audio-codec-44khz

NVIDIA NeMo Audio Codec 44khz

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The NeMo Audio Codec is a neural audio codec which compresses audio into a quantized representation. The model can be used as a vocoder for speech synthesis.

The model works with full-bandwidth 44.1kHz speech. It might have lower performance with low-bandwidth speech (e.g. 16kHz speech upsampled to 44.1kHz) or with non-speech audio.

Model Architecture

The NeMo Audio Codec model uses symmetric convolutional encoder-decoder architecture based on HiFi-GAN. We use Finite Scalar Quantization (FSQ), with 8 codebooks and 1000 entries per codebook.

For more details please refer to our paper.

Input

• Input Type: Audio
• Input Format(s): .wav files
• Input Parameters: One-Dimensional (1D)
• Other Properties Related to Input: 44100 Hz Mono-channel Audio

Output

• Output Type: Audio
• Output Format: .wav files
• Output Parameters: One Dimensional (1D)
• Other Properties Related to Output: 44100 Hz Mono-channel Audio

How to Use this Model

The model is available for use in the NVIDIA NeMo, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Inference

For inference, you can follow our Audio Codec Inference Tutorial which automatically downloads the model checkpoint. Note that you will need to set the model_name parameter to "nvidia/audio-codec-44khz".

Alternatively, you can use the code below, which also handles the automatic checkpoint download:

import librosa
import torch
import soundfile as sf
from nemo.collections.tts.models import AudioCodecModel

model_name = "nvidia/audio-codec-44khz"
path_to_input_audio = ??? # path of the input audio
path_to_output_audio = ??? # path of the reconstructed output audio

nemo_codec_model = AudioCodecModel.from_pretrained(model_name).eval()

# get discrete tokens from audio
audio, _ = librosa.load(path_to_input_audio, sr=nemo_codec_model.sample_rate)

device = 'cuda' if torch.cuda.is_available() else 'cpu'
audio_tensor = torch.from_numpy(audio).unsqueeze(dim=0).to(device)
audio_len = torch.tensor([audio_tensor[0].shape[0]]).to(device)

with torch.no_grad():
  encoded_tokens, encoded_len = nemo_codec_model.encode(audio=audio_tensor, audio_len=audio_len)
  
  # Reconstruct audio from tokens
  reconstructed_audio, _ = nemo_codec_model.decode(tokens=encoded_tokens, tokens_len=encoded_len)

# save reconstructed audio
output_audio = reconstructed_audio.cpu().numpy().squeeze()
sf.write(path_to_output_audio, output_audio, nemo_codec_model.sample_rate)

Training

For fine-tuning on another dataset please follow the steps available at our Audio Codec Training Tutorial. Note that you will need to set the CONFIG_FILENAME parameter to the "audio_codec_44100.yaml" config. You also will need to set pretrained_model_name to "nvidia/audio-codec-44khz".

Training, Testing, and Evaluation Datasets:

Training Datasets

The NeMo Audio Codec is trained on a total of 14.2k hrs of speech data from 79 languages.

• MLS English - 12.8k hours, 2.8k speakers, English
• Common Voice - 1.4k hours, 50k speakers, 79 languages.

Test Datasets

• MLS English - 15 hours, 42 speakers, English
• Common Voice - 2 hours, 1356 speakers, 59 languages

Performance

We evaluate our codec using several objective audio quality metrics. We evaluate ViSQOL and PESQ for perception quality, ESTOI for intelligbility, mel spectrogram and STFT distances for spectral reconstruction accuracy, and SI-SDR 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.

Software Integration

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Ampere
• NVIDIA Blackwell
• NVIDIA Jetson
• NVIDIA Hopper
• NVIDIA Lovelace
• NVIDIA Pascal
• NVIDIA Turing
• NVIDIA Volta

Runtime Engine

• Nemo 2.0.0

Preferred Operating System

• Linux

License/Terms of Use

This model is for research and development only (non-commercial use) and the license to use this model is covered by the NSCLv1.

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns here.