DeepSpeech
silero-models
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DeepSpeech | silero-models | |
---|---|---|
67 | 32 | |
24,212 | 4,534 | |
1.2% | - | |
0.0 | 4.7 | |
2 months ago | 6 months ago | |
C++ | Jupyter Notebook | |
Mozilla Public License 2.0 | GNU General Public License v3.0 or later |
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
DeepSpeech
- Common Voice
- Ask HN: Speech to text models, are they usable yet?
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Looking to recreate a cool AI assistant project with free tools
- [DeepSpeech](https://github.com/mozilla/DeepSpeech) rather than Whisper for offline speech-to-text
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](https://opencv.org/) 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](https://github.com/mozilla/DeepSpeech) rather than Whisper for offline speech-to-text - [Coqui TTS](https://github.com/coqui-ai/TTS) instead of Whisper for text-to-speech - Browser automation with [Selenium](https://www.selenium.dev/) 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
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Linux Mint XFCE
algo assim? https://github.com/mozilla/DeepSpeech
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Are there any secure and free auto transcription software ?
If you're not afraid to get a little technical, you could take a look at mozilla/DeepSpeech (installation & usage docs here).
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Web Speech API is (still) broken on Linux circa 2023
There is a lot of TTS and SST development going on (https://github.com/mozilla/TTS; https://github.com/mozilla/DeepSpeech; https://github.com/common-voice/common-voice). That is the only way they work: Contributions from the wild.
- Deepspeech /common voice.
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Mozilla Launches Responsible AI Challenge
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.
[0]: https://github.com/mozilla/deepspeech
[1]: https://github.com/mozilla/firefox-translations/
[2]: https://research.mozilla.org/
silero-models
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Weird A.I. Yankovic, a cursed deep dive into the world of voice cloning
I doubt it's currently actually "the best open source text to speech", but the answer I came up with when throwing a couple of hours at the problem some months ago was "Silero" [0, 1].
Following the "standalone" guide [2], it was pretty trivial to make the model render my sample text in about 100 English "voices" (many of which were similar to each other, and in varying quality). Sampling those, I got about 10 that were pretty "good". And maybe 6 that were the "best ones" (pretty natural, not annoying to listen to).
IIRC the license was free for noncommercial use only. I'm not sure exactly "how open source" they are, but it was simple to install the dependencies and write the basic Python to try it out; I had to write a for loop to try all the voices like I wanted. I ended using something else for the project for other reasons, but this could still be fairly good backup option for some use cases IMO.
[0] https://github.com/snakers4/silero-models#text-to-speech
- What's the best text-to-speech free non-cloud software?
- Hey can anyone else add the text to speech
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Messing around with a TTS extension
Glados was the first experiment. I moved on to silero afterwards: https://github.com/snakers4/silero-models
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Ask HN: Open-source video transcribing software?
Some months ago I tried the Silero Models: https://github.com/snakers4/silero-models
With the audio sources I had, in English, the transcription had many mistakes. The good side is that installing and running the software worked as described in their documentation, so maybe it’s worth giving it a try by yourself.
- Silero V3:20种语言的快速高质量文本到语音,有173种声音 (Silero V3: fast high-quality text-to-speech in 20 languages with 173 voices)
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Hacker News top posts: Jun 20, 2022
Silero V3: fast high-quality text-to-speech in 20 languages with 173 voices\ (56 comments)
- Silero V3: fast high-quality text-to-speech in 20 languages with 173 voices
What are some alternatives?
Kaldi Speech Recognition Toolkit - kaldi-asr/kaldi is the official location of the Kaldi project.
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
NeMo - A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
picovoice - On-device voice assistant platform powered by deep learning
piper - A fast, local neural text to speech system
STT - 🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
Serpent.AI - Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own!
Porcupine - On-device wake word detection powered by deep learning
PaddleSpeech - Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration