pyannote-audio
uis-rnn
| pyannote-audio | uis-rnn | |
|---|---|---|
| 15 | 3 | |
| 5,077 | 1,530 | |
| 4.3% | 0.3% | |
| 8.6 | 3.5 | |
| 3 days ago | 8 months ago | |
| Jupyter Notebook | Python | |
| MIT License | Apache License 2.0 |
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pyannote-audio
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Open Source Libraries
pyannote/pyannote-audio
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AI Transcribing tool for video with two voices?
Open Source. I've found this to be pretty nice, which is just a wrapper on some hugging face models https://github.com/pyannote/pyannote-audio
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Show HN: PodText.ai – Search anything said on a podcast, Highlight text to play
(not the creator, but I've built something similar for personal use)
This is a great library for determining which speaker is speaking during each time in an audio file (this is called speaker diarization); I imagine they used it or something like it. Works really well out of the box!
https://github.com/pyannote/pyannote-audio
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I wanted to use OpenAI's Whisper speech-to-text on my Mac without installing stuff in the Terminal so I made MacWhisper, a free Mac app to transcribe audio and video files for easy transcription and subtitle generation. Would love to hear some feedback on it!
Do you think pyannote could be implemented in the Pro version of the app to support diarization?
- I won several speaker diarization challenges with pyannote.audio
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I made a free transcription service powered by Whisper AI
Free startup idea: Use Whisper with pyannote-audio[0]’s speaker diarization. Upload a recording, get back a multi-speaker annotated transcription.
Make a JSON API and I’ll be your first customer.
[0] https://github.com/pyannote/pyannote-audio
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Can Whisper differentiate between different voices?
Whisper can’t, but pyannote-audio can. I’ve seen a couple of prototypes out there which link the two together.
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[D] Is there a way to distinguish different human voices from 1 audio file ?
You can use pyannote python library. It will identify different speakers from audio and will create small audio files with those speakers.
- Post-Game Analysis: Destiny & Alex VS Andrew & Zen Shapiro
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A quick and dirty tool for automatically analyzing speaking time in online debates (Effortpost)
This Colab notebook is basically a standard template (with small changes) provided by pyannote-audio, the library implementing the speaker diarization functionality we need. (template)
uis-rnn
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[D] Is there a way to distinguish different human voices from 1 audio file ?
Looks like you can get an put of the box here: https://github.com/google/uis-rnn
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Putting my degree to use. (Exclude Specials and Guests)
Discussion: - When I started this, I thought I would use something like the VoxSort Diarization and it would be easy. But these apps are terrible, especially in recognizing Joey apart from Garnt. Connor has a distinct voice so it was recognizable but still bad. But I didn't think Joey's and Garnt's voices were so similar. - Tested the thing and it's accuracy is almost 99%. - You can still improve this by cutting the episode into smaller chunk but 1 second is the maximum for my computer, any smaller than that i will run out of RAM. I can work to get around this but hey I'm lazy. - The library to implement yourself from google.
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Finally, my degree can be useful
I used this algorithm from Google to determine "who spoke when".
What are some alternatives?
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)
pyDenStream - Implementation of the DenStream algorithm in Python.
speechbrain - A PyTorch-based Speech Toolkit
lightning-bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers.
Resemblyzer - A python package to analyze and compare voices with deep learning
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Kaldi Speech Recognition Toolkit - kaldi-asr/kaldi is the official location of the Kaldi project.
hover - :speedboat: Label data at scale. Fun and precision included.
inaSpeechSegmenter - CNN-based audio segmentation toolkit. Allows to detect speech, music, noise and speaker gender. Has been designed for large scale gender equality studies based on speech time per gender.
ECAPA-TDNN - Unofficial reimplementation of ECAPA-TDNN for speaker recognition (EER=0.86 for Vox1_O when train only in Vox2)
segmentation_models.pytorch - Segmentation models with pretrained backbones. PyTorch.
Clover - An Efficient DNA Clustering algorithm based on Tree Structure.