speech-to-text-websockets-python
dejavu
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| speech-to-text-websockets-python | dejavu | |
|---|---|---|
| - | 15 | |
| 87 | 6,316 | |
| - | - | |
| 0.0 | 0.0 | |
| almost 5 years ago | 5 days ago | |
| Python | Python | |
| - | MIT License |
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speech-to-text-websockets-python
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Tracking mentions began in Dec 2020.
dejavu
- Audio Fingerprinting and Recognition in Python
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Contacting Collectors or Creating API to help with searching
This doesn't seem hard, you can use something like this to dwoanload the songs: https://stackoverflow.com/a/27481870/6151784 and something like this to calculate how much they match: https://github.com/worldveil/dejavu The question is would you create a (dedicated) server to do your work? Or your own pc? You could also create a very simple page where someone would paste you a YouTube profile URL and you would check all songs of this URL. Also to have a db and save information about the matching and which youtube profiles have alsready been checked. Something like that could work.
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Tiny bit of experience but need to compile a Github program. What is the best video / resource to learn to do this quickly?
If you read the installation.md file it clearly states that it has only been tested on UNIX systems, so you might be on your own trying to get it to wor in windows.
- Help needed with school project
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Identification of all usages of OSTs in Made in Abyss (S1)
Using neural networks seems complicated, did you tried audio fingerprinting? I have been using this audio fingerprinting library to power this anime song synchronization script. You can check Panako and dejavu too.
- Dejavu – Audio fingerprinting and recognition algorithm
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fingerprinting sections of audio from file
I want to say these few seconds match these few seconds from a different audio track. Using dejavu raw has overhead I do not need/want and hence I've been fiddling around with the fingerprint script. When modifying the global variables I can get better hits or worse hits, I will admit that even after reading there recommended article and many other sources, I can't find some good explanations about the mathematics behind the filtering after the specgram has been applied. As far as a I am aware we first apply filters to find/make fine points across the spectrogram after that we only check the distance between points along the time axis not the frequency or a hypotenuse (weird).
- Some information and advice about DDoS, from someone who was there during #opPayback
- List of resources
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Uploading an audio dataset into a database for comparison
I used a repo called https://github.com/worldveil/dejavu to compare audio hashed fingerprints and distinguish the difference between them.
What are some alternatives?
SpeechRecognition - Speech recognition module for Python, supporting several engines and APIs, online and offline.
django-elastic-transcoder - Django + AWS Elastic Transcoder
m3u8 - Python m3u8 Parser for HTTP Live Streaming (HLS) Transmissions
Watson Developer Cloud Python SDK - :snake: Client library to use the IBM Watson services in Python and available in pip as watson-developer-cloud
audiolazy - Expressive Digital Signal Processing (DSP) package for Python
speechpy - :speech_balloon: SpeechPy - A Library for Speech Processing and Recognition: http://speechpy.readthedocs.io/en/latest/
pyechonest - Python client for the Echo Nest API
aeneas - aeneas is a Python/C library and a set of tools to automagically synchronize audio and text (aka forced alignment)
pyAudioAnalysis - Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
praatIO - A python library for working with praat, textgrids, time aligned audio transcripts, and audio files. It is primarily used for extracting features from and making manipulations on audio files given hierarchical time-aligned transcriptions (utterance > word > syllable > phone, etc).
id3reader - Id3reader.py is a Python module that reads ID3 metadata tags in MP3 files.