The Low Frame-rate Speech Codec is a neural audio codec that leverages finite scalar quantization and adversarial training with large speech language models to achieve high-quality audio compression with a 1.89 kbps bitrate and 21.5 frames per second.
Sample Rate
Frame Rate
Bit Rate
# Codebooks
Codebook Size
Embed Dim
FSQ Levels
22050
21.5
1.89kpbs
8
2016
32
[8, 7, 6, 6]
Model Architecture
Low Frame-rate Speech Codec model is composed of a fully convolutional generator neural network and three discriminators.
The generator comprises an encoder, followed by vector quantization, and a HiFi-GAN-based decoder.
The encoder consists of five residual blocks, each block containing three residual layers similar to the multi-receptive field fusion (MRF) module.
For the vector quantization, we have used Finite Scalar Quantization (FSQ) with eight codebooks and four dimensions per code and 2016 codes per codebook.
For the discriminators, we utilize three neural networks, all employing a squared-GAN and feature-matching loss. We adopt the multi-period discriminator and the multi-scale complex STFT discriminator.
Additionally, we proposed the use of Speech Language Models (SLMs) as a discriminator. SLMs encode information ranging from acoustic to semantic aspects, which could benefit our model's training, especially in low frame rate settings where accurate pronunciation is difficult to achieve due to the high compression rate. We adopted the 12-layer WavLM as the SLM. During training, we resample the input audio to 16 kHz before feeding it into the WavLM model, extracting the intermediary layer features. These features are then fed to a discriminative head composed of four 1D convolutional layers.
Other Properties Related to Input: 22050 Hz Mono-channel Audio
Output
Output Type: Audio
Output Format: .wav files
Output Parameters: One Dimensional (1D)
Other Properties Related to Output: 22050 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 refer to our Audio Codec Inference Tutorial, which automatically downloads the model checkpoint. Ensure that you set the model_name parameter to "nvidia/low-frame-rate-speech-codec-22khz".
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
path_to_input_audio = ??? # path of the input audio
path_to_output_audio = ??? # path of the reconstructed output audio
# load audio codec model
nemo_codec_model = AudioCodecModel.from_pretrained("nvidia/low-frame-rate-speech-codec-22khz").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)
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)
If preferred, you can manually download the checkpoint and use the provided code to run inference on the model:
import librosa
import torch
import soundfile as sf
from nemo.collections.tts.models import AudioCodecModel
codec_path = ??? # set here the model .nemo checkpoint path
path_to_input_audio = ??? # path of the input audio
path_to_output_audio = ??? # path of the reconstructed output audio
# load audio codec model
nemo_codec_model = AudioCodecModel.restore_from(restore_path=codec_path, map_location="cpu").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)
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_low_frame_rate_22050.yaml" config. You also will need to set pretrained_model_name to "audio_codec_low_frame_rate_22khz".
Training, Testing, and Evaluation Datasets:
The Low Frame-rate Speech Codec was trained on 28.7k hours of speech data spanning 105 languages. The model was evaluated using multilingual audiobook-style data and high-quality English recordings. For further details, refer to our paper.
Training Datasets
The Low Frame-rate Speech Codec is trained on a total of 28.7k hrs of speech data from 105 languages.
Properties: To assess our models' performance on studio-quality audio, we utilized the F10 and M10 speakers from the DAPS Clear dataset. These speakers were also employed in the evaluation of the DAC model.
Performance
We evaluated our codec using multiple objective audio quality metrics across two distinct test sets. Additionally, we compared our model's performance with state-of-the-art codecs. For further details, please refer to our paper.
Please note that the released checkpoint yields slightly different results compared to those reported in the paper. Due to legal data constraints, we retrained the model after removing one speaker from the training set. This retraining was performed for 170k steps, compared to the original 124k steps, leading to slight improvements across almost all metrics.
This model is for research and development only (non-commercial use) and the license to use this model is covered by the NSCLv1.
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