" ...] +``` + +The `"` around the filename are required if the path contains spaces. +The separated files will be under `C:\Users\YOUR_USERNAME\demucs\separated\demucs\`. The next time you want to use Demucs, start again the [Anaconda prompt][prompt] and simply run +```bash +cd %HOMEPATH% +conda activate demucs +cd demucs +python.exe -m demucs.separate -d cpu "PATH_TO_AUDIO_FILE_1" ... +``` + +## Updating Demucs + +In order to update Demucs, simply run the following from the Anaconda Prompt: +```bash +cd %HOMEPATH% +cd demucs +git pull +conda env update -f environment-cpu.yml +``` + +## Potential errors + +If you have an error saying that `mkl_intel_thread.dll` cannot be found, you can try to first run +`conda install -c defaults intel-openmp -f`. Then try again to run the `demucs.separate` command. If it still doesn't work, you can try to run first `set CONDA_DLL_SEARCH_MODIFICATION_ENABLE=1`, then again the `demucs.separate` command and hopefully it will work 🙏. +If you get a permission error, please try starting the Anaconda Prompt as administrator. + + +[install]: https://www.anaconda.com/distribution/#windows +[prompt]: https://docs.anaconda.com/anaconda/user-guide/getting-started/#open-prompt-win diff --git a/dora.py b/dora.py new file mode 100644 index 0000000000000000000000000000000000000000..589b456eb32432c73b2849c38ee5f2e5da747849 --- /dev/null +++ b/dora.py @@ -0,0 +1,267 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +""" +Dora the Explorer, special thank to @pierrestock. +""" +import argparse +import json +import logging +import shlex +import subprocess as sp +import time +from collections import namedtuple +from functools import partial +from itertools import product # noqa +from pathlib import Path + +import treetable as tt # really great package for ascii art tables + +from demucs.parser import get_name, get_parser + +logger = logging.getLogger(__name__) +parser = get_parser() +logs = Path("logs") +logs.mkdir(exist_ok=True) + +Job = namedtuple("Job", "args name sid") + + +def fname(name, kind): + return logs / f"{name}.{kind}" + + +def get_sid(name): + sid_file = fname(name, "sid") + try: + return int(open(sid_file).read().strip()) + except IOError: + return None + + +def cancel(sid): + sp.run(["scancel", str(sid)], check=True) + + +def reset_job(name): + sid_file = fname(name, "sid") + if sid_file.is_file(): + sid_file.unlink() + + +def get_done(name): + done_file = fname(name, "done") + return done_file.exists() + + +def get_metrics(name): + json_file = fname(name, "json") + try: + return json.load(open(json_file)) + except IOError: + return [] + + +def schedule(name, args, nodes=1, partition="priority", time=2 * 24 * 60, large=True, gpus=8): + log = fname(name, "log") + command = [ + "sbatch", + f"--job-name={name}", + f"--output={log}.%t", + "--mem=460G", + f"--cpus-per-task={8*gpus}", + f"--gpus={gpus}", + f"--nodes={nodes}", + "--tasks-per-node=1", + f"--partition={partition}", + # "--exclude=learnfair0748,learnfair0821", + "--comment=Old codebase, not requeue, very few jobs", + f"--time={time}", + ] + if large: + command += ["--constraint=volta32gb"] + srun_flags = f"--output={shlex.quote(str(log))}.%t" + + run_cmd = ["#!/bin/bash"] + run_cmd.append(f"srun {srun_flags} python3 run_slurm.py " + " ".join(args)) + result = sp.run(command, stdout=sp.PIPE, input="\n".join(run_cmd).encode('utf-8'), + check=True).stdout.decode('utf-8') + sid = int(result.strip().rsplit(' ', 1)[1]) + open(fname(name, "sid"), "w").write(str(sid)) + return sid + + +def _check(sids): + cs_ids = ','.join(map(str, sids)) + result = sp.run(['squeue', f'-j{cs_ids}', '-o%A,%T,%P', '--noheader'], + check=True, + capture_output=True) + lines = result.stdout.decode('utf-8').strip().split('\n') + results = {} + for line in lines: + line = line.strip() + if not line: + continue + sid, status, partition = line.split(',', 2) + sid = int(sid) + results[sid] = status.lower() + for sid in sids: + if sid not in results: + results[sid] = 'failed' + return results + + +class Monitor: + def __init__(self, cancel=False, base=[]): + self.cancel = cancel + self.base = base + self.jobs = [] + + def schedule(self, args, *vargs, **kwargs): + args = self.base + args + name = get_name(parser, parser.parse_args(args)) + sid = get_sid(name) + if sid is None and not self.cancel: + sid = schedule(name, args, *vargs, **kwargs) + self.jobs.append(Job(sid=sid, name=name, args=args)) + + def gc(self): + names = set(job.name for job in self.jobs) + for f in logs.iterdir(): + stem, suffix = f.name.rsplit(".", 1) + if suffix == "sid": + if stem not in names: + sid = get_sid(stem) + if sid is not None: + print(f"GCing {stem} / {sid}") + cancel(sid) + f.unlink() + + def check(self, trim=None, reset=False): + to_check = [] + statuses = {} + for job in self.jobs: + if get_done(job.name): + statuses[job.sid] = "done" + elif job.sid is not None: + to_check.append(job.sid) + statuses.update(_check(to_check)) + + if trim is not None: + trim = len(get_metrics(self.jobs[trim].name)) + + lines = [] + for index, job in enumerate(self.jobs): + status = statuses.get(job.sid, "failed") + if status in ["failed", "completing"] and reset: + reset_job(job.name) + status = "reset" + + meta = {'name': job.name, 'sid': job.sid, 'status': status[:2], "index": index} + metrics = get_metrics(job.name) + if trim is not None: + metrics = metrics[:trim] + meta["epoch"] = len(metrics) + if metrics: + metrics = metrics[-1] + else: + metrics = {} + lines.append({'meta': meta, 'metrics': metrics}) + + table = tt.table(shorten=True, + groups=[ + tt.group("meta", [ + tt.leaf("index", align=">"), + tt.leaf("name"), + tt.leaf("sid", align=">"), + tt.leaf("status"), + tt.leaf("epoch", align=">") + ]), + tt.group("metrics", [ + tt.leaf("train", ".2%"), + tt.leaf("valid", ".2%"), + tt.leaf("best", ".2%"), + tt.leaf("true_model_size", ".2f"), + tt.leaf("compressed_model_size", ".2f"), + ]) + ]) + print(tt.treetable(lines, table, colors=["0", "38;5;245"])) + + +def main(): + parser = argparse.ArgumentParser("grid.py") + parser.add_argument("-c", "--cancel", action="store_true", help="Cancel all jobs") + parser.add_argument( + "-r", + "--reset", + action="store_true", + help="Will reset the state of failed jobs. Next invocation will reschedule them") + parser.add_argument("-t", "--trim", type=int, help="Trim metrics to match job with given index") + args = parser.parse_args() + + monitor = Monitor(base=[], cancel=args.cancel) + sched = partial(monitor.schedule, nodes=1) + + tasnet = ["--tasnet", "--split_valid", "--samples=80000", "--X=10", "-b", "32"] + extra_path = Path.home() / "musdb_raw_44_allstems" + extra = [f"--raw={extra_path}"] + + sched([]) + sched(extra) + sched(tasnet) + sched(tasnet + ["--repitch=0"]) + sched(tasnet + extra + ["--repitch=0"]) + + ch48 = ["--channels=48"] + sched(ch48) + + ch32 = ["--channels=32"] + sched(ch32) + + # Main models + for seed in [43, 44]: + base = [f"--seed={seed}"] + sched(base) + sched(base + extra) + sched(base + tasnet) + sched(base + tasnet + extra) + + # Ablation study + + sched(["--no_glu"]) + sched(["--no_rewrite"]) + sched(["--context=1"]) + sched(["--rescale=0"]) + sched(["--mse"]) + sched(["--lstm_layers=0"]) + sched(["--lstm_layers=0", "--depth=7"]) + sched(["--no_resample"]) + sched(["--repitch=0"]) + + # Quantization + sched(["--diffq=0.0003"]) + + if args.cancel: + for job in monitor.jobs: + if job.sid is not None: + print(f"Canceling {job.name}/{job.sid}") + cancel(job.sid) + return + + names = [job.name for job in monitor.jobs] + json.dump(names, open(logs / "herd.json", "w")) + + # Cancel any running job that was removed from the above sched calls. + monitor.gc() + while True: + if args.reset: + monitor.check(reset=True) + return + monitor.check(trim=args.trim) + time.sleep(5 * 60) + + +if __name__ == "__main__": + main() diff --git a/environment-cpu.yml b/environment-cpu.yml new file mode 100644 index 0000000000000000000000000000000000000000..f738933cf4644239713fdff97d6a106366a59a4d --- /dev/null +++ b/environment-cpu.yml @@ -0,0 +1,22 @@ +name: demucs + +channels: + - pytorch + - conda-forge + +dependencies: + - python>=3.7,<3.9 + - ffmpeg>=4.2 + - pytorch>=1.8.1 + - torchaudio>=0.8 + - tqdm>=4.36 + - pip + - pip: + - diffq>=0.1 + - julius>=0.2.3 + - lameenc>=1.2 + - musdb>=0.4.0 + - museval>=0.4.0 + - soundfile + - treetable>=0.2.3 + diff --git a/environment-cuda.yml b/environment-cuda.yml new file mode 100644 index 0000000000000000000000000000000000000000..aaa9179bf4e2a3e34ae39605ac174fff26f52551 --- /dev/null +++ b/environment-cuda.yml @@ -0,0 +1,22 @@ +name: demucs + +channels: + - pytorch + - conda-forge + +dependencies: + - python>=3.7,<3.9 + - ffmpeg>=4.2 + - pytorch>=1.8.1 + - cudatoolkit>=10 + - torchaudio>=0.8 + - tqdm>=4.36 + - pip + - pip: + - diffq>=0.1 + - julius>=0.2.3 + - lameenc>=1.2 + - musdb>=0.4.0 + - museval>=0.4.0 + - soundfile + - treetable>=0.2.3 diff --git a/hubconf.py b/hubconf.py new file mode 100644 index 0000000000000000000000000000000000000000..be932691b9b80645cdf8d147daee019f45077614 --- /dev/null +++ b/hubconf.py @@ -0,0 +1,9 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +dependencies = ['diffq', 'julius', 'torch', 'tqdm'] + +from demucs.pretrained import demucs, tasnet # noqa diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..71b6fe787699de8a29a70bc5b4c5e5781e47a00e --- /dev/null +++ b/requirements.txt @@ -0,0 +1,10 @@ +# those requirements are only for separating tracks with pre-trained model. +# If you require training or model evaluation, please look at the conda environment +# files, environmnent-cpu.yml and environment-cuda.yml +diffq>=0.1 +lameenc>=1.2 +julius>=0.2.3 +numpy +torch>=1.8.1 +torchaudio>=0.8 +tqdm diff --git a/result_table.py b/result_table.py new file mode 100644 index 0000000000000000000000000000000000000000..30ae2a6518f2d8c43fbfcf684f3c58f346f2c8d7 --- /dev/null +++ b/result_table.py @@ -0,0 +1,137 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import gzip +import json +import sys +from collections import defaultdict +from pathlib import Path + +import numpy as np +import treetable as tt + +BASELINES = [ + 'WaveUNet', + 'MMDenseLSTM', + 'OpenUnmix', + 'IRM2', +] +EVALS = Path("evals") +LOGS = Path("logs") +BASELINE_EVALS = Path("baselines") +STD_KEY = "seed" + +parser = argparse.ArgumentParser("result_table.py") +parser.add_argument("-p", + "--paper", + action="store_true", + help="show results from the paper experiment") +parser.add_argument("-i", "--individual", action="store_true", help="no aggregation by seed") +parser.add_argument("-l", "--latex", action="store_true", help="output easy to copy latex") +parser.add_argument("metric", default="SDR", nargs="?") +args = parser.parse_args() + +if args.paper: + EVALS = Path("results/evals") + LOGS = Path("results/logs") + + +def read_track(metric, results, pool=np.nanmedian): + all_metrics = {} + for target in results["targets"]: + source = target["name"] + metrics = [frame["metrics"][metric] for frame in target["frames"]] + metrics = pool(metrics) + all_metrics[source] = metrics + return all_metrics + + +def read(metric, path, pool=np.nanmedian): + all_metrics = defaultdict(list) + for f in path.iterdir(): + if f.name.endswith(".json.gz"): + results = json.load(gzip.open(f, "r")) + metrics = read_track(metric, results, pool=pool) + for source, value in metrics.items(): + all_metrics[source].append(value) + return {key: np.array(value) for key, value in all_metrics.items()} + + +all_stats = defaultdict(list) +for name in BASELINES: + all_stats[name] = [read(args.metric, BASELINE_EVALS / name / "test")] + +for path in EVALS.iterdir(): + results = path / "results" / "test" + if not results.exists(): + continue + if not args.paper and not (LOGS / (path.name + ".done")).exists(): + continue + name = path.name + model = "Demucs" + if "tasnet" in name: + model = "Tasnet" + if name == "default": + parts = [] + else: + parts = [p.split("=") for p in name.split(" ") if p != '--tasnet'] + if not args.individual: + parts = [(k, v) for k, v in parts if k != STD_KEY] + name = model + " " + " ".join(f"{k}={v}" for k, v in parts) + stats = read(args.metric, results) + if (not stats or len(stats["drums"]) != 50): + print(f"Missing stats for {results}", file=sys.stderr) + else: + all_stats[name].append(stats) + +metrics = [tt.leaf("score", ".2f"), tt.leaf("std", ".2f")] +sources = ["drums", "bass", "other", "vocals"] + +mytable = tt.table([tt.leaf("name"), tt.group("all", metrics + [tt.leaf("count")])] + + [tt.group(source, metrics) for idx, source in enumerate(sources)]) + +lines = [] +for name, stats in all_stats.items(): + line = {"name": name} + if 'accompaniment' in stats: + del stats['accompaniment'] + alls = [] + for source in sources: + stat = [np.nanmedian(s[source]) for s in stats] + alls.append(stat) + line[source] = {"score": np.mean(stat), "std": np.std(stat) / len(stat)**0.5} + alls = np.array(alls) + line["all"] = { + "score": alls.mean(), + "std": alls.mean(0).std() / alls.shape[1]**0.5, + "count": alls.shape[1] + } + lines.append(line) + + +def latex_number(m): + out = f"{m['score']:.2f}" + if m["std"] > 0: + std = "{:.2f}".format(m["std"])[1:] + out += f" $\\scriptstyle\\pm {std}$" + return out + + +lines.sort(key=lambda x: -x["all"]["score"]) +if args.latex: + for line in lines: + cols = [ + line['name'], + latex_number(line["all"]), + latex_number(line["drums"]), + latex_number(line["bass"]), + latex_number(line["other"]), + latex_number(line["vocals"]) + ] + print(" & ".join(cols) + r" \\") +else: + print(tt.treetable(lines, mytable, colors=['33', '0'])) diff --git a/results/.DS_Store b/results/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..21288f8f0495338e0f2d5eca188f13502ff3ea3c Binary files /dev/null and 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@@ -0,0 +1,66 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +""" +Run training locally on all visible GPUs. Start only +one task per node as this script will spawn one child for each GPU. +""" +import subprocess as sp +import sys +import time + +import torch as th + +from demucs.utils import free_port + + +def main(): + args = sys.argv[1:] + gpus = th.cuda.device_count() + + port = free_port() + args += ["--world_size", str(gpus), "--master", f"127.0.0.1:{port}"] + tasks = [] + + for gpu in range(gpus): + kwargs = {} + if gpu > 0: + kwargs['stdin'] = sp.DEVNULL + kwargs['stdout'] = sp.DEVNULL + # We keep stderr to see tracebacks from children. + tasks.append(sp.Popen(["python3", "-m", "demucs"] + args + ["--rank", str(gpu)], **kwargs)) + tasks[-1].rank = gpu + + failed = False + try: + while tasks: + for task in tasks: + try: + exitcode = task.wait(0.1) + except sp.TimeoutExpired: + continue + else: + tasks.remove(task) + if exitcode: + print(f"Task {task.rank} died with exit code " + f"{exitcode}", + file=sys.stderr) + failed = True + if failed: + break + time.sleep(1) + except KeyboardInterrupt: + for task in tasks: + task.terminate() + raise + if failed: + for task in tasks: + task.terminate() + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/run_slurm.py b/run_slurm.py new file mode 100755 index 0000000000000000000000000000000000000000..a880058d4ebc12883a756e0c22f0a596662a2043 --- /dev/null +++ b/run_slurm.py @@ -0,0 +1,89 @@ +#!/usr/bin/env python3 +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +""" +Run training from Slurm on all visible GPUs. Start only +one task per node as this script will spawn one child for each GPU. +This will not schedule a job but instead should be launched from srun/sbatch. +""" +import os +import subprocess as sp +import sys +import time + +import torch as th + +from demucs.utils import free_port + + +def main(): + args = sys.argv[1:] + gpus = th.cuda.device_count() + n_nodes = int(os.environ['SLURM_JOB_NUM_NODES']) + node_id = int(os.environ['SLURM_NODEID']) + job_id = int(os.environ['SLURM_JOBID']) + + rank_offset = gpus * node_id + hostnames = sp.run(['scontrol', 'show', 'hostnames', os.environ['SLURM_JOB_NODELIST']], + capture_output=True, + check=True).stdout + master_addr = hostnames.split()[0].decode('utf-8') + + if n_nodes == 1: + port = free_port() + else: + port = 20_000 + (job_id % 40_000) + args += ["--world_size", str(n_nodes * gpus), "--master", f"{master_addr}:{port}"] + tasks = [] + + print("About to go live", master_addr, node_id, n_nodes, file=sys.stderr) + sys.stderr.flush() + + for gpu in range(gpus): + kwargs = {} + if gpu > 0: + kwargs['stdin'] = sp.DEVNULL + kwargs['stdout'] = sp.DEVNULL + # We keep stderr to see tracebacks from children. + tasks.append( + sp.Popen(["python3", "-m", "demucs"] + args + + ["--rank", str(rank_offset + gpu)], **kwargs)) + tasks[-1].rank = rank_offset + gpu + + failed = False + try: + while tasks: + for task in tasks: + try: + exitcode = task.wait(0.1) + except sp.TimeoutExpired: + continue + else: + tasks.remove(task) + if exitcode: + print(f"Task {task.rank} died with exit code " + f"{exitcode}", + file=sys.stderr) + failed = True + else: + print(f"Task {task.rank} exited successfully") + if failed: + break + time.sleep(1) + except KeyboardInterrupt: + for task in tasks: + task.terminate() + raise + if failed: + for task in tasks: + task.terminate() + + sp.run(["scancel", str(job_id)], check=True) + sys.exit(1) + + +if __name__ == "__main__": + main() diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000000000000000000000000000000000000..d54d56a0d403187f4add6e76e85b5346b28dcf67 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,8 @@ +[pep8] +max-line-length = 100 + +[flake8] +max-line-length = 100 + +[yapf] +column_limit = 100 diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..812ff33f332b0f54e2915834dc941f20abcd3b67 --- /dev/null +++ b/setup.py @@ -0,0 +1,60 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. +# author: adefossez +# Inspired from https://github.com/kennethreitz/setup.py + +from pathlib import Path +from importlib.machinery import SourceFileLoader + + +from setuptools import setup + +NAME = 'demucs' +DESCRIPTION = 'Music source separation in the waveform domain.' + +URL = 'https://github.com/facebookresearch/demucs' +EMAIL = 'defossez@fb.com' +AUTHOR = 'Alexandre Défossez' +REQUIRES_PYTHON = '>=3.7.0' + +HERE = Path(__file__).parent + +# Trick taken from https://github.com/sigsep/sigsep-mus-db/blob/master/setup.py +VERSION = SourceFileLoader( + 'demucs', 'demucs/__init__.py' +).load_module().__version__ + +REQUIRED = [i.strip() for i in open(HERE / "requirements.txt")] + +try: + with open(HERE / "README.md", encoding='utf-8') as f: + long_description = '\n' + f.read() +except FileNotFoundError: + long_description = DESCRIPTION + +setup( + name=NAME, + version=VERSION, + description=DESCRIPTION, + long_description=long_description, + long_description_content_type='text/markdown', + author=AUTHOR, + author_email=EMAIL, + python_requires=REQUIRES_PYTHON, + url=URL, + packages=['demucs'], + install_requires=REQUIRED, + extras_require={'dev': ['flake8']}, + include_package_data=True, + license='MIT License', + classifiers=[ + # Trove classifiers + # Full list: https://pypi.python.org/pypi?%3Aaction=list_classifiers + 'License :: OSI Approved :: MIT License', + 'Topic :: Multimedia :: Sound/Audio', + 'Topic :: Scientific/Engineering :: Artificial Intelligence', + ], +) diff --git a/test.mp3 b/test.mp3 new file mode 100644 index 0000000000000000000000000000000000000000..668604d876880d39913ef73bcf9df4b4308a9d70 Binary files /dev/null and b/test.mp3 differ diff --git a/valid_table.py b/valid_table.py new file mode 100644 index 0000000000000000000000000000000000000000..2e0105a016ff2c9d2406bf9d02d3283333f18974 --- /dev/null +++ b/valid_table.py @@ -0,0 +1,63 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# All rights reserved. +# +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree. + +import argparse +import json +from collections import defaultdict +from pathlib import Path + +import numpy as np +import treetable as tt + +LOGS = Path("logs") +STD_KEY = "seed" +METRIC = "best" + +parser = argparse.ArgumentParser("result_table.py") +parser.add_argument("-p", + "--paper", + action="store_true", + help="show results from the paper experiment") +parser.add_argument("-i", "--individual", action="store_true", help="no aggregation by seed") +args = parser.parse_args() + +if args.paper: + LOGS = Path("results/logs") + +all_stats = defaultdict(list) + +for path in LOGS.iterdir(): + if path.suffix == ".json" and (args.paper or path.with_suffix(".done").exists()): + metric = json.load(open(path))[-1][METRIC] + name = path.stem + model = "Demucs" + if "tasnet" in name: + model = "Tasnet" + if name == "default": + parts = [] + else: + parts = [p.split("=") for p in name.split(" ") if "--tasnet" not in p] + if not args.individual: + parts = [(k, v) for k, v in parts if k != STD_KEY] + name = model + " " + " ".join(f"{k}={v}" for k, v in parts) + all_stats[name].append(metric) + +metrics = [tt.leaf("score", ".4f"), tt.leaf("std", ".3f"), tt.leaf("count", ".2f")] + +mytable = tt.table([tt.leaf("name"), tt.group("valid", metrics)]) + +lines = [] +for name, stats in all_stats.items(): + line = {"name": name} + stats = np.array(stats) + line["valid"] = { + "score": stats.mean(), + "std": stats.std() / stats.shape[0]**0.5, + "count": stats.shape[0] + } + lines.append(line) +lines.sort(key=lambda x: x["valid"]["score"]) +print(tt.treetable(lines, mytable, colors=['33', '0']))