NeMo
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@@ -5,7 +5,7 @@ license_link: https://developer.nvidia.com/downloads/license/nsclv1
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- # NVIDIA NeMo Audio Codec
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@@ -23,7 +23,7 @@ The NeMo Audio Codec is a neural audio codec which compresses audio into a quant
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  ## Model Architecture
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  The NeMo Audio Codec model uses symmetric convolutional encoder-decoder architecture based on [HiFi-GAN](https://arxiv.org/abs/2010.05646).
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- For the vector quantization, we use [Finite Scalar Quantization (FSQ)](https://arxiv.org/abs/2309.15505) with eight codebooks and 1000 codes per codebook.
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  For more details please check [our paper](https://arxiv.org/abs/2406.05298).
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  ## Performance
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- We evaluate our codec using several objective audio quality metrics. We evaluate [ViSQO](https://github.com/google/visqol) and [PESQ](https://lightning.ai/docs/torchmetrics/stable/audio/perceptual_evaluation_speech_quality.html) for perception quality, [ESTOI](https://ieeexplore.ieee.org/document/7539284) for intelligbility, mel spectrogram and STFT distances for spectral reconstruction accuracy, and SI-SDR [7] for phase reconstruction accuracy. Metrics are reported on the test set for both the MLS English and CommonVoice data. The model has not been trained or evaluated on non-speech audio.
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  | Dataset | ViSQOL |PESQ |ESTOI |Mel Distance |STFT Distance|SI-SDR|
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  |:-----------:|:----------:|:----------:|:----------:|:-----------:|:-----------:|:-----------:|
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- | MLS English | 4.50 | 3.69 | 0.94 | 0.066 | 0.033 | 8.33 |
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- | CommonVoice | 4.53 | 3.55 | 0.93 | 0.100 | 0.057 | 7.63 |
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  ## Software Integration
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+ # NVIDIA NeMo Audio Codec 22khz
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  <style>
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  img {
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  ## Model Architecture
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  The NeMo Audio Codec model uses symmetric convolutional encoder-decoder architecture based on [HiFi-GAN](https://arxiv.org/abs/2010.05646).
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+ For the vector quantization, we use [Finite Scalar Quantization (FSQ)](https://arxiv.org/abs/2309.15505), with eight codebooks, and 1000 entries per codebook.
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  For more details please check [our paper](https://arxiv.org/abs/2406.05298).
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  ## Performance
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+ We evaluate our codec using several objective audio quality metrics. We evaluate [ViSQOL](https://github.com/google/visqol) and [PESQ](https://lightning.ai/docs/torchmetrics/stable/audio/perceptual_evaluation_speech_quality.html) for perception quality, [ESTOI](https://ieeexplore.ieee.org/document/7539284) for intelligbility, mel spectrogram and STFT distances for spectral reconstruction accuracy, and SI-SDR [7] for phase reconstruction accuracy. Metrics are reported on the test set for both the MLS English and CommonVoice data. The model has not been trained or evaluated on non-speech audio.
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  | Dataset | ViSQOL |PESQ |ESTOI |Mel Distance |STFT Distance|SI-SDR|
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  |:-----------:|:----------:|:----------:|:----------:|:-----------:|:-----------:|:-----------:|
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+ | MLS English | 4.50 | 3.69 | 0.94 | 0.066 | 0.033 | 8.33 |
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+ | CommonVoice | 4.53 | 3.55 | 0.93 | 0.100 | 0.057 | 7.63 |
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  ## Software Integration
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