Text-to-speech (TTS) processing

1. A computer-implemented method for generating audio data corresponding to different vocal attributes, the method comprising:

generating, using a speech model and input text data, first audio output data corresponding to a first vocal attribute, wherein generating the first audio output data using the speech model comprises:

generating, using a conditioning model, conditioning data using input text metadata, the conditioning data corresponding to at least one of pitch, rate, and volume,

generating, using a sample model, audio sample data corresponding to the input text data and conditioning data, and

generating, using an output model and a first sub-model corresponding to the first vocal attribute, audio output data using the audio sample data, the audio output data corresponding to a response to a query corresponding to the input text data, wherein the first vocal attribute includes at least one of a style, accent, tone, and language; and

receiving a request to change from the first vocal attribute to a second vocal attribute;

determining that a second sub-model corresponds to the second vocal attribute;

selecting a second speech model including the sample model, the conditioning model, the output model, and the second sub-model; and

generating, using the second speech model, second audio output data corresponding to the second vocal attribute.

2. The computer-implemented method of claim 1 , further comprising:

deleting the first sub-model;

adding the second sub-model in place of the first sub-model;

holding values of nodes of the speech model constant; and

during training of the second sub-model, allowing values of nodes of the second sub-model to vary,

wherein training the second sub-model occurs after a runtime period of the first sub-model.

3. The computer-implemented method of claim 1 , further comprising:

receiving a first request to generate the first audio output data corresponding to the first vocal attribute;

selecting, based on the first request, the first sub-model;

receiving a second request to generate the second audio output data corresponding to the second vocal attribute; and

selecting, based on the second request, the second sub-model.

4. The computer-implemented method of claim 1 , further comprising:

performing, by the sample model, a 2×1 dilated convolution of the input text data; and

combining, by the sample model, prosody data with an output of the 2×1 dilated convolution,

wherein the prosody data corresponds to the first vocal attribute.

5. A computer-implemented method comprising:

receiving text data;

receiving text metadata corresponding to the text data;

generating, using the text metadata and a conditioning model, conditioning data;

generating, using the text data, the conditioning data, a first sub-model of a speech model, and the speech model, first audio output data corresponding to a first vocal attribute;

receiving a request to change from the first vocal attribute to a second vocal attribute;

determining that a second sub-model of the speech model corresponds to the second vocal attribute; and

generating, using second text data, second conditioning data, the second sub-model, and the speech model, second audio output data corresponding to the second vocal attribute.

6. The computer-implemented method of claim 5 , further comprising:

receiving training data corresponding to the second vocal attribute; and

training, using the training data, the second sub-model.

7. The computer-implemented method of claim 6 , further comprising:

during training the second sub-model, holding values corresponding to nodes of the speech model constant.

8. The computer-implemented method of claim 5 , wherein generating the second audio output data further comprises:

performing, using the second sub-model, an affine transformation on an output of the speech model.

9. The computer-implemented method of claim 5 , wherein generating the second audio output data further comprises:

performing, using the speech model, a dilated convolution operation on the text data; and

performing, using the second sub-model, a speaker transform operation on a result of the dilated convolution operation.

10. The computer-implemented method of claim 5 , wherein generating the conditioning data further comprises:

generating, using the second sub-model, modified output data of the conditioning model.

11. The computer-implemented method of claim 5 , further comprising selecting at least a part of the conditioning model as the second sub-model.

12. The computer-implemented method of claim 5 , further comprising:

receiving second text metadata corresponding to a third vocal attribute;

generating, using the second text metadata and the conditioning model, second conditioning data; and

generating, using third text data, the second conditioning data, the second sub-model, and the speech model, third audio output data corresponding to the third vocal attribute.

13. A system comprising:

at least one processor; and

at least one memory including instructions that, when executed by the at least one processor, cause the system to:

receive text data;

receive text metadata corresponding to the text data;

generate, using the text metadata and a conditioning model, conditioning data;

generate, using the text data, the conditioning data, a first sub-model of a speech model, and the speech model, first audio output data corresponding to a first vocal attribute;

receive a request to change from the first vocal attribute to a second vocal attribute

determine that a second sub-model of the speech model corresponds to the second vocal attribute; and

generate, using second text data, second conditioning data, the second sub-model, and the speech model, second audio output data corresponding to the second vocal attribute.

14. The system of claim 13 , wherein the memory further comprises instructions that, when executed by the at least one processor, further cause the system to:

receive training data corresponding to the second vocal attribute; and

train, using the training data, the second sub-model.

15. The system of claim 13 , wherein the memory further comprises instructions that, when executed by the at least one processor, further cause the system to:

during training the second sub-model, hold values corresponding to nodes of the speech model constant.

16. The system of claim 13 , wherein the memory further comprises instructions that, when executed by the at least one processor, further cause the system to:

perform, using the second sub-model, an affine transformation on an output of the speech model.

17. The system of claim 13 , wherein the memory further comprises instructions that, when executed by the at least one processor, further cause the system to:

perform, using the speech model, a dilated convolution operation on the text data; and

perform, using the second sub-model, a speaker transform operation on an output of the dilated convolution.

18. The system of claim 13 , wherein the memory further comprises instructions that, when executed by the at least one processor, further cause the system to:

generate, using the second sub-model, modified output data of the conditioning model.

19. The system of claim 13 , wherein the memory further comprises instructions that, when executed by the at least one processor, further cause the system to select at least a part of the conditioning model as the second sub-model.

20. The system of claim 13 , wherein the memory further comprises instructions that, when executed by the at least one processor, further cause the system to:

receive second text metadata corresponding to a third vocal attribute;

generate, using the second text metadata and the conditioning model, second conditioning data; and

generate, using third text data, the second conditioning data, the second sub-model, and the speech model, third audio output data corresponding to the third vocal attribute.