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NeMo Alternatives
Similar projects and alternatives to NeMo
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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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.
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common-voice
Common Voice is part of Mozilla's initiative to help teach machines how real people speak.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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TTS
:robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts) (by mozilla)
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vosk-api
Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
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equinox
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/
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common-voice-android
Repository of "CV Project" app. It's an unofficial app for Mozilla Common Voice, which permits you to contribute to this project via your device.
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pyannote-audio
Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding
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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.
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STT
🐸STT - The deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
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SaaSHub
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NeMo reviews and mentions
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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.
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A note from our sponsor - SaaSHub
www.saashub.com | 26 Apr 2024
Stats
NVIDIA/NeMo is an open source project licensed under Apache License 2.0 which is an OSI approved license.
The primary programming language of NeMo is Python.
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