ニュースとサイト内検索
ニュース、ツール、モデル、ガイド、特集、ワークフロー、プロンプトを関連付けて検索。
AI モデル 97
“Transcripti”
Voxtral Small 24B 2507
Voxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Input audio...
Voxtral-Mini-3B-2507
Voxtral Mini is an enhancement of Ministral 3B, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Learn more about Voxtral in our blog post here and our research paper. Voxtral builds upon Ministral-3B with powerful audio understanding capabilities. - **Dedicated transcription mode**: Voxtral can operate in a pure speech transcription mode to maximize performance. By default, Voxtral automatically predicts the source audio language and transcribes the text accordingly - **Long-form context**: With a 32k token context length, Voxtral handles audios up to 30 minutes for transcription, or 40 minutes for understanding - **Built-in Q&A and summarization**: Supports asking questions directly through audio. Analyze audio and generate structured summaries without the need for separate ASR and language models - **Natively multilingual**: Automatic language detection and state-of-the-art performance in the world’s most widely used languages (English, Spanish, French, Portuguese, Hindi, German, Dutch, Italian) - **Function-calling straight from voice**: Enables direct triggering of backend functions, workflows, or API calls based on spoken user intents - **Highly capable at text**: Retains the text understanding capabilities of its language model backbone, Ministral-3B
whisper-large-v3-gguf
GGUF conversions of openai/whisper-large-v3 for use with transcribe.cpp. Ported from upstream commit 06f233f, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper large-v3 — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). v3 family adds Cantonese (yue) and uses a 128-bin mel input. Encoder-decoder transformer; 30-second windows with chunked long-form decoding.
whisper-large-v3
Language detection capability of this model has been degraded during training - it is intended for mostly-hebrew audio transcription. Language token should be explicitly set to Hebrew. Additionally, the tanslation task was not trained and also degraded. This model would not be able to translate in any reasonable capacity. Please follow the original model card for usage details - replacing with this model name. You can also fine other weight formats ad quantizations on the ivrit ai HF page.
moss-transcribe-diarize
**Joint transcription and speaker diarization in a single 0.9B-parameter model — know who said what, and when** Native speech-to-text in the **OpenASR** runtime — engineered for peak performance on CPU & GPU, **no Python at inference time**. RTF = real-time factor on the fixed 11s JFK clip (**lower is faster**); RAM peak measured per pack in an isolated subprocess. JFK ΔWER compares each quantized build's JFK transcript to this model's fp16 JFK transcript, so it measures quantization drift rather than absolute recognition accuracy. **q8_0** is the recommended default — near-reference quality at a fraction of the footprint.
Audio8-ASR-Infinite
**Audio8 ASR Infinite** is a native streaming speech recognition model built to be as responsive as possible. It offers a selectable audio clock (80/120/160 ms) and a transcription delay (240–560 ms). With our adapted vLLM build it transcribes unlimited-length audio **24/7** without drifting. - **Super responsive** — the native streaming architecture decodes 12.5 times per second. - **Unlimited-length transcription** — a rolling KV Cache keeps both **memory and latency constant**, even in **24/7 operation**. - **Selectable streaming clock** — one text token per clock step (12.5 / 8.3 / 6.25 decisions per second), balancing perception granularity and resource cost. - **Configurable transcription delay** — set how much delay to trade for accuracy. - **Semantic VAD** — distinguishes thinking pauses, stuttering and real end of turn, where traditional acoustic VAD usually fails. - **Bilingual** — Chinese and English. The checkpoint has a native context of 30 seconds. But with Rolling KV Cache, it can transcribe 24/7 nonstop.
muscriptor-large
**MuScriptor** is an open-weight model for **general-purpose, multi-instrument automatic music transcription (AMT)**: it converts a music recording (any genre, multiple simultaneous instruments) into a stream of notes played. This repository hosts the **large** variant (≈1.3B parameters) — the **flagship, best-quality checkpoint**. For a smaller footprint use `muscriptor-medium` (≈300M, good trade-off) or `muscriptor-small` (≈100M, fastest). `load_model` accepts a size keyword (`"small"`/`"medium"`/`"large"`), a local `.safetensors` path, or an `hf://` / `https://` URL. Weights loaded by size keyword (or any `hf://` URL) are cached in the standard Hugging Face cache (`~/.cache/huggingface/hub`, configurable via `HF_HOME`); weights fetched from a plain `http(s)://` URL are cached under `~/.cache/muscriptor/`. Input audio can be WAV or any format `libsndfile` reads (mp3, flac, ogg, m4a, …); it is resampled to 16 kHz mono internally.
Geneformer
Geneformer is a foundational transformer model pretrained on a large-scale corpus of human single cell transcriptomes to enable context-aware predictions in settings with limited data in network biology. Geneformer is a foundational transformer model pretrained on a large-scale corpus of single cell transcriptomes representing a broad range of human tissues. Geneformer V1 was originally pretrained in June 2021 on Genecorpus-30M, a corpus comprised of ~30 million human single cell transcriptomes. We excluded cells with high mutational burdens (e.g. malignant cells and immortalized cell lines) that could lead to substantial network rewiring without companion genome sequencing to facilitate interpretation. The current updated Geneformer V2 is pretrained on ~104 million human single cell transcriptomes (non-cancer) from Genecorpus-104M. The cancer continual learning V2 variant was continually pretrained on ~14 million cancer transcriptomes to yield a cancer domain-tuned model. Each single cell’s transcriptome is presented to the model as a rank value encoding where genes are ranked by their expression in that cell scaled by their expression across the entire Genecorpus (~30M for V1, ~104M for V2). The rank value encoding provides a nonparametric representation of that cell’s transcriptome and takes advantage of the many observations of each gene’s expression across the pretraining corpus to prioritize genes that distinguish cell state. Specifically, this method will deprioritize ubiquitously highly-expressed housekeeping genes by scaling them to a lower rank. Conversely, genes such as transcription factors that may be lowly expressed when they are expressed but highly distinguish cell state will move to a higher rank within the encoding. Furthermore, this rank-based approach may be more robust against technical artifacts that may systematically bias the absolute transcript counts value while the overall relative ranking of genes within each cell remains more stable.
whisper-large-v2-gguf
GGUF conversions of openai/whisper-large-v2 for use with transcribe.cpp. Ported from upstream commit ae46427, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper large-v2 — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). Encoder-decoder transformer; 30-second windows with chunked long-form decoding.
whisper-base-gguf
Ported from upstream commit e37978b, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper base — converted to GGUF for transcribe.cpp. Multilingual transcription, language detection, and speech translation (audio in any supported language → English text). Encoder-decoder transformer; 30-second windows with chunked long-form decoding. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.