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This is the official vLLM-native packaging of `FunAudioLLM/Fun-ASR-Nano-2512`. It preserves the official checkpoint tensors and adds the layout required by vLLM's `FunASRForConditionalGeneration` implementation. This repository does not define a new model and does not add LoRA weights. The 1,261 tensors in `model.safetensors` are bitwise equal to the tensors in the official source `model.pt` at revision `272c57b82523ada6fd87095e955f8e29100979ab`. The complete machine-readable record is in `MODEL_PROVENANCE.json`. The conversion can be reproduced with `convert_from_official.py`.

Speech recognition
Input Audio→ Output Text
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GGUF build of **Fun-ASR-Nano** (SenseVoice SAN-M encoder + adaptor + **Qwen3-0.6B** LLM decoder) for the zero-Python, CPU/edge **FunASR llama.cpp runtime** — the accuracy leader (LLM decoder), single C++ binary. The Fun-ASR-Nano LLM (Qwen3-0.6B) ships in three tiers — all within 0.1% CER (184-file micro-CER). Pair any with `funasr-encoder-f16.gguf` (470 MB). These are GGUF weights for the **FunASR llama.cpp runtime** — a whisper.cpp-style, single self-contained binary for CPU / edge. Grab a prebuilt binary, then fetch this model and run:

Speech recognition
Input Audio→ Output Text
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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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Category pending
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**Fun-CosyVoice 3.0**: Demos; Paper; Modelscope; Huggingface; CV3-Eval **CosyVoice 2.0**: Demos; Paper; Modelscope; HuggingFace **CosyVoice 1.0**: Demos; Paper; Modelscope; HuggingFace

Speech synthesis
Input Text→ Output Audio
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SenseVoiceSmall

FunAudioLLM

SenseVoice is part of the **FunASR** ecosystem — one industrial-grade open-source toolkit for **ASR · VAD · punctuation · speaker diarization · emotion / event · LLM-ASR**. A Star really helps the project (and keeps you updated): > ⚡ **CPU / edge — no GPU, no Python:** run SenseVoiceSmall as a single self-contained binary via **llama.cpp / GGUF** (like whisper.cpp), with built-in VAD. Prebuilt binaries + one-command model download → SenseVoiceSmall-GGUF · runtime · guide github repo : https://github.com/FunAudioLLM/SenseVoice

Speech recognition
Input Audio→ Output Text
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GGUF build of **SenseVoiceSmall** (SAN-M encoder + CTC) for the zero-Python, CPU/edge **FunASR llama.cpp runtime** — multilingual ASR with language / emotion / event tags, **~20× real-time on CPU**. These are GGUF weights for the **FunASR llama.cpp runtime** — a whisper.cpp-style, single self-contained binary for CPU / edge. Grab a prebuilt binary, then fetch this model and run: The binary prints **transcription text** directly (no Python detok). `--ids` for raw ids / `--keep-tags` for the lang/emotion tags.

Speech recognition
Input Audio→ Output Text
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This repository contains a self-contained Q8_0 export of SenseVoiceSmall for the audio.cpp spec-backed runtime. The GGUF embeds the `sense_asr` schema-v1 model specification, SenseVoice metadata, SentencePiece vocabulary, CMVN tensors, and 919 model tensors. It loads directly without `--model-spec-override`. The integration is tracked in audio.cpp pull request #218. The model was exported from `FunAudioLLM/SenseVoiceSmall` revision `3847d57b6bdf2dd8875cb1508d2af43d80a16bf7` with the official `runtime/llama.cpp/export_sensevoice_gguf.py` exporter using `--wtype q8_0` and `--model-spec`.

Speech recognition
Input Audio→ Output Text
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small

funnel-transformer

Pretrained model on English language using a similar objective objective as ELECTRA. It was introduced in this paper and first released in this repository. This model is uncased: it does not make a difference between english and English. Disclaimer: The team releasing Funnel Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. Funnel Transformer is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.

Embeddings & similarity
Input Text→ Output Embedding
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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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Category pending
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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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Category pending
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