| import datasets.arrow_dataset | |
| import numpy as np | |
| import torch | |
| import transformers.pipelines.text_to_audio | |
| from datasets import load_dataset | |
| from transformers import pipeline | |
| def load_model() -> transformers.pipelines.text_to_audio.TextToAudioPipeline: | |
| """ | |
| Подгрузка модели преобразования текста в речь | |
| :return: class TextToAudioPipeline | |
| """ | |
| return pipeline("text-to-speech", "microsoft/speecht5_tts") | |
| def load_speaker_dataset() -> datasets.arrow_dataset.Dataset: | |
| """ | |
| Подгрузка датасета для озвучивания текста | |
| :return: class Dataset | |
| """ | |
| return load_dataset("Matthijs/cmu-arctic-xvectors", split="validation") | |
| def text_to_speech( | |
| text: str, | |
| synthesiser: transformers.pipelines.text_to_audio.TextToAudioPipeline, | |
| embeddings_dataset: datasets.arrow_dataset.Dataset | |
| ) -> (np.ndarray, int): | |
| """ | |
| Преобразование текста в речь | |
| :param text: Текст | |
| :param synthesiser: pipeline для озвучивания текста | |
| :param embeddings_dataset: dataset для озвучивания текста | |
| :return: tuple (audio data, sampling rate) | |
| """ | |
| speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0) | |
| speech = synthesiser(text, forward_params={"speaker_embeddings": speaker_embedding}) | |
| return speech['audio'], speech['sampling_rate'] | |