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        "author": "Antoine CAILLON",
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        "description": "# MSPrior\n\n### A multi(scale/stream) prior model for realtime temporal learning\n\n## Disclaimer\n\nThis is an experimental project that *will* be subject to lots of changes.\n\n## Installation\n\n```bash\npip install acids-msprior\n```\n\n## Usage\n\nMSPrior assumes you have\n\n1. A pretrained RAVE model exported **without streaming** as a torchscript `.ts` file\n2. The dataset on which RAVE has been trained (a folder of audio files).\n\n### 1. Preprocessing\n\nMSPrior operates on the latent representation yielded by RAVE. Therefore, we start by encoding the entirety of the audio dataset into a latent dataset. \n\n```bash\nmsprior preprocess --audio /path/to/audio/folder --out_path /path/to/output/folder --rave /path/to/pretrained/rave.ts\n```\n\n### 2. Training\n\nMSPrior has several possible configurations. The default is a ALiBi-Transformer with a skip prediction backend, which can run in realtime on powerful computers (e.g. Apple M1-2 chips, GPU enabled Linux stations). A less demanding configuration is a large GRU. Both configurations can launched be using\n\n```bash\nmsprior train --config configuration  --db_path /path/to/preprocessed/dataset --name training_name --pretrained_embedding /path/to/pretrained/rave.ts\n```\n\nHere are the different configurations available\n\n\n<table>\n<thead>\n<tr>\n<th>Name</th>\n<th>Description</th>\n</tr>\n</thead>\n<tbody>\n\n<tr>\n<td>decoder_only</td>\n<td rowspan=2>Unconditional autoregressive models, relying solely on previous samples to produce a prediction. The recurrent mode uses a Gated Recurrent Unit instead of a Transformer, suitable for small datasets and lower computational requirements.</td>\n</tr>\n\n<tr>\n<td>recurrent</td>\n</tr>\n\n<tr>\n<td>encoder_decoder</td>\n<td rowspan=2>Encoder / decoder autoregressive mode, where the generation process is conditioned by an external input (aka seq2seq). The continuous version is based on <i>continuous features</i> instead of a discrete token sequence.</td>\n</tr>\n\n<tr>\n<td>encoder_decoder_continuous</td>\n</tr>\n\n</tbody>\n</table>\n\nThe configurations `decoder_only` and `recurrent` are readily usable, the seq2seq variants depends on another project called `rave2vec` that will be open sourced in the near future.  \n\n### 3. Export\n\nExport your model to a `.ts` file that you can load inside the [nn~ external for Max/MSP and PureData](https://github.com/acids-ircam/nn_tilde).\n\n```bash\nmsprior export --run /path/to/your/run\n```\n\n**WARNING**\n\nIf you are training on top of a *continuous* rave (i.e. anything but the `discrete` configuration), you shoud pass the `--continuous` flag during export\n\n```bash\nmsprior export --run /path/to/your/run --continuous\n```\n\n### 4. Realtime usage\n\nOnce exported, you can load the model inside MaxMSP following the image below.\n\n![Max Msp usage](docs/maxmsp_usage.png)\n\nNote that additional inputs (e.g. semantic) are only available when using seq2seq models. The last output yields the perplexity of the model.\n\n## Funding\n\nThis work is funded by the DAFNE+ N\u00b0 101061548 project, and is led at IRCAM in the STMS lab.\n\n<img src=\"https://ec.europa.eu/regional_policy/images/information-sources/logo-download-center/eu_co_funded_en.jpg\" width=200px/>\n",
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