CosyVoice-300M
FunAudioLLM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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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 17
CosyVoice-300M-Instruct
FunAudioLLM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。
基础资料已收录 · 缺失资料后续补充
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查看详情 ↗CosyVoice-300M-SFT
FunAudioLLM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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查看详情 ↗CosyVoice2-0.5B
FunAudioLLM**Fun-CosyVoice 3.0** is an advanced text-to-speech (TTS) system based on large language models (LLM), surpassing its predecessor (CosyVoice 2.0) in content consistency, speaker similarity, and prosody naturalness. It is designed for zero-shot multilingual speech synthesis in the wild. - **Language Coverage**: Covers 9 common languages (Chinese, English, Japanese, Korean, German, Spanish, French, Italian, Russian), 18+ Chinese dialects/accents (Guangdong, Minnan, Sichuan, Dongbei, Shan3xi, Shan1xi, Shanghai, Tianjin, Shandong, Ningxia, Gansu, etc.) and meanwhile supports both multi-lingual/cross-lingual zero-shot voice cloning. - **Content Consistency & Naturalness**: Achieves state-of-the-art performance in content consistency, speaker similarity, and prosody naturalness. - **Pronunciation Inpainting**: Supports pronunciation inpainting of Chinese Pinyin and English CMU phonemes, providing more controllability and thus suitable for production use. - **Text Normalization**: Supports reading of numbers, special symbols and various text formats without a traditional frontend module. - **Bi-Streaming**: Support both text-in streaming and audio-out streaming, and achieves latency as low as 150ms while maintaining high-quality audio output. - **Instruct Support**: Supports various instructions such as languages, dialects, emotions, speed, volume, etc. Optionally, you can unzip `ttsfrd` resource and install `ttsfrd` package for better text normalization performance. Notice that this step is not necessary. If you do not install `ttsfrd` package, we will use wetext by default.
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查看详情 ↗fsmn-vad-GGUF
FunAudioLLM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。
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗Fun-ASR-MLT-Nano-2512
FunAudioLLMThis model 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): Fun-ASR-MLT-Nano-2512 is the 800M-parameter multilingual checkpoint in the Fun-ASR family. It is trained on hundreds of thousands of hours of multilingual speech and supports 31 languages, with emphasis on East and Southeast Asian languages. For the Chinese, English, and Japanese checkpoint trained on tens of millions of hours, use Fun-ASR-Nano-2512. **This repository hosts the MLT checkpoint.** Its supported-language scope differs from the standard Fun-ASR-Nano checkpoint.
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查看详情 ↗Fun-ASR-Nano-2512
FunAudioLLMThis model 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 Fun-ASR-Nano as a single self-contained binary via **llama.cpp / GGUF** (like whisper.cpp), with built-in VAD. Prebuilt binaries + one-command model download → Fun-ASR-Nano-GGUF · runtime · guide Fun-ASR is an end-to-end speech recognition large model launched by Tongyi Lab. It is trained on tens of millions of hours of real speech data, possessing powerful contextual understanding capabilities and industry adaptability. It supports low-latency real-time transcription and covers 31 languages. It excels in vertical domains such as education and finance, accurately recognizing professional terminology and industry expressions, effectively addressing challenges like "hallucination" generation and language confusion, achieving "clear hearing, understanding meaning, and accurate writing."
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查看详情 ↗Fun-ASR-Nano-2512-GGUF
FunAudioLLMStandalone audio.cpp GGUF builds of FunAudioLLM/Fun-ASR-Nano-2512-hf. Each file embeds the model configuration, processor configuration, tokenizer, chat template, and the audio.cpp model package specification. The source checkpoint is pinned to revision `854d88f94205cd17d2afdb24332130d86fbe654a`. The source `model.safetensors` SHA256 is `335ca3e74917f1156690400e2c344350112950165789cf78ce3d0a367affd821`. Fun-ASR-Nano currently provides offline multilingual ASR. It does not expose streaming or timestamp output. On CUDA, audio.cpp keeps the Q8_0 encoder and adaptor weights native and loads decoder weights as BF16 by default for stable logits. An explicit `fun_asr_nano.decoder_weight_type` session option overrides that default.
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查看详情 ↗Fun-ASR-Nano-2512-hf
FunAudioLLMThis is the Hugging Face Transformers-compatible version of Fun-ASR-Nano-2512. Fun-ASR-Nano is an end-to-end speech recognition model by FunAudioLLM, trained on tens of millions of hours of real speech data. This checkpoint supports Chinese, English, and Japanese; its Chinese coverage includes 7 dialect groups and 26 regional accents. For 31-language recognition, use the separate Fun-ASR-MLT-Nano-2512 checkpoint. Start with the released **Transformers 5.17.0** package and this native `-hf` checkpoint. A repository clone, custom model code and `trust_remote_code=True` are not required for this path. The original toolkit checkpoint and the vLLM/GGUF artifacts are different formats, not interchangeable model names.
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查看详情 ↗Fun-ASR-Nano-2512-vllm
FunAudioLLMThis 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`.
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查看详情 ↗Fun-ASR-Nano-GGUF
FunAudioLLMGGUF 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:
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查看详情 ↗Fun-Audio-Chat-8B
FunAudioLLM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。
基础资料已收录 · 缺失资料后续补充
输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗Fun-CosyVoice3-0.5B-2512
FunAudioLLM**Fun-CosyVoice 3.0**: Demos; Paper; Modelscope; Huggingface; CV3-Eval **CosyVoice 2.0**: Demos; Paper; Modelscope; HuggingFace **CosyVoice 1.0**: Demos; Paper; Modelscope; HuggingFace
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查看详情 ↗Paraformer-GGUF
FunAudioLLM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。
基础资料已收录 · 缺失资料后续补充
输入 音频→ 输出 文本
价格待核验多模态与渠道费用须分别核对
查看详情 ↗SenseVoiceSmall
FunAudioLLMSenseVoice 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
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查看详情 ↗SenseVoiceSmall-GGUF
FunAudioLLMGGUF 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.
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查看详情 ↗SenseVoiceSmall-GGUF-audiocpp
FunAudioLLMThis 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`.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。