Eugene Kharitonov*, Ann Lee*, Adam Polyak, Yossi Adi, Jade Copet, Kushal Lakhotia, Tu-Anh Nguyen,
Morgane Rivière, Abdelrahman Mohamed, Emmanuel Dupoux, Wei-Ning Hsu
Previous generative spoken language modeling work have used discrete units that discard most of the prosodic information, failing to leverage prosody for better comprehension and generation. We introduce a prosody-aware generative spoken language model (pGSLM). It is composed of a multi-stream transformer language model (MS-TLM) of speech, represented as discovered unit and prosodic feature streams, and an adapted HiFi-GAN model converting MS-TLM outputs to waveforms. Experimental results show that the pGSLM can utilize prosody to improve both prosody and content modeling, and also generate more natural, meaningful, and coherent speech given a spoken prompt. * equal contribution
In conditional generation, the system encodes a waveform prompt into a sequence of tuples of pseudo-text units, normalized F0, and duration. This sequence is then fed to the multi-stream language model (MS-TLM) from which a continuation is sampled in an auto-regressive manner. The result is then passed to the decoder to produce a new waveform. The entire pipeline is trained without supervision or text. Below are prompts, ground-truth continuations (resynthesised), and samples from our models.
|Prompt||Resynthesis||Continuous Prosody||Quantized Prosody|