  Generative models have gained more and more attention in recent years for
their remarkable success in tasks that required estimating and sampling data
distribution to generate high-fidelity synthetic data. In speech,
text-to-speech synthesis and neural vocoder are good examples where generative
models have shined. While generative models have been applied to different
applications in speech, there exists no general-purpose generative model that
models speech directly. In this work, we take a step toward this direction by
showing a single pre-trained generative model can be adapted to different
downstream tasks with strong performance. Specifically, we pre-trained a
generative model, named SpeechFlow, on 60k hours of untranscribed speech with
Flow Matching and masked conditions. Experiment results show the pre-trained
generative model can be fine-tuned with task-specific data to match or surpass
existing expert models on speech enhancement, separation, and synthesis. Our
work suggested a foundational model for generation tasks in speech can be built
with generative pre-training.
