DeepSpeech
NeMo
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DeepSpeech | NeMo | |
---|---|---|
67 | 29 | |
24,278 | 10,021 | |
1.2% | 6.5% | |
0.0 | 9.8 | |
2 months ago | 5 days ago | |
C++ | Python | |
Mozilla Public License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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/
NeMo
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[P] Making a TTS voice, HK-47 from Kotor using Tortoise (Ideally WaveRNN)
I don't test WaveRNN but from the ones that I know the best that is open source is FastPitch. And it's easy to use, here is the tutorial for voice cloning.
- [N] Huggingface/nvidia release open source GPT-2B trained on 1.1T tokens
- [D] What is the best open source text to speech model?
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[D] JAX vs PyTorch in 2023
Nowadays... bigger repos like https://github.com/NVIDIA/NeMo are all pytorch, lots of work also published by Meta and Microsoft is all torch. I check new work on GitHub all the time and I haven't seen a Tensorflow repo in years except one.
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[D] What's stopping you from working on speech and voice?
- https://github.com/NVIDIA/NeMo
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Can I use PyTorch to build a fast capitalization recoverer?
Can’t you use the NeMo model and just strip the punctuation from the output again if you don’t want it? You can also fine tune the the model with capitalization only if you look at the examples https://github.com/NVIDIA/NeMo/blob/stable/tutorials/nlp/Punctuation_and_Capitalization.ipynb The capitalization and punctuation are annotated separately (U indicates that the word should be upper cased, and O - no capitalization ). The model seems to be a token level classifier not seq to seq so there should also be a way to get just the capitalization part but you would have to look into the model as it’s not shown in the examples.
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I made a free transcription service powered by Whisper AI
I think there's been talk to do speaker diarization with whisper-asr-webservice[0] which is also written in python and should be able to make use of goodies such as pyannote-audio, py-webrtcvad, etc.
Whisper is great but at the point we get to kludging various things together it starts to make more sense to use something like Nvidia NeMo[1] which was built with all of this in mind and more
[0] - https://github.com/ahmetoner/whisper-asr-webservice
[1] - https://github.com/NVIDIA/NeMo
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Mozilla Common Voice - Korean Language is live - Help Build a Korean Corpus for Training AI/Navi/etc
[커먼보이스 전자우편](mailto:[email protected]) || Common Voice || Korean Language Homepage || FAQs || Speaking Aloud and Reviewing Recordings || Sentence Collector || NVidia/NeMo
- Whisper – open source speech recognition by OpenAI
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Using Edge Biometrics For Better AI Security System Development
The final security grain was added with speech-to-text anti-spoofing built on QuartzNet from the Nemo framework. This model provides a decent quality user experience and is suitable for real-time scenarios. To measure how close what the person says to what the system expects, requires calculation of the Levenshtein distance between them.
What are some alternatives?
Kaldi Speech Recognition Toolkit - kaldi-asr/kaldi is the official location of the Kaldi project.
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
picovoice - On-device voice assistant platform powered by deep learning
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
STT - 🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
espnet - End-to-End Speech Processing Toolkit
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
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.
dicio-android - Dicio assistant app for Android