pyAudioAnalysis
whisper
pyAudioAnalysis | whisper | |
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11 | 344 | |
5,673 | 60,617 | |
- | 3.1% | |
5.0 | 6.4 | |
about 1 month ago | 6 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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pyAudioAnalysis
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How would I compare two voice recordings of the same sentence and advise one speaker how to get closer to the second?
I actually came up with an el cheapo version of what I want to accomplish that isn't perfect but without any research can implement it and it may actually prove useful to language learners. PM me if you're interested in hearing it and critiquing it. I can share here that I'm using this guy's multiple repos though: https://github.com/tyiannak/pyAudioAnalysis
- How do I run code only when an audio file has bass
- A Python library for audio feature extraction, classification, segmentation and applications
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Phonetic search for audio files
Update: From one researcher to another. I was referred to a Python Audio AI project . Once I determine exactly which module to use I should be smooth sailing. I'll send more updates soon.
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Clustering songs with different lengths
Hey folks, I'm looking into clustering audio files with features extracted by pyAudioAnalysis. However, every feature (I'm interested in MFCC, spectral centroid and spread, and BPM) is extracted for each frame of the song (by default 0.05s, excluding BPM that relates to the whole) so tracks with different lengths produce arrays with different shapes.
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AUDIO ANALYSIS WITH LIBROSA
To learn more about pyAudioAnalysis here you go.
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Creating Audio Features with PyAudio Analysis
Humans are great at classifying noises. We can hear a chirp and surmise that it belongs to a bird, we can hear an abstract noise and classify it as as speech with a particular meaning and definition. This relationship between humans and audio classification forms the basis of speech and human communication as a whole. Translating this incredible ability to computers on the other hand can be a difficult challenge to say the least. Whilst we can naturally decompose signals, how do we teach computers to do this, and how do we show what parts of the signal matter and what parts of the signal are irrelevant or noisy? This is where PyAudio Analysis comes in. PyAudio Analysis is an open source Python project by Theodoros Giannakopoulos, a Principle researcher of multimodal machine learning at the Multimedia Analysis Group of the Computational Intelligence Lab (MagCIL). The package aims to simplify the feature extraction and classification process by providing a number of helpful tools at can sift through the signal and create relevant features. These features can then be used to train models for classification tasks.
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[P] Feature extraction for acoustic signals
This might be relevant, which has a set of feature extraction methods implemented: https://github.com/tyiannak/pyAudioAnalysis/wiki/3.-Feature-Extraction
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Hacker News top posts: Dec 11, 2021
A library for audio feature extraction, regression, classification, segmentation\ (2 comments)
- Audio feature extraction, classification, segmentation and applications
whisper
- Creando Subtítulos Automáticos para Vídeos con Python, Faster-Whisper, FFmpeg, Streamlit, Pillow
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Why I Care Deeply About Web Accessibility And You Should Too
Let’s not talk about local models as the hardware requirements are way beyond most of these people’s reach. I have a MacBook Air with an M2 chip and 8GB of RAM and can hardly run Whisper locally, so I use this HuggingFace space.
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How I built NotesGPT – a full-stack AI voice note app
Last week, I launched notesGPT, a free and open source voice note app that has 35,000 visitors, 7,000 users, and over 1,000 GitHub stars so far in the last week. It allows you to record a voice note, transcribes it uses Whisper, and uses Mixtral via Together to extract action items and display them in an action items view. It’s also fully open source and comes equipped with authentication, storage, vector search, action items, and is fully responsive on mobile for ease of use.
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Ask HN: Can AI break a speech audio into individual words?
I found a pretty good discussion in the topic here:
https://github.com/openai/whisper/discussions/1243
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WhisperSpeech – An Open Source text-to-speech system built by inverting Whisper
There is a plot of language performance on their repo: https://github.com/openai/whisper
I am not aware of a multi-lingual leaderboard for speech recognition models.
- Ask HN: AI that allows you to make phone calls in a language you don't speak?
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Ask HN: Favorite Podcast Episodes of 2023?
I don't know how OP does it, but here's how I'd do it:
* Generate a transcript by runing Whisper against the podcast audio file: https://github.com/openai/whisper
* Upload transcript to ChatGPT and ask it to summarize.
* Automate all the above.
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Need advice
Ahh, that makes sense. I've been building something like that, but only from other languages into English using Whisper
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Subtitle is now open-source
Whisper already generates subtitles[0], supporting VTT and SRT so this is just a thin wrapper around that.
[0]: https://github.com/openai/whisper/blob/e58f28804528831904c3b...
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StyleTTS2 – open-source Eleven Labs quality Text To Speech
> although it does require you to wear headphones so the bot doesn't hear itself and get interrupted.
Maybe you can rely on some sort of speaker identification to sort this out?
https://github.com/openai/whisper/discussions/264
What are some alternatives?
librosa - Python library for audio and music analysis
vosk-api - Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node
pydub - Manipulate audio with a simple and easy high level interface
silero-vad - Silero VAD: pre-trained enterprise-grade Voice Activity Detector
SpeechRecognition - Speech recognition module for Python, supporting several engines and APIs, online and offline.
buzz - Buzz transcribes and translates audio offline on your personal computer. Powered by OpenAI's Whisper.
pyAcoustics - A collection of python scripts for extracting and analyzing acoustics from audio files.
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)
mingus - Mingus is a music package for Python
whisper.cpp - Port of OpenAI's Whisper model in C/C++
Watson Developer Cloud Python SDK - :snake: Client library to use the IBM Watson services in Python and available in pip as watson-developer-cloud
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.