audio-magnet-small
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audiobox-aesthetics
facebookThis model has been pushed to the Hub using the PytorchModelHubMixin integration: - Code: https://github.com/facebookresearch/audiobox-aesthetics - Paper: https://huggingface.co/papers/2502.05139 --- README below copied from https://github.com/facebookresearch/audiobox-aesthetics * Paper arXiv / MetaAI. * Blogpost ai.meta.com
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詳細を見る ↗audiogen-medium
facebookAudioGen is an autoregressive transformer LM that synthesizes general audio conditioned on text (Text-to-Audio). Internally, AudioGen operates over discrete representations learnt from the raw waveform, using an EnCodec tokenizer. AudioGen was presented at AudioGen: Textually Guided Audio Generation by *Felix Kreuk, Gabriel Synnaeve, Adam Polyak, Uriel Singer, Alexandre Défossez, Jade Copet, Devi Parikh, Yaniv Taigman, Yossi Adi*. AudioGen 1.5B is a variant of the original AudioGen model that follows MusicGen architecture. More specifically, it is trained over a 16kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz with a delay pattern between the codebooks. Having only 50 auto-regressive steps per second of audio, this AudioGen model allows faster generation while reaching similar performances to the original AudioGen model introduced in the paper.
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詳細を見る ↗audioseal
facebookWe introduce AudioSeal, a method for speech localized watermarking, with state-of-the-art robustness and detector speed. It jointly trains a generator that embeds a watermark in the audio, and a detector that detects the watermarked fragments in longer audios, even in the presence of editing. Audioseal achieves state-of-the-art detection performance of both natural and synthetic speech at the sample level (1/16k second resolution), it generates limited alteration of signal quality and is robust to many types of audio editing. Audioseal is designed with a fast, single-pass detector, that significantly surpasses existing models in speed — achieving detection up to two orders of magnitude faster, making it ideal for large-scale and real-time applications. AudioSeal requires Python >=3.8, Pytorch >= 1.13.0, omegaconf, julius, and numpy. To install from PyPI: - AudioSeal Generator. It takes as input an audio signal (as a waveform), and outputs a watermark of the same size as the input, that can be added to the input to watermark it. Optionally, it can also take as input a secret message of 16-bits that will be encoded in the watermark. - AudioSeal Detector. It takes as input an audio signal (as a waveform), and outputs a probability that the input contains a watermark at each sample of the audio (every 1/16k s). Optionally, it may also output the secret message encoded in the watermark.
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詳細を見る ↗autopartgen
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詳細を見る ↗bart-base
facebookBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
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詳細を見る ↗bart-large
facebookBART model pre-trained on English language. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in this repository. Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
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詳細を見る ↗bart-large-cnn
facebookBART model pre-trained on English language, and fine-tuned on CNN Daily Mail. It was introduced in the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Lewis et al. and first released in [this repository (https://github.com/pytorch/fairseq/tree/master/examples/bart). Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. BART is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
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詳細を見る ↗bart-large-mnli
facebookThis is the checkpoint for bart-large after being trained on the MultiNLI (MNLI) dataset. Additional information about this model: - The bart-large model page - BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension - BART fairseq implementation This method is surprisingly effective in many cases, particularly when used with larger pre-trained models like BART and Roberta. See this blog post for a more expansive introduction to this and other zero shot methods, and see the code snippets below for examples of using this model for zero-shot classification both with Hugging Face's built-in pipeline and with native Transformers/PyTorch code.
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詳細を見る ↗bart-large-xsum
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詳細を見る ↗blaser-2.0-qe
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詳細を見る ↗blaser-2.0-ref
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詳細を見る ↗blenderbot-1B-distill
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詳細を見る ↗blenderbot-3B
facebookBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
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詳細を見る ↗blenderbot-400M-distill
facebookBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
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詳細を見る ↗blenderbot-90M
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詳細を見る ↗blenderbot_small-90M
facebookBuilding open-domain chatbots is a challenging area for machine learning research. While prior work has shown that scaling neural models in the number of parameters and the size of the data they are trained on gives improved results, we show that other ingredients are important for a high-performing chatbot. Good conversation requires a number of skills that an expert conversationalist blends in a seamless way: providing engaging talking points and listening to their partners, both asking and answering questions, and displaying knowledge, empathy and personality appropriately, depending on the situation. We show that large scale models can learn these skills when given appropriate training data and choice of generation strategy. We build variants of these recipes with 90M, 2.7B and 9.4B parameter neural models, and make our models and code publicly available. Human evaluations show our best models are superior to existing approaches in multi-turn dialogue in terms of engagingness and humanness measurements. We then discuss the limitations of this work by analyzing failure cases of our models.
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詳細を見る ↗blt
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詳細を見る ↗blt-1b
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詳細を見る ↗blt-7b
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詳細を見る ↗blt-entropy
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詳細を見る ↗boxer
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詳細を見る ↗chameleon-30b
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詳細を見る ↗chameleon-7b
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