espnet
WhisperLive
espnet | WhisperLive | |
---|---|---|
15 | 4 | |
7,892 | 1,180 | |
1.3% | 11.9% | |
10.0 | 9.4 | |
1 day ago | 25 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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espnet
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WhisperSpeech – An Open Source text-to-speech system built by inverting Whisper
You might check out this list from espnet. They list the different corpuses they use to train their models sorted by language and task (ASR, TTS etc):
https://github.com/espnet/espnet/blob/master/egs2/README.md
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[D] What's stopping you from working on speech and voice?
- https://github.com/espnet/espnet
- Íslensk talgervilsrödd sem hægt er að nota á Macca
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High quality, fast performing, local text to speech generation
This link has instructions for doing this for a Japanese model. It would have to be altered to work with ljspeech and the fine tune dataset.
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Text to speech generation
This work is made possible by the excellent advancements in text to speech modeling. ESPnet is a great project and should be checked out for more advanced and a wider range of use cases. This pipeline was also made possible by the great work from espnet_onnx in building a framework to export models to ONNX.
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[P] TorToiSe - a true zero-shot multi-voice TTS engine
CMU WavLab has ESPNet https://espnet.github.io/espnet/ which includes a number of high quality TTS models including VITS (which in my subjective experience is just as good as what is demonstrated here). Also the inference on various ESPNet pretrained TTS models is reasonable and sentences take on average 5 seconds per word to generate the waveform on my totally mid PC setup.
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How to get Job in NLP?
The reason I'm saying this is to point out that having and in-depth knowledge on speech processing/generation requires a lot of information about signal processing and human speech in general (eg. acoustics and phonetics). However, if you're not into learning everything there is to know about a subject, just take one state-of-the-art example and study that as best as you can. Pick one environment/toolkit, for example espnet and simply go with that.
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Help picking a good speech recognition library
https://github.com/espnet/espnet (kind of like a newer Kaldi, but also not beginner friendly)
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speechbrain VS espnet - a user suggested alternative
2 projects | 13 Oct 2021
both provide e2e ASR support but espnet does have more utilities where as speechbarain is clean
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Need help with training ASR model from scratch.
This is relatively small amount of speech to train the model from scratch, but you can train using another pre-trained model for initialization. There are numbers of end-to-end ASR toolkits which can be used for this: https://github.com/NVIDIA/NeMo and https://github.com/espnet/espnet
WhisperLive
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Show HN: WhisperFusion – Ultra-low latency conversations with an AI chatbot
Everything runs locally, we use:
- WhisperLive for the transcription - https://github.com/collabora/WhisperLive
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WhisperSpeech – An Open Source text-to-speech system built by inverting Whisper
Check out WhisperLive: https://github.com/collabora/WhisperLive
If you're grappling with the slow march from cool tech demos to real-world language model apps, you might wanna check out WhisperLive. It's this rad open-source project that’s all about leveraging Whisper models for slick live transcription. Think real-time, on-the-fly translated captions for those global meetups. It's a neat example of practical, user-focused tech in action. Dive into the details on their GitHub page
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Whisper: Nvidia RTX 4090 vs. M1 Pro with MLX
https://github.com/collabora/WhisperLive
The is another one that uses huggingface's implementation, but I haven't tried it since my spec doesn't support flash-att2
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Triple Threat: The Power of Transcription, Summary, and Translation
Curious to see how this works? Check out our demo page - https://col.la/transcription to generate your own transcription, summary, and translation, or use our browser extension - https://github.com/collabora/WhisperLive to get live transcriptions.
What are some alternatives?
speechbrain - A PyTorch-based Speech Toolkit
cog-whisper-diarization - Cog implementation of transcribing + diarization pipeline with Whisper & Pyannote
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)
whisper-writer - 💬📝 A small dictation app using OpenAI's Whisper speech recognition model.
k2 - FSA/FST algorithms, differentiable, with PyTorch compatibility.
obs-zoom-and-follow - Dynamic zoom and mouse tracking script for OBS Studio
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
gpt_chatbot - This chatbot lets you use your microphone to communicate with GPT-4. It uses the OpenAI text to speech to respond with a voice. It uses Pinecone to store long term information and retrieves it to create context. API keys for OpenAI and Pinecone required. Tested on Windows
kaldi-gstreamer-server - Real-time full-duplex speech recognition server, based on the Kaldi toolkit and the GStreamer framwork.
whisper_streaming - Whisper realtime streaming for long speech-to-text transcription and translation
Kaldi Speech Recognition Toolkit - kaldi-asr/kaldi is the official location of the Kaldi project.
gpt-voice-conversation-chatbot - Allows you to have an engaging and safely emotive spoken / CLI conversation with the AI ChatGPT / GPT-4 while giving you the option to let it remember things discussed.