larynx
NeMo
larynx | NeMo | |
---|---|---|
18 | 29 | |
788 | 10,084 | |
- | 2.7% | |
0.0 | 9.8 | |
11 months ago | 5 days ago | |
Python | Python | |
MIT License | 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.
larynx
-
Home Assistant’s Year of the Voice – Chapter 2
The most exciting thing about Home Assistant's "Year of the Voice", for me, is that it is apparently enabling/supporting @synesthesiam's continued phenomenal contributions to the FLOSS off-line voice synthesis space.
The quality, variety & diversity of voices that synesthesiam's "Larynx" TTS project (https://github.com/rhasspy/larynx/) made available, completely transformed the Free/Open Source Text To Speech landscape.
In addition "OpenTTS" (https://github.com/synesthesiam/opentts) provided a common API for interacting with multiple FLOSS TTS projects which showed great promise for actually enabling "standing on the shoulders of" rather than re-inventing the same basic functionality every time.
The new "Piper" TTS project mentioned in the article is the apparent successor to Larynx and, along with the accompanying LibriTTS/LibriVox-based voice models, brings to FLOSS TTS something it's never had before:
* Too many voices! :)
Seriously, the current LibriTTS voice model version has 900+ voices (of varying quality levels), how do you even navigate that many?![0]
And that's not even considering the even higher quality single speaker models based on other audio recording sources.
Offline TTS while immensely valuable for individuals, doesn't seem to be attractive domain for most commercial entities due to lack of lock-in/telemetry opportunities so I was concerned that we might end up missing out on further valuable contributions from synesthesiam's specialised skills & experience due to financial realities & the human need for food. :)
I'm glad we instead get to see what happens next.
[0] See my follow-up comment about this.
-
Text to speech
Larynx!
-
Ask HN: Are there any good open source Text-to-Speech tools?
I've had good results with https://github.com/rhasspy/larynx
-
Recommend a Text to Speech tool ?
Larynx is a really good text-to-speech engine
-
Klipper on android
I was able to install 3.7 following this guide. https://github.com/rhasspy/larynx/issues/9
- I built an audio only Gemini client.
-
NaturalSpeech: End-to-End Text to Speech Synthesis with Human-Level Quality
If you've not already encountered them I'd definitely encourage you to check out these Free/Open Source projects too:
* Larynx: https://github.com/rhasspy/larynx/
* OpenTTS: https://github.com/synesthesiam/opentts
* Likely Mimic3 in the near future: https://mycroft.ai/blog/mimic-3-preview/
Larynx in particular has a focus on "faster than real-time" while OpenTTS is an attempt to package & provide common REST API to all Free/Open Source Text To Speech systems so the FLOSS ecosystem can build on previous work supported by short-lived business interests, rather than start from scratch every time.
AIUI the developer of the first two projects now works for Mycroft AI & is involved in the development of Mimic3 which seems very promising given how much of an impact on quality his solo work has had in just the past couple of years or so.
-
Need a recommendation: Self hosted speech to text service
I haven't used it on it's own, but Larynx has worked well for me for Rhasspy
- NATSpeech: High Quality Text-to-Speech Implementation with HuggingFace Demo
- Question: Does anybody know of a working Text to Speech for python on pi?
NeMo
-
[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?
-
[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.
-
[D] What's stopping you from working on speech and voice?
- https://github.com/NVIDIA/NeMo
-
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.
-
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
-
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
-
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?
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
pyannote-audio - Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
TTS - 🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
DeepSpeech - 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.
RHVoice - a free and open source speech synthesizer for Russian and other languages
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
TTS - :robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts)
espnet - End-to-End Speech Processing Toolkit
rhasspy - Offline private voice assistant for many human languages
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference