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Voice conversion models have developed for decades, and current mainstreamresearch focuses on non-streaming voice conversion. However, streaming voiceconversion is more suitable for practical application scenarios thannon-streaming voice conversion. In this paper, we propose a streamingany-to-many voice conversion based on fully non-autoregressive model, whichincludes a streaming transformer based acoustic model and a streaming vocoder.Streaming transformer based acoustic model is composed of a pre-trained encoderfrom streaming end-to-end based automatic speech recognition model and adecoder modified on FastSpeech blocks. Streaming vocoder is designed forstreaming task with pseudo quadrature mirror filter bank and causalconvolution. Experimental results show that the proposed method achievessignificant performance both in latency and conversion quality and can bereal-time on CPU and GPU.

Streaming non-autoregressive model for any-to-many voice conversion