precise-wakeword-model-maker
DeepSpeech
precise-wakeword-model-maker | DeepSpeech | |
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2 | 68 | |
16 | 24,324 | |
- | 0.8% | |
4.8 | 0.0 | |
about 2 years ago | 2 months ago | |
Python | C++ | |
Apache License 2.0 | Mozilla Public License 2.0 |
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precise-wakeword-model-maker
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Make your own custom wakeword and other FOSS voice assistant solutions
Precise Wakeword Model Maker
DeepSpeech
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ESpeak-ng: speech synthesizer with more than one hundred languages and accents
As I understand it DeepSpeech is no longer actively maintained by Mozilla: https://github.com/mozilla/DeepSpeech/issues/3693
For Text To Speech, I've found Piper TTS useful (for situations where "quality"=="realistic"/"natual"): https://github.com/rhasspy/piper
For Speech to Text (which AIUI DeepSpeech provided), I've had some success with Vosk: https://github.com/alphacep/vosk-api
- 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.
What are some alternatives?
Leon - 🧠 Leon is your open-source personal assistant.
Kaldi Speech Recognition Toolkit - kaldi-asr/kaldi is the official location of the Kaldi project.
wakeword-data-collector - A prototype CLI in Python where a user can collect all of the recordings needed to produce a wakeword
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)
mfcc-rust
picovoice - On-device voice assistant platform powered by deep learning
NLU-engine-prototype-benchmarks - Demo and benchmarks for building an NLU engine similar to those in voice assistants. Several intent classifiers are implemented and benchmarked. Conditional Random Fields (CRFs) are used for entity extraction.
STT - 🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
precise-rs - Precise hotword listener on Tract and Rust
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
NLU-snips-converter - Quick and dirty solution to convert CSV training data into Snips JSON format and train an engine
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.