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The UnivNet model is a state-of-the-art neural vocoder which synthesizes audio waveforms from full-band MEL spectrograms, introduced in "UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation" by Won Jang, Dan Lim, Jaesam Yoon, Bongwan Kim, Juntae Kim. UnivNet is a generative adversarial network (GAN) in which the generator is trained to convert real (or fake, during training) log MEL spectrograms to waveforms, and the discriminator is trained to classify whether input waveforms are real or fake. From the original paper abstract: > Most neural vocoders employ band-limited mel-spectrograms to generate waveforms. If full-band spectral features are used as the input, the vocoder can be provided with as much acoustic information as possible. However, in some models employing full-band mel-spectrograms, an over-smoothing problem occurs as part of which non-sharp spectrograms are generated. To address this problem, we propose UnivNet, a neural vocoder that synthesizes high-fidelity waveforms in real time. Inspired by works in the field of voice activity detection, we added a multi-resolution spectrogram discriminator that employs multiple linear spectrogram magnitudes computed using various parameter sets. Using full-band mel-spectrograms as input, we expect to generate high-resolution signals by adding a discriminator that employs spectrograms of multiple resolutions as the input. In an evaluation on a dataset containing information on hundreds of speakers, UnivNet obtained the best objective and subjective results among competing models for both seen and unseen speakers. These results, including the best subjective score for text-to-speech, demonstrate the potential for fast adaptation to new speakers without a need for training from scratch. This checkpoint was released as part of an unofficial implementation by maum-ai (on which the `transformers` implementation is also based). As far as I know, there is no official model or code release by the original authors from Kakao Enterprise.

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Kavi-350M

dgflash

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ppo-Huggy

dgodderis

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Game AI
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Int4 quantization (group_size 64, transformer block Linear weights only) of dgrauet/ltx-2.3-mlx, the MLX conversion of Lightricks/LTX-2.3. These weights are a derivative work and are distributed under the same terms as the model they were converted from. A verbatim copy ships with them as `LICENSE`, and the upstream text is at https://github.com/Lightricks/LTX-2/blob/main/LICENSE-2. Use of these weights is subject to it, including any restrictions it attaches. These weights can be used with ltx-2-mlx, a native MLX inference pipeline for LTX-2.3 on Apple Silicon:

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Int8 quantization (group_size 64, transformer block Linear weights only) of dgrauet/ltx-2.3-mlx, the MLX conversion of Lightricks/LTX-2.3. These weights are a derivative work and are distributed under the same terms as the model they were converted from. A verbatim copy ships with them as `LICENSE`, and the upstream text is at https://github.com/Lightricks/LTX-2/blob/main/LICENSE-2. Use of these weights is subject to it, including any restrictions it attaches. These weights can be used with ltx-2-mlx, a native MLX inference pipeline for LTX-2.3 on Apple Silicon:

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This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: https://seb.sbert.net

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Part of the developments at the Digital Humanities@University of Bern. Developed by Jonas Widmer and Tobias Hodel in conjunction with researchers and institutions mentioned below. Finetuned on Kurrent-dataset, containing: - Material from the State Archives of Zurich ("Regierungsratsprotokolle"), provided by the State Archives of Zurich - Lecture notes of Humboldt Lectures, provided by the Berlin-Brandenburgian Academy of Sciences - Diary of Eugen Huber, provided by the University of Zurich - Handwriting and Copies by and of Gottfried Semper (provided by the respective research project at ETH Zürich and USI Mendrisio) - Konzilsprotokolle, University of Greifswald (19th century) - as well as many other smaller collections/examples This is a `VisionEncoderDecoderModel` for **line images**: it reads one cropped text line per call, which is also what the CER above was measured on. A whole page is not an input it has — pass one and it returns a fragment of it.

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ball

dhalsim33

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