spark-tts-normazlied-masri-mega
MohamedGomaa30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。
Basic profile indexed · More details to follow
From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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
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。
Basic profile indexed · More details to follow
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。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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。
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. 👑🏺
**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.
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。
**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.
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。
Basic profile indexed · More details to follow
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。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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。
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.
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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。
Basic profile indexed · More details to follow
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。
Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.