Method for model training and tone conversion, device, and medium

A method for tone conversion, comprising:

• acquiring source audio data and a tone feature of a target speaker;
• and
• acquiring a tone feature of the source audio data by a tone extraction network in a pre-trained tone conversion model;
• acquiring a semantic feature based on the tone feature and a linear spectrum corresponding to the source audio data by a tone removal network in the tone conversion model, wherein the semantic feature is a feature in the source audio data that is not related to a tone of a speaker but is related to semantic information;
• and acquiring synthesized audio data based on the semantic feature and the tone feature of the target speaker by a vocoder in the tone conversion model.

An electronic device for training a tone conversion model, at least comprising a processor and a memory, wherein the processor, when loading and running at least one computer program stored in the memory, is caused to perform:

• acquiring a sample set, wherein the sample set contains sample audio data of different speakers, each piece of the sample audio data corresponding to a piece of target audio data, the target audio data and the sample audio data having same semantic information;
• acquiring a first tone feature of any piece of the sample audio data sample audio data by a tone extraction network in an original tone conversion model;
• acquiring a first semantic feature based on the first tone feature and a linear spectrum corresponding to the sample audio data by a tone removal network in the original tone conversion model, wherein the first semantic feature is a feature in the sample audio data that is not related to a tone of the speaker but is related to the semantic information;
• and acquiring synthesized audio data based on the first semantic feature and a second tone feature of the target audio data corresponding to the sample audio data by a vocoder in the original tone conversion model;
• and
• acquiring a trained tone conversion model by training the original tone conversion model based on the target audio data and synthesized audio data corresponding to each piece of the sample audio data.