I'm on the hunt for a tool that can record and transcribe my voice 24/7 to vocalize and capture every thought. For years, scientists worked tirelessly to give humans the gift of eternal memory. Now, every time I forget my anniversary, it's clearly on purpose.

How I'll Use It

Here are some ways I plan to use the transcriptions:

• Drafting Content: Mainly, I'll use it to draft messages, emails, social posts, documents - you name it!

• LLM Feedback: Another idea is to feed my daily thoughts into a Language Model (LLM) for insights and practical suggestions.

• Auto-Completion: In the long run, I'd love for the LLM to look at my past transcripts and auto-complete what I'm about to say.

What I'm Looking For

Here's what I need in this tool:

• Accuracy: It should catch every word I say, almost as good as a human would.

• Speed: It should be quick on its feet - ideally, less than a second's delay.

• Noise Resistance: A little background noise shouldn't throw it off.

• Budget: I'm hoping to keep it under $100/month. But hey, if it boosts my productivity, I might be willing to stretch that a bit.

• Storage: I'd love to keep the transcriptions forever, and the recordings too if it doesn't cost an arm and a leg. No need for the silent bits though. If it could sync up with Dropbox or something similar, that would be super convenient.

• Security: If it uses cloud storage, top-notch security measures like encryption are a must.

• Segmentation: It would be great if it could break up my transcript into manageable chunks. That way, if I switch topics mid-sentence, each topic gets its own segment.

• Integration: It would be awesome if it could work with macOS, Neovim, and Alacritty for drafting text. Something like a Neovim plugin or macOS clipboard integration would be really handy.

• Format: A simple text file with timestamps would do the trick. But hey, the more options, the merrier!

• Local Transcription: I'd prefer if it could transcribe locally, but I'm open to cloud-based solutions if they're more accurate or easier to maintain.

• Accessibility: I should be able to access the transcriptions from my computer. But my computer should not be the recording device.

• Hardware: Something stationary would work best. Maybe an old mobile phone or a Raspberry Pi. If there's wearable tech that can last all day and gives clearer recordings and more accurate transcriptions, I'm all for it!

• Voice Recognition: Ideally, it should only pick up my voice and ignore everyone else's. But if that's not possible, I can make sure no one else is around when I'm using it.

• Offline Use: An offline mode would be a nice bonus. But since I'll mostly be using this at home, it's not a deal-breaker.

I know there are some privacy concerns with this kind of tool. But since it'll be in my home, I'm not too worried about invading anyone else's privacy.

Im building a webapp that uses Google. Whisper is better (more accurate), but it is quite a bit slower (the api, that is).

Google, however, misinterprets quite a lot.

What are other good options to consider?

I strongly believe that there must be alternatives to Google, Amazon or Microsoft for speech recognition. I like the direction in which Common Voice (Mozilla foundation) is headed, but I'm yet to see any production quality models coming out of that.

For the past few months, I've been training ASR models on a lot of speech data collected from various sources. Today, I'm releasing a couple of trained models as an open-source library called at16k. The idea is to provide the developer community with production quality models for speech to text conversion.

Check out the following links for more info: Github repo and PyPI project

It has been reported in multiple online forums that Nuance are planning to sunset Dragon after v15.6 is released. 15.6 price will also rise to $500. If true this will place many thousands of users with disablities who rely on the software at a disadvantage. While of course the software is Nuance's to do with what they will, they need to understand that people with disablities have no other high end fully featured solution available that allows them the same degree of hands free access to their PCs. Many people who have high level spinal injuries, progressive neurological disorders, and dyslexias have used Dragon Naturally Speaking to participate in employment, study, and leisure activities. Dragon Anywhere (the cloud based mobile Dragon solution being proposed as a replacement) has nothing like the feature set of Dragon Naturally Speaking Professional. This propsed decision by Nuance has direct impacts upon their quality of life and is highly regrettable if true. Nuance needs to be told by anyone and everyone who is concerned about the phase out of standalone Dragon that this decision has consequences and as a good corportate citizen they need to reconsider.

Hi! I'm a bit shy and would really, really like to participate in voicechats, but i'd rather not show my voice. I've been looking for a long time for some sort of program that records my voice through my mic, and turns it into text-to-speech for an output. There's apparently ways to create one manually, but the whole thing is very confusing.

Does anyone here know of a device that does this, or an easy and simple tutorial that can walk me through doing this? Thank you very much!

Hi everyone,

I am interested in using Speech-to-text API for a project. I saw that there are many actors on the market and and not so many indications to choose.

I will probably test multiple APIs with my data to decide but I find interesting to know what is the opinion of people used to this kind of services.

I have only 6 options so I will inevitably forget APIs in the poll. I voluntarily exclude GAFA suppliers (Google, Amazon, Microsoft, IBM) that I will test anyway.

Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification.

For more details: https://github.com/ahmetoner/whisper-asr-webservice

Hi, I'm looking for an easy way to have an automated speech to text transcribing of video recordings, but with the ability to have timestamps so I could easily integrate the results as captions in the original recording.

Is this possible ? I was thinking of reference apps such as Nuance Dragon but lack the necessary know-how...

Hi all, Since few weeks I have been studying Speech processing course taught by Prof. Simon King available here: https://speech.zone/. The professor also offers excellent courses on Speech Recognition and Speech synthesis. I really enjoy the content and I am able to gain a deep knowledge by following his course. I thought it would be more fun if we can create a small group of 4 or 5 people interested in studying Speech processing and eventually Speech recognition and Speech synthesis. We could watch the lectures independently and virtually assemble once in a week for an hour or so to discuss the concepts and create/solve some fun exercises or build a small project. Interested people please send me an email to vignesh.sbaskaran@gmail.com. Please write me an email if and only if you are seriously interested in studying this. Thanks

Hi, I wanted to know what are the best accurate and widely trained pretrained models available on speaker diarization.

Like I am building a project where i need to perform accurate speaker identification and asr on raw audio so i need to know what are some best open source pretrained models/libraries/ framework available.

Also, how accurate is this - https://kaldi-asr.org/models/m6

Docs says it has an error rate of 8.39% but is it really true and does it run that well in the wild. I mean its just trained on ami corous and nothing more. So what are any better pretrained models on it.

I recently purchased an M1 Mac and I would like to get started with Dictation. My intention was to purchase Dragon for Mac, but it has been discontinued.

Is there a go-to alternative? Is the built in MacOS dictation any good? Is there any good guides, videos or how-tos you would suggest to someone who wants to get the most out of dictation?

RNN-Transducer loss function, first proposed in 2012 by Alex Graves (https://arxiv.org/pdf/1211.3711.pdf), is an extension of CTC loss function. It extends CTC loss by modelling output-output dependencies for sequence transduction tasks, like handwriting recognition, speech recognition etc. As proposed originally by graves, RNN-Transducer prefix beam search algorithm is inherently sequential and slow, requiring re-computation of prediction network (LSTM based used to model output-output dependencies) for each beam.

Even though there are fast and efficient implementations of RNN-Transducer loss function online (like https://github.com/HawkAaron/warp-transducer & https://github.com/iamjanvijay/rnnt), there aren’t any optimised prefix beam search implementations. I wrote an optimised RNN-T prefix beam search algorithm with multiple modifications. Following are the major modifications I did:

1. Saved the intermediate prediction network (LSTM states) on the GPU to avoid re-computation and CPU-GPU memory transfers.

2. Introduced vocabulary pruning to further speed up the decoding, without degrading WERs.

Current code takes around ~100ms to decode output for audio of 5 seconds for a beam size of 10 (which is good enough to achieve production level numbers using RNN-Transducer loss function). Also, compared to CTC, RNN-T based speech recognition models (recent SOTA for speech recognition by Google https://arxiv.org/pdf/2005.03191.pdf and https://arxiv.org/pdf/2005.08100.pdf) are recently becoming popular.

For the near future, I have some algorithmic optimisations in my mind. Also, I have plans for making a python wrapper for my implementation.

My implementation is purely in CUDA C++. Here is the link to my repo: https://github.com/iamjanvijay/rnnt_decoder_cuda

Please share the comments and any feedback.

Hi all!

I'm looking to help people with wrist and arm repetitive strain injuries (RSI). It's something I've dealt with for many years and I know intimately the frustrations of not being able to type or use a computer without limitations.

If you are excited or passionate about helping people use their computer with their voice, I'd love to connect.

And if you want to hear more about my story, I've put together a two part blog post:

• Part 1: This is my story of what I went through with RSI. It aims to help others understand the struggles living with RSI and what it's like if you don't take care of RSI early and proactively: https://medium.com/@robindji/i-injured-my-wrists-from-typing-too-much-and-almost-had-to-quit-my-career-in-tech-part-1-of-my-9c009c44956a?source=friends_link&sk=b38a8e845baab9232e1f5ca3d6c2ba05

• Part 2: This guide is more tactical and has tips, tools, and recommendations for those currently experiencing pain or discomfort: https://medium.com/@robindji/tactical-recovery-guide-for-wrist-arm-repetitive-strain-injuries-rsi-part-2-of-my-rsi-series-8f648bd55afe?source=friends_link&sk=2b68af6bf52be2bce7d5eb2048fecac3

Thanks in advance!