SincNet
pyannote-audio
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| SincNet | pyannote-audio | |
|---|---|---|
| 3 | 15 | |
| 1,097 | 5,027 | |
| - | 7.0% | |
| 0.0 | 8.6 | |
| about 3 years ago | 5 days ago | |
| Python | Jupyter Notebook | |
| MIT License | MIT License |
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SincNet
- Does this SincNet (neural architecture) contain a discriminator?
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TypeError: layer_norm(): argument 'input' (position 1) must be Tensor, not SincNet.
the sincnet class is taken from here: https://github.com/mravanelli/SincNet/blob/master/dnn_models.py
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[R][P] Announcing audax, a audio ML/DL framework in Jax
Code for https://arxiv.org/abs/1808.00158 found: https://github.com/mravanelli/SincNet
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)
What are some alternatives?
speechbrain - A PyTorch-based Speech Toolkit
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)
cnnimageretrieval-pytorch - CNN Image Retrieval in PyTorch: Training and evaluating CNNs for Image Retrieval in PyTorch
Image-Forgery-Detection-CNN - Image forgery detection using convolutional neural networks. Group 10's final project for TU Delft's course CS4180 Deep Learning 2019.
Resemblyzer - A python package to analyze and compare voices with deep learning
ruptures - ruptures: change point detection in Python
Kaldi Speech Recognition Toolkit - kaldi-asr/kaldi is the official location of the Kaldi project.
UHV-OTS-Speech - A data annotation pipeline to generate high-quality, large-scale speech datasets with machine pre-labeling and fully manual auditing.
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.
stereo-image-generation - This repository contains code to generate stereo (Side by side) image from a single image.
uis-rnn - This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.