Hey guys, need a tip. What works best for Amharic to English translation? Any good models that perform better than google translate?
There’s already drama that OpenAI trained their model from a solution from a researcher’s private codex session. Oh boy.
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I would say they’re overreacting, but I’m smart enough to know I’m dumb compared to them. This is happening a little too many times for my liking.
X
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Never supplement one part of your life with another (Financially). For example, don’t say “with the money I get from a car I rent out I will sustain a business I’m running”. If you have different hustles, isolate them. It’s called ring-fencing and I learned the hard way.
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Forwarded from Software Guy
YouTube
ተገናኙ!!! ገጠር ያለው ታዳጊ .... ሰለሞን ሙሉጌታ አዲሱ መፅሀፉን አርቴፊሻል ኢንተለጀንሲን በሰይፉ በኢቢኤስ | Seifu on EBS
AI ሁል ጊዜ ትክክል ነዉ አፍሪካ ምን ሊገጥማት ይችላል? ሰለሞን ሙሉጌታ አዲሱ መፅሀፉን አርቴፊሻል ኢንተለጀንሲን በሰይፉ በኢቢኤስ | Seifu on EBS
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አዝናኝ እና ቁምነገር አዘል ቪዲዮዎችን በየሳምንቱ ለመመልከት Seifu on EBS https://bit.ly/2VgLrdM Subscribe በማድረግ ደንበኛችን ይሁኑ!
በሰይፉ በኢቢኤስ…
Welcome to the home of Seifu on EBS!
አዝናኝ እና ቁምነገር አዘል ቪዲዮዎችን በየሳምንቱ ለመመልከት Seifu on EBS https://bit.ly/2VgLrdM Subscribe በማድረግ ደንበኛችን ይሁኑ!
በሰይፉ በኢቢኤስ…
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Ah, memories, I found an old hackathon entry for gebeya we had made with some friends (from almost ten years ago). https://devpost.com/software/compra?_gl=1*1exeaal*_gcl_au*MTQ5MDU4MTQzOS4xNzkwMjMxODY5*_ga*MjA1NTE3ODQzOC4xNzkwMjMxODcy*_ga_0YHJK3Y10M*czE3OTAyMzE4NzEkbzEkZzEkdDE3OTAyMzE5MTUkajE2JGwwJGgw
Devpost
compra
An alternative way of buying items by letting the user specify what he/she wants FIRST.
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Get the latest upstream master, branch out to work on a small fix that takes about 15 minutes, push branch, create pr, pr is 50 commits behind master and there are several conflicts.
Oh the joy
(Exaggerated true story)
Oh the joy
(Exaggerated true story)
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Forwarded from Addis AI
We open-sourced Addis Scribe Streaming, realtime speech recognition for Amharic.
It beats Google Chirp 3 on both accuracy and speed:
Word error rate: 28.4% vs 32.5%
First words on screen: 1.56s vs 6.67s
Final text after you stop talking: 30ms vs 1,411ms
On natural speech (WAXAL) we beat Google Chirp 3 in both streaming and batch mode. On FLEURS we went from 35.0% to 19.8% WER.
Hohe remains the most accurate offline Amharic model in our WAXAL test, and on FLEURS the two are within a point of each other (19.8% vs 20.6%). We built Addis Scribe for the live and edge inference side, where text has to appear while people are still talking.
Trained on 5,000+ hours of Amharic speech. Test sets were never used in training. Per-clip results and scripts are in the repo.
Try it😊 : https://docs.addisassistant.com/docs/playground/speech-to-text
API and SDK docs: docs.addisassistant.com
HuggingFace🤗: huggingface.co/addisai/addis-scribe-streaming
@addisassistantai
It beats Google Chirp 3 on both accuracy and speed:
Word error rate: 28.4% vs 32.5%
First words on screen: 1.56s vs 6.67s
Final text after you stop talking: 30ms vs 1,411ms
On natural speech (WAXAL) we beat Google Chirp 3 in both streaming and batch mode. On FLEURS we went from 35.0% to 19.8% WER.
Hohe remains the most accurate offline Amharic model in our WAXAL test, and on FLEURS the two are within a point of each other (19.8% vs 20.6%). We built Addis Scribe for the live and edge inference side, where text has to appear while people are still talking.
Trained on 5,000+ hours of Amharic speech. Test sets were never used in training. Per-clip results and scripts are in the repo.
Try it
API and SDK docs: docs.addisassistant.com
HuggingFace🤗: huggingface.co/addisai/addis-scribe-streaming
@addisassistantai
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