Matcha-TTS
librosa
| Matcha-TTS | librosa | |
|---|---|---|
| 1 | 14 | |
| 397 | 6,724 | |
| - | 1.7% | |
| 8.0 | 7.2 | |
| 18 days ago | 3 days ago | |
| Jupyter Notebook | Python | |
| MIT License | ISC License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Matcha-TTS
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Open Source Libraries
shivammehta25/Matcha-TTS
librosa
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Open Source Libraries
librosa/librosa: Python library for audio and music analysis
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A Cross-Platform library for audio spectrogram and feature extraction, support mobile real-time computing
How does this compare to mature libraries for other platforms like librosa?
- Precious Advices About AI-supported Audio Classification Model
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What are the common audio feature tool libraries in python?
I use librosa now. What other useful audio feature extraction libraries are there?
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Looking for a program that will examine a folder full of mp3s or flacs and list out ones with lower or higher than average volume
librosa can do that easily but I think there is an easier way to find what are you looking for:
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Get amplitude of every audio frame of .wav
I have a .wav file, and using python, I'd like to get a list of every audio frame where the amplitude is at the resting position. How could I achieve this? I think the librosa library could do such a thing, but I'm struggling to find exactly how to do it. Any help would be greatly appreciated, thank you.
- Show HN: I'm building a browser-based DAW
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AUDIO ANALYSIS WITH LIBROSA
Librosa is a Python package developed for music and audio analysis. It is specific on capturing the audio information to be transformed into a data block. However, the documentation and example are good to understand how to work with audio data science projects.
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AUDIO CLASSIFICATION USING DEEP LEARNING
Hello! welcome once again to the continuation of the last blog post about audio analysis using the Librosa python library, if you missed this article don't worry here you can enjoy audio analysis techniques with Librosa.
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DATA AUGMENTATION IN NATURAL LANGUAGE PROCESSING
Changing pitch of the audio:- in this technique python package for audio analysis like Librosa is the best tool to go with, by adding effect on the audio pitch to create new audio data.
What are some alternatives?
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
pyAudioAnalysis - Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
audio-webui - A webui for different audio related Neural Networks
pydub - Manipulate audio with a simple and easy high level interface
faster-whisper - Faster Whisper transcription with CTranslate2
essentia - C++ library for audio and music analysis, description and synthesis, including Python bindings
PaddleSpeech - Easy-to-use Speech Toolkit including Self-Supervised Learning model, SOTA/Streaming ASR with punctuation, Streaming TTS with text frontend, Speaker Verification System, End-to-End Speech Translation and Keyword Spotting. Won NAACL2022 Best Demo Award.
kapre - kapre: Keras Audio Preprocessors
piper - A fast, local neural text to speech system
beets - music library manager and MusicBrainz tagger
whisper - Robust Speech Recognition via Large-Scale Weak Supervision
audioread - cross-library (GStreamer + Core Audio + MAD + FFmpeg) audio decoding for Python