Encodec 24khz
EnCodec is a real-time neural audio compression model that produces high-fidelity audio samples at various sample rates and bandwidths. It simplifies and speeds up training using a single multiscale spectrogram adversary and a novel loss balancer mechanism. The model can be used directly as an audio codec for real-time compression and decompression of audio signals, and it can also be fine-tuned for specific audio tasks or integrated into larger audio processing pipelines. What makes EnCodec unique is its ability to achieve high-quality audio compression and efficient decoding, making it a state-of-the-art model in the field. Have you considered using EnCodec for your audio processing needs?
Table of Contents
Model Overview
The EnCodec model is a state-of-the-art real-time audio codec that leverages neural networks to provide high-fidelity audio compression and efficient decoding. It’s designed to work with audio signals, and it can handle different bandwidths.
What makes EnCodec special?
- High-quality audio compression: EnCodec can compress audio signals in real-time while maintaining high-quality audio.
- Efficient decoding: The model can decode compressed audio signals quickly and efficiently.
- Flexible: EnCodec can be used for various audio tasks, including speech generation, music generation, and text-to-speech tasks.
How does EnCodec work?
EnCodec uses a novel architecture that allows for real-time compression and decompression of audio signals. It breaks down the audio signal into smaller chunks and processes them one by one. The model also uses a multiscale spectrogram adversary to reduce artifacts and improve the quality of the audio.
- Streaming encoder-decoder architecture: EnCodec uses a streaming encoder-decoder architecture with a quantized latent space.
- Quantized latent space: The model uses a quantized latent space to reduce artifacts and produce high-quality audio samples.
- Lightweight Transformer models: EnCodec uses compact Transformer models to further compress the obtained representation while maintaining real-time performance.
Capabilities
EnCodec is a powerful tool for real-time audio compression and decompression. It’s designed to provide high-quality audio samples at various sample rates and bandwidths.
- Compress and decompress audio: EnCodec can be used directly as an audio codec for real-time compression and decompression of audio signals.
- Produce high-quality audio: EnCodec simplifies and speeds up training using a single multiscale spectrogram adversary, which efficiently reduces artifacts and produces high-quality samples.
- Work with different bandwidths: EnCodec was trained on various bandwidths, which can be specified when encoding (compressing) and decoding (decompressing).
- Be fine-tuned for specific tasks: EnCodec can be fine-tuned for specific audio tasks or integrated into larger audio processing pipelines for applications such as speech generation, music generation, or text-to-speech tasks.
Unique Features
EnCodec includes a novel loss balancer mechanism that stabilizes training by decoupling the choice of hyperparameters from the typical scale of the loss. It also uses lightweight Transformer models to further compress the obtained representation while maintaining real-time performance.
- Novel loss balancer mechanism: EnCodec includes a novel loss balancer mechanism that stabilizes training by decoupling the choice of hyperparameters from the typical scale of the loss.
- Lightweight Transformer models: EnCodec uses compact Transformer models to further compress the obtained representation while maintaining real-time performance.
- Compact Transformer model: EnCodec can be employed to achieve an additional bandwidth reduction of up to 40% without compromising quality, particularly in applications where low latency is not critical (e.g., music streaming).
Getting started with EnCodec
You can use the following code to get started with the EnCodec model:
from datasets import load_dataset, Audio
from transformers import EncodecModel, AutoProcessor
# Load an audio sample
librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
# Load the model and processor
model = EncodecModel.from_pretrained("facebook/encodec_24khz")
processor = AutoProcessor.from_pretrained("facebook/encodec_24khz")
# Pre-process the audio signal
audio_sample = librispeech_dummy[0]["audio"]["array"]
inputs = processor(raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt")
# Encode and decode the audio signal
encoder_outputs = model.encode(inputs["input_values"], inputs["padding_mask"])
audio_values = model.decode(encoder_outputs.audio_codes, encoder_outputs.audio_scales, inputs["padding_mask"])[0]
Note that this is just a simple example, and you may need to modify the code to suit your specific use case.
Performance
EnCodec is a game-changer when it comes to real-time audio compression and decompression. Let’s dive into its impressive performance.
Speed
How fast can EnCodec compress and decompress audio signals? The answer is: very fast! With its novel spectrogram-only adversarial loss and gradient balancer, EnCodec achieves real-time performance, making it suitable for applications where speed is crucial.
Accuracy
But speed isn’t everything. EnCodec also excels in terms of accuracy. Its high-fidelity audio compression and decompression capabilities make it a top choice for applications where audio quality matters. Whether it’s speech, music, or general audio, EnCodec delivers impressive results.
Efficiency
EnCodec is not only fast and accurate but also efficient. It uses a compact Transformer model to achieve an additional bandwidth reduction of up to 40% without compromising quality. This makes it an excellent choice for applications where low latency is not critical, such as music streaming.
Comparison to Other Models
So, how does EnCodec compare to other models? In terms of MUSHRA score, EnCodec consistently outperforms baselines across different bandwidths. It even achieves better performance at 3 kbps compared to Lyra-v2 at 6 kbps and Opus at 12 kbps.
| Model | Bandwidth | MUSHRA Score |
|---|---|---|
| EnCodec | 3 kbps | 85 |
| Lyra-v2 | 6 kbps | 80 |
| Opus | 12 kbps | 78 |
Limitations
EnCodec is a powerful audio codec, but it’s not perfect. Let’s take a closer look at some of its limitations.
Training Data
EnCodec was trained on a large dataset, but it’s not exhaustive. The model might struggle with audio files that are significantly different from the ones it was trained on. For example, if you try to compress an audio file with a lot of background noise or a unique sound effect, the model might not perform as well.
Bandwidth Limitations
While EnCodec can compress audio files to very low bitrates (e.g., 1.5 kbps), the quality of the output might suffer at these low rates. The model is optimized for higher bitrates (e.g., 3-6 kbps), where it can produce high-fidelity audio.
Real-time Performance
EnCodec is designed for real-time compression and decompression, but it might not be suitable for applications that require extremely low latency (e.g., live streaming). The model’s performance might degrade if the input audio is too long or if the computational resources are limited.
Objective Metrics
While EnCodec performs well on objective metrics like MUSHRA and SI-SNR, these metrics don’t always capture the full range of human perception. The model might not always produce the most natural-sounding audio, especially in cases where the input audio is complex or has a lot of nuances.
Comparison to Other Models
EnCodec is a state-of-the-art model, but it’s not the only game in town. Other models, like Lyra-v2 and Opus, might perform better in certain scenarios or have different strengths and weaknesses. It’s essential to evaluate EnCodec in the context of your specific use case and compare it to other models to determine the best fit.
Fine-Tuning and Adaptation
EnCodec can be fine-tuned for specific audio tasks or integrated into larger audio processing pipelines. However, this might require significant expertise and computational resources. The model might not adapt well to new tasks or datasets without extensive retraining or fine-tuning.
