Does anyone know of some good open sourced projects for OCR? Tesseract always seems to be the default, and then it seems Google cloud, and other services are miles ahead. However, for those who don't want to rely on the big tech companies, are there any comparable alternatives?
Wav2vec Overview: Semi and Unsupervised Speech Recognition
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Re: Wav2vec Overview: Semi and Unsupervised Speech Recognition
#12One addendum to the linked post's notes: > SoTa in low-resource setting Libri-light by a lot on WER clean test 100h labeled: others ~4 vs theirs ~2.5 > SoTa on high-resource noisy data (3.3 vs 3.4) close to SoTa on clean data This note isn't super specific, but it's outdated if I'm understanding it correctly. To my understanding, the SOTA on this data is held by Conformer 1B (a 1 billion parameter model), at 1.4 clea…
I always wonder how people figure out these successful gigantic models if it takes hundreds of TPUs and days to train them.
I recently bought rtx 3090 in hopes of playing around with some computer vision applications but I guess having 24GB VRAM is nothing if I want to get something SOTA working.
Re: Wav2vec Overview: Semi and Unsupervised Speech Recognition
#13Does anyone know of some good open sourced projects for OCR? Tesseract always seems to be the default, and then it seems Google cloud, and other services are miles ahead. However, for those who don't want to rely on the big tech companies, are there any comparable alternatives?
There is easyocr which is good enough but lacks maturity (it was acknowledged at some point by Yann LeCun). The code base isn't ideal. I'm currently working on my own custom OCR since easyocr isn't perfect at detecting emails for example Www.ismaj@gmail ;com
Re: Wav2vec Overview: Semi and Unsupervised Speech Recognition
#14Does anyone know of some good open sourced projects for OCR? Tesseract always seems to be the default, and then it seems Google cloud, and other services are miles ahead. However, for those who don't want to rely on the big tech companies, are there any comparable alternatives?
I recently came across CRAFT wich appears to have come out of the ICDAR2017 Robust reading challenge.
It performed better than expected. I only tested a few images so please don't take my word for it.
That led me to PaddleOCR. There is still plenty of room for improvement but I found it way more convenient to use for my purposes than messing with Tesseract.
Re: Wav2vec Overview: Semi and Unsupervised Speech Recognition
#15One addendum to the linked post's notes: > SoTa in low-resource setting Libri-light by a lot on WER clean test 100h labeled: others ~4 vs theirs ~2.5 > SoTa on high-resource noisy data (3.3 vs 3.4) close to SoTa on clean data This note isn't super specific, but it's outdated if I'm understanding it correctly. To my understanding, the SOTA on this data is held by Conformer 1B (a 1 billion parameter model), at 1.4 clea…
I always wonder how people figure out these successful gigantic models if it takes hundreds of TPUs and days to train them. I recently bought rtx 3090 in hopes of playing around with some computer vision applications but I guess having 24GB VRAM is nothing if I want to get something SOTA working.
The EfficientNet paper has some good things to say on this.
If you're working at a place with giant datacenters full of (T/G)PUs, you can train one giant model a few times, or train smaller models hundreds of times. Without hyperparameter search, there's a really high chance that you're just looking in the wrong region and wind up with something gigantic but kinda meh.
So, the simple strategy is to use the smaller models to find a great mix of hyperparameters, and then scale up to a gigantic model. The EfficientNet paper demonstrates some fairly reliable ways to scale up the model, changing width and depth together according to a scaling factor.
But yeah, even for smaller model footprints, the ability to run tens of experiments in parallel goes a very long way. If you've got a single GPU to play with, I would instead try to focus on a well-scoped interesting question that you can answer without having to demonstrate SOTA-ness, as it will be an uphill climb.
Also remember that it's good to lean heavily on pre-trained models to save time. Anything you can do to iterate faster, really.
Re: Wav2vec Overview: Semi and Unsupervised Speech Recognition
#16One addendum to the linked post's notes: > SoTa in low-resource setting Libri-light by a lot on WER clean test 100h labeled: others ~4 vs theirs ~2.5 > SoTa on high-resource noisy data (3.3 vs 3.4) close to SoTa on clean data This note isn't super specific, but it's outdated if I'm understanding it correctly. To my understanding, the SOTA on this data is held by Conformer 1B (a 1 billion parameter model), at 1.4 clea…
I always wonder how people figure out these successful gigantic models if it takes hundreds of TPUs and days to train them. I recently bought rtx 3090 in hopes of playing around with some computer vision applications but I guess having 24GB VRAM is nothing if I want to get something SOTA working.
The RTX 3090 is a beast compared to what researchers had available to them just a few years ago.
Don't try to chase SOTA - that's a fruitless endeavour.
24GB of VRAM is plenty for CV and you can train some excellent models with it. You also need to keep in mind that you don't necessarily need to train models from scratch either.
You can achieve great things by downloading a well-tested, pretrained model and fine-tune it for your particular task or application. Trying to come up with new models and training them from scratch is an exercise in futility for really big models.
I usually only train smaller models (couple of million parameters) and training and finetuning usually takes anywhere from a few hours to a day or two. But then again my hardware is two generations older than yours.
Re: Wav2vec Overview: Semi and Unsupervised Speech Recognition
#17Does anyone know of some good open sourced projects for OCR? Tesseract always seems to be the default, and then it seems Google cloud, and other services are miles ahead. However, for those who don't want to rely on the big tech companies, are there any comparable alternatives?
I recently came across CRAFT wich appears to have come out of the ICDAR2017 Robust reading challenge. It performed better than expected. I only tested a few images so please don't take my word for it. That led me to PaddleOCR. There is still plenty of room for improvement but I found it way more convenient to use for my purposes than messing with Tesseract. https://github.com/clovaai/CRAFT-pytorch https://github.com/…
I just tried it at [1] on the text of your comment and it replied with an empty result.
Perhaps I should try more examples, but it doesn't look like it's ready yet.
Re: Wav2vec Overview: Semi and Unsupervised Speech Recognition
#18Earlier quoted context omitted.
I always wonder how people figure out these successful gigantic models if it takes hundreds of TPUs and days to train them. I recently bought rtx 3090 in hopes of playing around with some computer vision applications but I guess having 24GB VRAM is nothing if I want to get something SOTA working.
The RTX 3090 is a beast compared to what researchers had available to them just a few years ago. Don't try to chase SOTA - that's a fruitless endeavour. 24GB of VRAM is plenty for CV and you can train some excellent models with it. You also need to keep in mind that you don't necessarily need to train models from scratch either. You can achieve great things by downloading a well-tested, pretrained model and fine-tune…
The real research problem is being able to buy a 3090.
Re: Wav2vec Overview: Semi and Unsupervised Speech Recognition
#19Does anyone know of some good open sourced projects for OCR? Tesseract always seems to be the default, and then it seems Google cloud, and other services are miles ahead. However, for those who don't want to rely on the big tech companies, are there any comparable alternatives?
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Re: Wav2vec Overview: Semi and Unsupervised Speech Recognition
#20Does anyone know of some good open sourced projects for OCR? Tesseract always seems to be the default, and then it seems Google cloud, and other services are miles ahead. However, for those who don't want to rely on the big tech companies, are there any comparable alternatives?
In terms of setup and ease of use jaided OCR[1] is the best one I have used out there. I can't stress how many times I tried other alternative (tesseract, paddle OCR) out there and kept going back to use their OCR library. Mainly because it supports a large variety of languages out of the box while provide nice results.
Disclaimer : I am not affiliated to jaidedAI, just a satisfied user.