This model card provides details and information about EnCodec, a state-of-the-art real-time audio codec developed by Meta AI. EnCodec is a high-fidelity audio codec leveraging neural networks. It introduces a streaming encoder-decoder architecture with quantized latent space, trained in an end-to-end fashion. The model simplifies and speeds up training using a single multiscale spectrogram adversary that efficiently reduces artifacts and produces high-quality samples. It also includes a novel loss balancer mechanism that stabilizes training by decoupling the choice of hyperparameters from the typical scale of the loss. Additionally, lightweight Transformer models are used to further compress the obtained representation while maintaining real-time performance. - **Repository:** GitHub Repository - **Paper:** EnCodec: End-to-End Neural Audio Codec

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encodec_32_khz_bass_1_drums_1_other_4

facebook

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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encodec_32khz

facebook

This model card provides details and information about EnCodec 32kHz, a state-of-the-art real-time audio codec developed by Meta AI. This EnCodec checkpoint was trained specifically as part of the MusicGen project, and is intended to be used in conjuction with the MusicGen models. EnCodec is a high-fidelity audio codec leveraging neural networks. It introduces a streaming encoder-decoder architecture with quantized latent space, trained in an end-to-end fashion. The model simplifies and speeds up training using a single multiscale spectrogram adversary that efficiently reduces artifacts and produces high-quality samples. It also includes a novel loss balancer mechanism that stabilizes training by decoupling the choice of hyperparameters from the typical scale of the loss. Additionally, lightweight Transformer models are used to further compress the obtained representation while maintaining real-time performance. This variant of EnCodec is trained on 20k of music data, consisting of an internal dataset of 10K high-quality music tracks, and on the ShutterStock and Pond5 music datasets. - **Repository:** GitHub Repository - **Paper:** Simple and Controllable Music Generation

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encodec_48khz

facebook

This model card provides details and information about EnCodec, a state-of-the-art real-time audio codec developed by Meta AI. EnCodec is a high-fidelity audio codec leveraging neural networks. It introduces a streaming encoder-decoder architecture with quantized latent space, trained in an end-to-end fashion. The model simplifies and speeds up training using a single multiscale spectrogram adversary that efficiently reduces artifacts and produces high-quality samples. It also includes a novel loss balancer mechanism that stabilizes training by decoupling the choice of hyperparameters from the typical scale of the loss. Additionally, lightweight Transformer models are used to further compress the obtained representation while maintaining real-time performance. EnCodec can be used directly as an audio codec for real-time compression and decompression of audio signals. It provides high-quality audio compression and efficient decoding. The model was trained on various bandwiths, which can be specified when encoding (compressing) and decoding (decompressing). Two different setup exist for EnCodec:

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esm-1b

facebook

This repository has been deprecated and will be deleted shortly. All ESM models have been moved to their official names to match their naming at the original FAIR repo. You can now find the ESM-1b model at facebook/esm1b_t33_650M_UR50S.

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esm_t6_8M_UR50D

facebook

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esm1b_t33_650M_UR50S

facebook

ESM-1b (paper, repository) is a transformer protein language model, trained on protein sequence data without label supervision. The model is pretrained on Uniref50 with an unsupervised masked language modeling (MLM) objective, meaning the model is trained to predict amino acids from the surrounding sequence context. This pretraining objective allows ESM-1b to learn generally useful features which can be transferred to downstream prediction tasks. ESM-1b has been evaluated on a variety of tasks related to protein structure and function, including remote homology detection, secondary structure prediction, contact prediction, and prediction of the effects of mutations on function, producing state-of-the-art results. **Important note**: ESM-2 is now available in a range of checkpoint sizes. For most tasks, ESM-2 performance will be superior to ESM-1 and ESM-1b, and so we recommend using it instead unless your goal is explicitly to compare against ESM-1b. The ESM-2 checkpoint closest in size to ESM-1b is esm2_t33_650M_UR50D. The ESM-1b model is based on the RoBERTa architecture and training procedure, using the Uniref50 2018_03 database of protein sequences. Note that the pretraining is on the raw protein sequences only. The training is purely unsupervised -- during training no labels are given related to structure or function.

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