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This model is a fine-tuned version of NVIDIA's Parakeet CTC 1.1B for **Persian (Farsi)** Automatic Speech Recognition (ASR). This repository includes the unpacked tokenizer files (`tokenizer.json`, `vocab.json`, etc.) compatible with Hugging Face Transformers. You can use these files to: - Continue fine-tuning the model - Reuse the tokenizer in other frameworks or experiments

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语音识别
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
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ParsVoice-XTTS

MohammadJRanjbar

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

分类待补充
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QwenCleo-ASR

mohammedaly22

The best open-source model for Egyptian Arabic & code-switching speech recognition Built on Qwen3-ASR-1.7B , fine-tuned for Egyptian dialect and Arabic ↔ English code-switching. QwenCleo — the name carries three meanings: Qwen , the powerful base model it is built on; Queen , signalling a model that reigns over its domain; and Cleo , for Cleopatra , the queen of Egypt — because this model is tailored for Egyptian Arabic. 👑🏺

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语音识别
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QwenCleo-ASR-GGUF

mohammedaly22

**Egyptian Arabic & code-switching speech recognition for audio.cpp: 3–4× faster than PyTorch on the same GPU, no Python** These are standalone GGUF packages of QwenCleo-ASR, a Qwen3-ASR-1.7B fine-tune for Egyptian Arabic and Arabic↔English code-switching (`فبتديك كل ال soft skills بشكل indirect`). On the author's 3,699-utterance Egyptian test set it reaches **19.85% WER, against 41.51% for base Qwen3-ASR-1.7B**. Full results are on the original model card. In audio.cpp it runs through the existing **`qwen3_asr`** family, the same C++ runtime and options as Qwen3-ASR: offline and streaming modes, long-audio chunking, word timestamps with Qwen3 Forced Aligner, and the OpenAI-compatible server.

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语音识别
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VoiceTut-TTS-GGUF

mohammedaly22

**Egyptian Arabic & code-switching TTS for audio.cpp: up to 6.6× faster than the PyTorch package on the same GPU, no Python** These are standalone GGUF packages of VoiceTut-TTS, an OmniVoice fine-tune trained on ~380 hours of Egyptian podcast speech. It speaks natural Egyptian Arabic, switches between Arabic and English mid-sentence (`عندي meeting بكرة`), and clones voices zero-shot. VoiceTut has exactly the same architecture as OmniVoice, so it runs through audio.cpp's existing **`omnivoice`** family with every OmniVoice route: voice cloning, voice design, streaming, the long-text chunker, and the server.

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语音合成
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imajev-2b

mohit67890

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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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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A text-to-image model in one file of 1,208 MB that paints a 512 × 512 picture in one, two or four steps, in a web browser, on WebGPU, with no server: try it in this model's Space or on the mindview site, in a desktop browser with WebGPU. On a MacBook Air (M4) in Chrome a picture takes about 4 s in one step, 6 s in two and 15 s in four. It has 4.61 billion weights, and all but 165 million of them are −1, 0 or +1. It is mindview-t2i with everything added for speed: three side branches trained on this ternary painter, for one, two and four steps, and a 2-step schedule whose first step sketches at a quarter of the size. Each branch is trained to paint like a full-precision teacher: EPFL's 1-step fine-tune of FLUX.2 [klein] 4B for one step, klein with a 2-step LoRA for two, and klein itself for four. It came out of mindview, an art installation that shows these models computing. Most of it is the work of others (see Sources and licenses). Times are for a first picture on a cool machine, from the prompt to the pixels. The Air has no fan and slows by about a third after minutes of painting.

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图像生成
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COST EXPLORER

好模型，也要算好成本。

费用分析

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

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

选型与常见问题
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。

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