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NISQA - Non-Intrusive Speech Quality and TTS Naturalness Assessment
| Date | Stars |
|---|---|
| 2026-07-24 | 963 |
| 2026-07-25 | 963 |
| 2026-07-28 | 963 |
| 2026-07-30 | 963 |
| 2026-08-06 | 963 |
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# NISQA: Speech Quality and Naturalness Assessment
*+++ News: The NISQA model has recently been updated to NISQA v2.0. The new version offers multidimensional predictions with higher accuracy and allows for training and finetuning the model.*
**Speech Quality Prediction:**
NISQA is a deep learning model/framework for speech quality prediction. The NISQA model weights can be used to predict the quality of a speech sample that has been sent through a communication system (e.g telephone or video call). Besides overall speech quality, NISQA also provides predictions for the quality dimensions *Noisiness*, *Coloration*, *Discontinuity*, and *Loudness* to give more insight into the cause of the quality degradation.
**TTS Naturalness Prediction:**
The NISQA-TTS model weights can be used to estimate the *Naturalness* of synthetic speech generated by a Voice Conversion or Text-To-Speech system (Siri, Alexa, etc.).
**Training/Finetuning:**
NISQA can be used to train new single-ended or double-ended speech quality prediction models with different deep learning architectures, such as CNN or DFF -> Self-Attention or LSTM -> Attention-Pooling or Max-Pooling. The provided model weights can also be applied to finetune the trained model towards new data or for transfer-learning to a different regression task (e.g. quality estimation of enhanced speech, speaker similarity estimation, or emotion recognition) .
**Speech Quality Datasets:**
We provide a large corpus of more than 14,000 speech samples with subjective speech quality and speech quality dimension labels.
## Table of Contents
- [Installation](#installation)
- [Using NISQA](#using-nisqa)
- [Prediction](#prediction)
- [Training](#training)
- [Finetuning / Transfer Learning](#finetuning--transfer-learning)
- [Training a new model](#training-a-new-model)
- [Evaluation](#evaluation)
- [NISQA Corpus](#nisqa-corpus)
- [Paper and License](#paper-and-license)
More information about the deep learning model structure, the used training datasets, and the training options, see the [NISQA paper](https://arxiv.org/abs/2104.09494) and the [Wiki](https://github.com/gabrielmittag/NISQA/wiki/).
## Installation
To install requirements install [Anaconda](https://www.anaconda.com/products/individual) and then use:
```setup
conda env create -f env.yml
```
This will create a new environment with the name "nisqa". Activate this environment to go on:
```setup2
conda activate nisqa
```
## Using NISQA
We provide examples for using NISQA to predict the quality of speech samples, to train a new speech quality model, and to evaluate the performance of a trained speech quality model.
There are three different model weights available, the appropriate weights should be loaded depending on the domain:
| Model | Prediction Output | Domain | Filename |
| --------------------- | --------------------------------------------------------------- | ------------------ | ------------------ |
| NISQA (v2.0) | Overall Quality, Noisiness, Coloration, Discontinuity, Loudness | Transmitted Speech | nisqa.tar |
| NISQA (v2.0) mos only | Overall Quality only (for finetuning/transfer learning) | Transmitted Speech | nisqa_mos_only.tar |
| NISQA-TTS (v1.0) | Naturalness | Synthesized Speech | nisqa_tts.tar |
### Prediction
There are three modes available to predict the quality of speech via command line arguments:
* Predict a single file
* Predict all files in a folder
* Predict all files in a CSV table
**Important:** Select "*nisqa.tar*" to predict the quality of a transmitted speech sample and "*nisqa_tts.tar*" to predict the Naturalness of a synthesized speech sample.
To predict the quality of a single .wav file use:
```
python run_predict.py --mode predict_file --pretrained_model weights/nisqa.tar --deg /path/to/wavExcerpt of 11,838 characters
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matched fp:cb939b4d564fddde, topic:text-to-speech, topic:tts, topic:speech-synthesis
matched fp:cb939b4d564fddde, topic:deep-learning, topic:pytorch