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AI モデル 2119
- mms-tts-kin↗
facebook
This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.
- Qwen3.8-Flash-Next-DS4-Q4↗
ivanfioravanti
DS4 quantization of Qwen/Qwen3.8-Flash-Next, built from the official BF16 checkpoint at revision `de4b8e4d43b917e7706784d8bb445c9af86a3540` with an importance-matrix calibrated Q4_K routed-expert recipe. The current release is the **qwen4exp-schema** structure: standard llama.cpp GGUF tensor naming that the DS4 engine (Metal graph) loads directly, with the 51B-value PLE n-gram table kept in the external sidecar so it never has to be memory-resident. The main GGUF includes the 48 trunk layers and one **embedded MTP block**. This is the only model version distributed here: use the same file for ordinary decoding or enable speculative decoding with `--mtp`. The external PLE table is required in both cases.
- wavlm-base-plus-sv↗
Microsoft
The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli
- wav2vec2-xls-r-300m-bengali↗
arijitx
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the OPENSLR_SLR53 - bengali dataset. It achieves the following results on the evaluation set. With 5 gram language model trained on 30M sentences randomly chosen from AI4Bharat IndicCorp dataset : - WER: 0.15322879016421437 - CER: 0.03413696666806267 Note : 5% of a total 10935 samples have been used for evaluation. Evaluation set has 10935 examples which was not part of training training was done on first 95% and eval was done on last 5%. Training was stopped after 180k steps. Output predictions are available under files section.
- bert-base-spanish-wwm-uncased↗
dccuchile
BETO is a BERT model trained on a big Spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks comparing BETO with Multilingual BERT as well as other (not BERT-based) models. All models use a vocabulary of about 31k BPE subwords constructed using SentencePiece and were trained for 2M steps. The following table shows some BETO results in the Spanish version of every task. We compare BETO (cased and uncased) with the Best Multilingual BERT results that we found in the literature (as of October 2019). The table also shows some alternative methods for the same tasks (not necessarily BERT-based methods). References for all methods can be found here.
- sam2-hiera-small↗
facebook
Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the SAM 2 paper for more information. SAM2 can be used for automatic mask generation to segment all objects in an image using the `mask-generation` pipeline: SAM2's key strength is its ability to track objects across video frames. Here's how to use it for video segmentation:
- sfm_unfiltered_e2e_misalignment_upsampled_base↗
geodesic-research
- ThinkingCap-Qwen3.8-27B-abliterated-NVFP4A16↗
IstroSec
A 4-bit weight-only quantization (NVFP4 weights, bf16 activations) of ThinkingCap-Qwen3.8-27B-abliterated, the uncensored variant of bottlecapai/ThinkingCap-Qwen3.8-27B. 20.6 GB, down from 55.6 GB in bf16, at KL 0.0155 against the bf16 model. The weights fit a 24 GB GPU with a little room for KV cache; 32 GB and up is comfortable. vLLM serves it on Ampere and newer (sm80+) with a weight-only kernel: Marlin FP4 on most GPUs (Ampere, Ada, Hopper, RTX 50, DGX Spark) and a dedicated BF16×FP4 kernel on B200/B300. Activations stay bf16, so no GPU computes in FP4 here. **Which build?** The W4A16 build (19.5 GB, KL 0.0260) runs on the same GPUs (vLLM: Marlin, Machete on Hopper) and is 1.1 GB smaller; this build has the lower KL. Speed was not measured for either. With memory to spare, the FP8-BLOCK build (31.3 GB, KL 0.0046) has the lowest KL of the published builds; on Ada GPUs use FP8-DYNAMIC (31.3 GB, KL 0.0068) instead.
- Qwen-Image-Edit-GGUF↗
QuantStack
This GGUF file is a direct conversion of Qwen/Qwen-Image-Edit Type | Name | Location | Download | ------------ | -------------------------------------------------- | --------------------------------- | ------------------------- | Main Model | Qwen-Image | `ComfyUI/models/unet` | GGUF (this repo) | Main Text Encoder | Qwen2.5-VL-7B | `ComfyUI/models/text_encoders` | Safetensors / GGUF | | Text_Encoder (mmproj) | Qwen2.5-VL-7B-Instruct-mmproj-BF16 | `ComfyUI/models/text_encoders` (same folder as your main text encoder) | GGUF (this repo) | VAE | Qwen-Image VAE | `ComfyUI/models/vae` | Safetensors (this repo) | Since this is a quantized model, all original licensing terms and usage restrictions remain in effect.
- IndicBERTv2-MLM-only↗
ai4bharat
A multilingual language model trained on IndicCorp v2 and evaluated on IndicXTREME benchmark. The model has 278M parameters and is available in 23 Indic languages and English. The models are trained with various objectives and datasets. The list of models are as follows: - IndicBERT-MLM [Model] - A vanilla BERT style model trained on IndicCorp v2 with the MLM objective - +Samanantar [Model] - TLM as an additional objective with Samanantar Parallel Corpus [Paper] | [Dataset] - +Back-Translation [Model] - TLM as an additional objective by translating the Indic parts of IndicCorp v2 dataset into English w/ IndicTrans model [Model] - IndicBERT-SS [Model] - To encourage better lexical sharing among languages we convert the scripts from Indic languages to Devanagari and train a BERT style model with the MLM objective Fine-tuning scripts are based on transformers library. Create a new conda environment and set it up as follows: