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AIモデルを探す 84956
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詳細を見る ↗Qwenmodel1st
atypical-chai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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詳細を見る ↗araelectra-base-discriminator
aubmindlab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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詳細を見る ↗aragpt2-base
aubmindlabYou can find more information in our paper AraGPT2 The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API. GPT2-base and medium uses the code from the `gpt2` folder and can trains models from the minimaxir/gpt-2-simple repository. These models were trained using the `lamb` optimizer and follow the same architecture as `gpt2` and are fully compatible with the `transformers` library.
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詳細を見る ↗aragpt2-medium
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詳細を見る ↗bert-base-arabert
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詳細を見る ↗bert-base-arabertv02
aubmindlab**AraBERT** is an Arabic pretrained language model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup There are two versions of the model, AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were split using the Farasa Segmenter. We evaluate AraBERT models on different downstream tasks and compare them to mBERT), and other state of the art models (*To the extent of our knowledge*). The Tasks were Sentiment Analysis on 6 different datasets (HARD, ASTD-Balanced, ArsenTD-Lev, LABR), Named Entity Recognition with the ANERcorp, and Arabic Question Answering on Arabic-SQuAD and ARCD
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詳細を見る ↗bert-base-arabertv02-twitter
aubmindlabAraBERTv0.2-Twitter-base/large are two new models for Arabic dialects and tweets, trained by continuing the pre-training using the MLM task on ~60M Arabic tweets (filtered from a collection on 100M). The two new models have had emojies added to their vocabulary in addition to common words that weren't at first present. The pre-training was done with a max sentence length of 64 only for 1 epoch. **AraBERT** is an Arabic pretrained language model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup
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詳細を見る ↗bert-base-arabertv2
aubmindlab**AraBERT** is an Arabic pretrained lanaguage model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup There are two versions of the model, AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were splitted using the Farasa Segmenter. We evalaute AraBERT models on different downstream tasks and compare them to mBERT), and other state of the art models (*To the extent of our knowledge*). The Tasks were Sentiment Analysis on 6 different datasets (HARD, ASTD-Balanced, ArsenTD-Lev, LABR), Named Entity Recognition with the ANERcorp, and Arabic Question Answering on Arabic-SQuAD and ARCD
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詳細を見る ↗bert-large-arabertv02
aubmindlab**AraBERT** is an Arabic pretrained lanaguage model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup There are two versions of the model, AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were splitted using the Farasa Segmenter. We evalaute AraBERT models on different downstream tasks and compare them to mBERT), and other state of the art models (*To the extent of our knowledge*). The Tasks were Sentiment Analysis on 6 different datasets (HARD, ASTD-Balanced, ArsenTD-Lev, LABR), Named Entity Recognition with the ANERcorp, and Arabic Question Answering on Arabic-SQuAD and ARCD
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詳細を見る ↗bert-large-arabertv02-twitter
aubmindlab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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詳細を見る ↗bert-large-arabertv2
aubmindlab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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詳細を見る ↗Audar-ASR-V1-Flash
audarai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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詳細を見る ↗Audar-ASR-V1-Turbo
audarai🧭 Overview · 📊 Benchmarks · 💻 GGUF Deploy · 🤗 Transformers · 🎙️ Streaming · ⚡ vLLM · 📄 Tech Report · 🐙 GitHub · ☁️ Audar API · 📜 License **Audar-ASR-V1-Turbo** is an **Arabic-first generative speech-recognition model** — the accuracy tier of the Audar-ASR family. It recasts transcription as **audio-conditioned next-token prediction** over a unified text vocabulary (a language-model decoder rather than a CTC or transducer objective), and is built on a permissively-licensed open-weight audio-LLM foundation and adapted in-house — the contribution is the adaptation (the data curriculum and the alignment rubric), not the foundation: The result is **state-of-the-art dialectal Arabic ASR** — the lowest average WER *and* CER of any evaluated system on the *Open Universal Arabic ASR Leaderboard*. It transcribes MSA and every major Arabic dialect, code-switched Arabic–English, and English, across **30 languages** in total.
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詳細を見る ↗Audar-TTS-V1-Flash
audarai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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詳細を見る ↗Audar-TTS-V1-Turbo
audaraiAudar-TTS-V1-Turbo is the **balanced production tier** of the Audar-TTS family — a **~1.64B-parameter** community-licensed speech model that trades a little of Flash's raw speed for higher fidelity and steadier prosody, while staying efficient. It is **Arabic-first** (including Gulf/Emirati and other dialects), fully **bilingual with English**, and **clones any voice from a 5–15 second reference clip** with no per-speaker fine-tuning. This repository ships it as **GGUF quantizations** for `llama.cpp`. Turbo treats speech synthesis as **next-token prediction**: a language-model backbone predicts discrete **audar-codec** acoustic tokens, which a lightweight neural codec decodes to **24 kHz** audio. There is **no phonemizer and no per-language G2P** — so Arabic dialects and Arabic⇄English code-switching are handled from data rather than brittle pronunciation rules. > **GGUF weights** under the **AudarAI Community License v1.0** — free for research and limited > commercial use by smaller organizations; larger/enterprise use needs an AudarAI Enterprise > License. See License.
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詳細を見る ↗Please note that this model is for research purpose only. A commercial license for a model that has been trained on much more data can be acquired with audEERING. The model expects a raw audio signal as input, and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust on MSP-Podcast (v1.7). The model was pruned from 24 to 12 transformer layers before fine-tuning. An ONNX export of the model is available from doi:10.5281/zenodo.6221127. Further details are given in the associated paper and tutorial.
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詳細を見る ↗The model expects a raw audio signal as input and outputs predictions for age in a range of approximately 0...1 (0...100 years) and gender expressing the probababilty for being child, female, or male. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust on aGender, Mozilla Common Voice, Timit and Voxceleb 2. For this version of the model we trained all 24 transformer layers. An ONNX export of the model is available from doi:10.5281/zenodo.7761387. Further details are given in the associated paper and tutorial.
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詳細を見る ↗The model expects a raw audio signal as input and outputs predictions for age in a range of approximately 0...1 (0...100 years) and gender expressing the probababilty for being child, female, or male. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning Wav2Vec2-Large-Robust on aGender, Mozilla Common Voice, Timit and Voxceleb 2. For this version of the model we only trained the first six transformer layers. An ONNX export of the model is available from doi:10.5281/zenodo.7761387. Further details are given in the associated paper and tutorial.
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詳細を見る ↗audio.cpp-gguf
audio-cppThis directory contains audio.cpp-native GGUF conversions of multiple speech models. These files are intended for use with audio.cpp. For conversion details, supported layouts, direct-file loading, sidecar embedding, and the latest compatibility notes, see the audio.cpp GGUF guide: The table lists the GGUF packages currently provided by this repository. License names follow audio.cpp's model license reference, which also records commercial-use conditions and other important notes.
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詳細を見る ↗Audio-Flamingo-GGUF
audio-cppThese self-contained GGUFs package the model tensors, tokenizer, configuration, and audio.cpp model spec for Audio Flamingo 3, Audio Flamingo Next, and Music Flamingo. AF3 and Next support transcription, captioning, and questions about speech, music, and environmental sounds. Music Flamingo specializes in music understanding; it produces text, not audio. Select a GGUF with `--model`. Use a question such as `Describe the instruments and tempo.` for general audio understanding. Requested timestamps or speaker descriptions are generated text, not structured alignment or diarization results. For server use, configure family `audio_flamingo`, task `asr`, and mode `offline`, then submit audio to `/v1/audio/transcriptions` with the instruction in `prompt`.
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詳細を見る ↗AuK-Base-and-Flash-GGUF
audio-cppThese GGUF files are for use with audio.cpp. The card's `base_model` points to the Tencent AuK base model. This package also includes optional files converted from AuK-Flash; Flash is a separate distilled variant and is not the pinned base model. > **Output quality:** We validate parity with upstream Python for the tested configuration, not whether AuK's output meets every quality expectation. If a result is disappointing, listen to the corresponding Python reference WAV linked below first. A similar Python result points to the upstream model's behavior, not necessarily an audio.cpp conversion issue. The quantized component combinations have not received the same 16-task parity validation. Pass the downloaded directory to `audiocpp_cli --model`. Keep the `config/` and `tokenizer/` directories alongside the GGUF files. The default components are `auk-base-f32.gguf`, `qwen2.5-omni-3b-bf16.gguf`, and `auk-vae-f32.gguf`. AuK-Flash requires `--session-option auk.variant=flash`; its default generator is `auk-flash-f32.gguf`. The Qwen component is Qwen2.5-Omni-3B.
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詳細を見る ↗Index-Echo-S2TT-GGUF
audio-cppGGUF packages for native speech-to-text translation with audio.cpp. Chinese speech produces timestamped source transcripts and English, Spanish, or Japanese translations. This port supports **S2TT only**; it does not generate translated speech. **There is room to improve 9B output parity and quantized quality**. Focused PRs with reproducible C++/Python comparisons are welcome. Note that 2B and 9B use the same C++ translation path, including the prompt, audio encoder, decoder implementation, and greedy sampler. 2B orig is parity safe, while 9B orig has drifts. 2B has 24 layers and tied token/output embeddings; 9B has 32 layers and a separate LM head. That can change how sensitive their outputs are to numerical differences, but I have not proved that it explains the 9B mismatch. These files were converted from the official 2B checkpoint at `72fd8bdc9bf8251f1c35f6d6289a950eda154395` and 9B checkpoint at `05f86cb7a38684916e193282b6cc02462ace4043`. Each GGUF embeds the audio tower, connector, text decoder, tokenizer, configuration, and audio.cpp model spec. The unused vision weights are not included.
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