DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers. (by mozilla)

DeepSpeech Alternatives

Similar projects and alternatives to DeepSpeech

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better DeepSpeech alternative or higher similarity.

DeepSpeech reviews and mentions

Posts with mentions or reviews of DeepSpeech. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-05.
  • Common Voice
    5 projects | | 5 Dec 2023
  • Ask HN: Speech to text models, are they usable yet?
    2 projects | | 22 Oct 2023
  • Looking to recreate a cool AI assistant project with free tools
    3 projects | /r/selfhosted | 2 Aug 2023
    - [DeepSpeech]( rather than Whisper for offline speech-to-text
    3 projects | /r/techsupport | 2 Aug 2023
    I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest way to implement it using free, open-source software. Here's what he used originally, followed by some open source candidates I'm considering but would love feedback and advice before starting: Original Tools: - YoloV8 does the heavy lifting with the object detection - OpenAI Whisper handles voice - GPT-4 handles the “AI” - Google Custom Search Engine handles web browsing - MacOS/iOS handles streaming the video from my iPhone to my Mac - Python for the rest Open Source Alternatives: - [ OpenCV]( instead of YoloV8 for computer vision and object detection - Replacing GPT-4 is still a challenge as I know there are some good open-source LLms like Llama 2, but I don't know how to apply this in the code perhaps in the form of api - [DeepSpeech]( rather than Whisper for offline speech-to-text - [Coqui TTS]( instead of Whisper for text-to-speech - Browser automation with [Selenium]( instead of Google Custom Search - Stream video from phone via RTSP instead of iOS integration - Python for rest of code I'm new to working with tools like OpenCV, DeepSpeech, etc so would love any advice on the best way to replicate the original project in an open source way before I dive in. Are there any good guides or better resources out there? What are some pitfalls to avoid? Any help is much appreciated!
  • Speech-to-Text in Real Time
    1 project | | 16 Jul 2023
  • Linux Mint XFCE
    1 project | /r/linuxbrasil | 29 Apr 2023
    algo assim?
  • Are there any secure and free auto transcription software ?
    2 projects | /r/software | 19 Apr 2023
    If you're not afraid to get a little technical, you could take a look at mozilla/DeepSpeech (installation & usage docs here).
  • Web Speech API is (still) broken on Linux circa 2023
    8 projects | /r/javascript | 15 Apr 2023
    There is a lot of TTS and SST development going on (;; That is the only way they work: Contributions from the wild.
  • Deepspeech /common voice.
    1 project | /r/mozilla | 14 Apr 2023
  • Mozilla Launches Responsible AI Challenge
    2 projects | | 15 Mar 2023
    Mozilla did release DeepSpeech[0] and Firefox Translation[1] (the latter of which they included in Firefox, to offer client-side webpage translations.)

    They definitely have fewer resources than OpenAI, and they do not produce SOTA research (their publications have plummeted to 1/year anyway[2]). So the only way for them to make progress is to seek government grants or make challenges like these.

    This challenge is unlikely to be profitable for the winning team: the expected value of winnings are likely around $1K when taking into account the probability that another team gets a better rank, but ML research projects are often more expensive (recently, Alpaca spent upwards of $600 on computation alone; and of course pretraining large models is much more expensive). So the main gain will be publicity.




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