اضبط الصوت حسب مثال — Match Audio Sound
🎛
Match your track's sound to a reference example
Murtadha hamid PRO
murtadha9
AI & ML interests
Building applied AI systems: models, data pipelines, and monitoring & analysis tooling across text, speech, and vision — with a focus on Arabic and the Iraqi dialect
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updated a Space about 1 hour ago
murtadha9/match-audio-sound published a Space about 1 hour ago
murtadha9/match-audio-sound updated a Space about 17 hours ago
murtadha9/edit-video-by-typingOrganizations
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Post
47
🎬 ارفع فيديو إنجليزياً.. واخذه مدبلجاً بالعربية الفصحى جاهزاً
الأداة تتولى عنك:
📝 تفريغ الكلام بتوقيته
🌐 ترجمة إلى الفصحى
لشاشة
لقناتك وصف🎙️ نطق بصوت عربي
⏱️ ضبط الصوت على التوقيت
💬 ترجمة عربية على
احتك وطلابك.. محتوى إنجليزي يصل إلى جمهور عربي بدل أن تفرّغ وتترجم وتسجّل صوتك وتضبط التوقيت بيدك
مجاني ويعمل من المتصفح ولا يحفظ ملفاتك.
🔗 https://huggingface.co/spaces/murtadha9/dub-video-to
-arabic
الأداة تتولى عنك:
📝 تفريغ الكلام بتوقيته
🌐 ترجمة إلى الفصحى
لشاشة
لقناتك وصف🎙️ نطق بصوت عربي
⏱️ ضبط الصوت على التوقيت
💬 ترجمة عربية على
احتك وطلابك.. محتوى إنجليزي يصل إلى جمهور عربي بدل أن تفرّغ وتترجم وتسجّل صوتك وتضبط التوقيت بيدك
مجاني ويعمل من المتصفح ولا يحفظ ملفاتك.
🔗 https://huggingface.co/spaces/murtadha9/dub-video-to
-arabic
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reacted to sergiopaniego's post with 🔥 3 days ago
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1,112
if you want to deep dive into how the new @MistralAI Large 4 was built, we sat down with the team to learn straight from them!
scaling, specialization, evals, cyber, sandboxing and why open weights matter
a really detailed look at what it takes to build and release an open frontier model, super recommended
https://www.youtube.com/watch?v=FczbSauVvjw
scaling, specialization, evals, cyber, sandboxing and why open weights matter
a really detailed look at what it takes to build and release an open frontier model, super recommended
https://www.youtube.com/watch?v=FczbSauVvjw
reacted to their post with 🔥 4 days ago
Post
73
Glyph Lens
Instead of retyping printed text from a photo or scanned page word by word, you get it back in Arabic or English, ready to copy or download. It also highlights the words it may have misread right on the image, so you only need to check those. Free, runs in the browser, stores nothing.
🔗 murtadha9/glyph-lens
Instead of retyping printed text from a photo or scanned page word by word, you get it back in Arabic or English, ready to copy or download. It also highlights the words it may have misread right on the image, so you only need to check those. Free, runs in the browser, stores nothing.
🔗 murtadha9/glyph-lens
reacted to savoji's post with 🔥 4 days ago
Post
1,815
One Qwen3-VL-8B checkpoint improves all three streaming spatial benchmarks at once: OST-Bench 50.52 to 63.93 (+13.41), UCS-Bench +7.44, OVO-S-Bench +4.91. The change is in the supervision, with no new memory module or annotation.
The ChronoReplay paper calls the failure mode interface lock-in: post-train on one answer format and that readout improves while others stall. A categorical-only control hits 58.03 on OVO-S but drops OST from 50.52 to 47.48.
🔍 The recipe
• Keep targets in native formats (option, metric value with units, timestamp)
• Replay episodes in source order, each target attached to its causal prefix
• Loss mass: 2/3 on 33,180 single-turn examples, 1/3 on 6,381 episodes (alpha = 0.5)
📊 The numbers
• Three-benchmark mean 56.86, against 50.74 (categorical-only) and 50.76 (trajectory-only)
• Chronological order beats a shifted-order control by about 2.41 OST points on two seeds; the 13.41 belongs to the whole recipe
• Adaptive frame selection adds +4.09 on OVO-S (57.98) with 80.50 frames instead of 128
⚠️ Caveats
• One backbone, indoor scenes only; the single-family comparison is confounded by data coverage
• The repo has a CPU smoke test and a --dry-run training command, but no license and no fine-tuned weights
ChronoReplay: One Answer Interface Is Not Enough for Streaming Spatial Intelligence (2610.34283)
https://arxiv.org/abs/2610.34283
https://github.com/yihaohu0118/ChronoReplay
If you fine-tune VLMs for embodied work, do you score categorical, metric and temporal readouts together?
📚 Read it elsewhere
• The deep dive on Medium: https://medium.com/p/8d2fb1bf832d
• The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/chronoreplay-lifts-qwen3-vl-8b-by
• The thread on X: https://x.com/saavoji/status/2116229682844877156
• Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#chronoreplay
The ChronoReplay paper calls the failure mode interface lock-in: post-train on one answer format and that readout improves while others stall. A categorical-only control hits 58.03 on OVO-S but drops OST from 50.52 to 47.48.
🔍 The recipe
• Keep targets in native formats (option, metric value with units, timestamp)
• Replay episodes in source order, each target attached to its causal prefix
• Loss mass: 2/3 on 33,180 single-turn examples, 1/3 on 6,381 episodes (alpha = 0.5)
📊 The numbers
• Three-benchmark mean 56.86, against 50.74 (categorical-only) and 50.76 (trajectory-only)
• Chronological order beats a shifted-order control by about 2.41 OST points on two seeds; the 13.41 belongs to the whole recipe
• Adaptive frame selection adds +4.09 on OVO-S (57.98) with 80.50 frames instead of 128
⚠️ Caveats
• One backbone, indoor scenes only; the single-family comparison is confounded by data coverage
• The repo has a CPU smoke test and a --dry-run training command, but no license and no fine-tuned weights
ChronoReplay: One Answer Interface Is Not Enough for Streaming Spatial Intelligence (2610.34283)
https://arxiv.org/abs/2610.34283
https://github.com/yihaohu0118/ChronoReplay
If you fine-tune VLMs for embodied work, do you score categorical, metric and temporal readouts together?
📚 Read it elsewhere
• The deep dive on Medium: https://medium.com/p/8d2fb1bf832d
• The long read on Substack: https://sajjadpakdamansavoji.substack.com/p/chronoreplay-lifts-qwen3-vl-8b-by
• The thread on X: https://x.com/saavoji/status/2116229682844877156
• Every version on sajjadpsavoji.github.io: https://sajjadpsavoji.github.io/blog#chronoreplay
- 2 replies
reacted to crp4222's post with 🔥 4 days ago
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2,730
I trained a small PII detector for PrivAiTe, my local proxy that masks personal data before a prompt reaches an LLM: crp4222/privaite-pii-minilm.
It is a multilingual MiniLM (117M parameters, 15 types from names and contacts to IDs, bank details and secrets), trained only on datasets whose licenses allow commercial use, released under BSD 3-Clause, with PyTorch, ONNX fp32 and int8, and Core ML builds.
On 9 public test sets it never saw, it masks as many person names as the best of GLiNER PII, NVIDIA GLiNER-PII, GLiNER2, Piiranha and OpenAI privacy-filter, or more, on 6 of them, usually with higher precision. It trails GLiNER PII on short lowercase voice requests and NVIDIA on court rulings.
It is also 2.5 to 7 times smaller and much faster: 9 ms per 1,000 characters on an M1 Pro Neural Engine and 23 ms on CPU in int8, against 186 to 621 ms for the others on the same machine and documents. Numbers, test sets and limits are on the card: crp4222/privaite-pii-minilm
It is a multilingual MiniLM (117M parameters, 15 types from names and contacts to IDs, bank details and secrets), trained only on datasets whose licenses allow commercial use, released under BSD 3-Clause, with PyTorch, ONNX fp32 and int8, and Core ML builds.
On 9 public test sets it never saw, it masks as many person names as the best of GLiNER PII, NVIDIA GLiNER-PII, GLiNER2, Piiranha and OpenAI privacy-filter, or more, on 6 of them, usually with higher precision. It trails GLiNER PII on short lowercase voice requests and NVIDIA on court rulings.
It is also 2.5 to 7 times smaller and much faster: 9 ms per 1,000 characters on an M1 Pro Neural Engine and 23 ms on CPU in int8, against 186 to 621 ms for the others on the same machine and documents. Numbers, test sets and limits are on the card: crp4222/privaite-pii-minilm
- 5 replies
reacted to javadtaghia's post with 🔥 4 days ago
