Giant-scale language fashions have made vital progress in generative duties involving multiple-speaker speech synthesis, music era, and audio era. The mixing of speech modality into multimodal unified giant fashions has additionally change into widespread, as seen in fashions like SpeechGPT and AnyGPT. These developments are largely as a consequence of discrete acoustic codec representations used from neural codec fashions. Nevertheless, it poses challenges in bridging the hole between steady speech and token-based language fashions. Whereas present acoustic codec fashions supply good reconstruction high quality, there’s room for enchancment in areas like excessive bitrate compression and semantic depth.
Present strategies concentrate on three essential areas to handle challenges in acoustic codec fashions. The primary technique consists of higher reconstruction high quality by strategies like AudioDec, which demonstrated the significance of discriminators, and DAC, which improved high quality utilizing strategies like quantizer dropout. The second technique makes use of enhanced compression-led developments equivalent to HiFi-Codec’s parallel GRVQ construction and Language-Codec’s MCRVQ mechanism, attaining good efficiency with fewer quantizers for each. The final technique goals to deepen the understanding of codec area with TiCodec modeling time-independent and time-dependent data, whereas FACodec separates content material, type, and acoustic particulars.
A workforce from Zhejiang College, Alibaba Group, and Meta’s Elementary AI Analysis have proposed WavTokenizer, a novel acoustic codec mannequin, that provides vital benefits over earlier state-of-the-art fashions within the audio area. WavTokenizer achieves excessive compression by decreasing the layers of quantizers and the temporal dimension of the discrete codec, with solely 40 or 75 tokens for one second of 24kHz audio. Furthermore, its design accommodates a broader VQ area, prolonged contextual home windows, improved consideration networks, a strong multi-scale discriminator, and an inverse Fourier remodel construction. It demonstrates robust efficiency, in numerous domains like speech, audio, and music.
The structure of WavTokenizer is designed for unified modeling throughout domains like multilingual speech, music, and audio. Its giant model is skilled on roughly 80,000 hours of knowledge from numerous datasets, together with LibriTTS, VCTK, CommonVoice, and so forth. Its medium model makes use of a 5,000-hour subset, whereas the small model is skilled on 585 hours of LibriTTS knowledge. The WavTokenizer’s efficiency is evaluated in opposition to state-of-the-art codec fashions utilizing official weight information from numerous frameworks equivalent to Encodec 2, HiFi-Codec 3, and so forth. It’s skilled on NVIDIA A800 80G GPUs, with enter samples of 24 kHz. The optimization of the proposed mannequin is completed utilizing the AdamW optimizer with particular studying charge and decay settings.
The outcomes demonstrated the excellent efficiency of WavTokenizer throughout numerous datasets and metrics. The WavTokenizer-small outperforms the state-of-the-art DAC mannequin by 0.15 on the UTMOS metric and the LibriTTS test-clean subset, which carefully aligns with human notion of audio high quality. Furthermore, this mannequin outperforms DAC’s 100-token mannequin throughout all metrics with solely 40 and 75 tokens, proving its effectiveness in audio reconstruction with a single quantizer. The WavTokenizer performs comparably to Vocos with 4 quantizers and SpeechTokenizer with 8 quantizers on goal metrics like STOI, PESQ, and F1 rating.
In conclusion, WavTokenizer reveals a big development in acoustic codec fashions, able to quantizing one second of speech, music, or audio into simply 75 or 40 high-quality tokens. This mannequin achieves outcomes similar to current fashions on the LibriTTS test-clean dataset whereas providing excessive compression. The workforce carried out a complete evaluation of the design motivations behind the VQ area and decoder and validated the significance of every new module by ablation research. The findings present that the WavTokenizer has the potential to revolutionize audio compression and reconstruction throughout numerous domains. Sooner or later, researchers plan to solidify WavTokenizer’s place as a cutting-edge resolution within the subject of acoustic codec fashions.
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Sajjad Ansari is a last yr undergraduate from IIT Kharagpur. As a Tech fanatic, he delves into the sensible functions of AI with a concentrate on understanding the impression of AI applied sciences and their real-world implications. He goals to articulate complicated AI ideas in a transparent and accessible method.