Text-Free Prosody-Aware Generative Spoken Language Modeling

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


Conditional Speech Generation Examples

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
Samples
In the first task, speech generation, MS-TLM models generate continuation of a speech prompt. In the second task, prosody continuation, the models generate prosody streams (duration and normalized F0), while the stream of units is fixed to that of the ground-truth utterance. For comparison, we additionally provide original utterances as-is and resynthesised using the HiFi-GAN vocoder used by MS-TLM.
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Sample paged based on HiFi-GAN page.