gabrielmittag/NISQA
NISQA - Non-Intrusive Speech Quality and TTS Naturalness Assessment
This helps you objectively assess the quality of speech from calls, video conferences, or recordings, and the naturalness of computer-generated voices like Siri or Alexa. You input audio files, and it outputs scores for overall quality, noisiness, coloration, discontinuity, loudness, or naturalness. This is for professionals in telecommunications, speech technology, or product development who need to evaluate audio quality.
917 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need an automated way to rate the sound quality of human speech after it's gone through a communication system or to assess how natural synthesized speech sounds.
Not ideal if you're looking for a tool to transcribe speech, identify speakers, or analyze the content of conversations.
Stars
917
Forks
150
Language
Python
License
MIT
Category
Last pushed
Dec 01, 2024
Commits (30d)
0
Dependencies
13
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/voice-ai/gabrielmittag/NISQA"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
yeyupiaoling/MASR
Pytorch实现的流式与非流式的自动语音识别框架，同时兼容在线和离线识别，目前支持Conformer、Squeezeformer、DeepSpeech2模型，支持多种数据增强方法。
shivammehta25/Matcha-TTS
[ICASSP 2024] 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching
coqui-ai/TTS
🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production
DigitalPhonetics/IMS-Toucan
Controllable and fast Text-to-Speech for over 7000 languages!
shivammehta25/Neural-HMM
Neural HMMs are all you need (for high-quality attention-free TTS)