librosa
tortoise-tts
librosa | tortoise-tts | |
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14 | 145 | |
6,699 | 11,819 | |
1.1% | - | |
7.2 | 8.0 | |
23 days ago | 1 day ago | |
Python | Jupyter Notebook | |
ISC License | Apache License 2.0 |
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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.
tortoise-tts
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ESpeak-ng: speech synthesizer with more than one hundred languages and accents
The quality also depends on the type of model. I'm not really sure what ESpeak-ng actually uses? The classical TTS approaches often use some statistical model (e.g. HMM) + some vocoder. You can get to intelligible speech pretty easily but the quality is bad (w.r.t. how natural it sounds).
There are better open source TTS models. E.g. check https://github.com/neonbjb/tortoise-tts or https://github.com/NVIDIA/tacotron2. Or here for more: https://www.reddit.com/r/MachineLearning/comments/12kjof5/d_...
- FLaNK Stack Weekly 12 February 2024
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OpenVoice: Versatile Instant Voice Cloning
I use Tortoise TTS. It's slow, a little clunky, and sometimes the output gets downright weird. But it's the best quality-oriented TTS I've found that I can run locally.
https://github.com/neonbjb/tortoise-tts
- [discussion] text to voice generation for textbooks
- DALL-E 3: Improving image generation with better captions [pdf]
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Open Source Libraries
neonbjb/tortoise-tts
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Running Tortoise-TTS - IndexError: List out of range
EDIT: It appears to be the exact same issue as this
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My Deep Learning Rig
It was primarily being used to train TTS models (see https://github.com/neonbjb/tortoise-tts), which largely fit into a single GPUs memory. So, for data parallelism, x8 PCIe isn't that much of a concern.
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PlayHT2.0: State-of-the-Art Generative Voice AI Model for Conversational Speech
Previously TortoiseTTS was associated with PlayHT in some way, although the exact connection is a bit vague [0].
From the descriptions here it sounds a lot like AudioLM / SPEAR TTS / some of Meta's recent multilingual TTS approaches, although those models are not open source, sounds like PlayHT's approach is in a similar spirit. The discussion of "mel tokens" is closer to what I would call the classic TTS pipeline in many ways... PlayHT has generally been kind of closed about what they used, would be interesting to know more.
I assume the key factor here is high quality, emotive audio with good data cleaning processes. Probably not even a lot of data, at least in the scale of "a lot" in speech, e.g. ASR (millions of hours) or TTS (hundreds to thousands). As opposed to some radically new architectural piece never before seen in the literature, there are lots of really nice tools for emotive and expressive TTS buried in recent years of publications.
Tacotron 2 is perfectly capable of this type of stuff as well, as shown by Dessa [1] a few years ago (this writeup is a nice intro to TTS concepts). With the limit largely being, at some point you haven't heard certain phonetic sounds before in a voice, and need to do something to get plausible outcomes for new voices.
[0] Discussion here https://github.com/neonbjb/tortoise-tts/issues/182#issuecomm...
[1] https://medium.com/dessa-news/realtalk-how-it-works-94c1afda...
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Comparing Tortoise and Bark for Voice Synthesis
Tortoise GitHub repo - Source code, documentation, and usage guide
What are some alternatives?
pyAudioAnalysis - Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
TTS - πΈπ¬ - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
pydub - Manipulate audio with a simple and easy high level interface
bark - π Text-Prompted Generative Audio Model
essentia - C++ library for audio and music analysis, description and synthesis, including Python bindings
Real-Time-Voice-Cloning - Clone a voice in 5 seconds to generate arbitrary speech in real-time
kapre - kapre: Keras Audio Preprocessors
piper - A fast, local neural text to speech system
beets - music library manager and MusicBrainz tagger
tacotron2 - Tacotron 2 - PyTorch implementation with faster-than-realtime inference
audioread - cross-library (GStreamer + Core Audio + MAD + FFmpeg) audio decoding for Python
larynx - End to end text to speech system using gruut and onnx