What open source speech synthesis data sets are there and how to use them in a standardized way?

Speech synthesis datasets are an important resource for training and evaluating speech synthesis models. Currently, there are many open source speech synthesis datasets available. Here are some common open source speech synthesis datasets:

CommonVoice: CommonVoice is an open source speech synthesis dataset created and maintained by Mozilla. It contains a large number of speech samples from different languages and accents from around the world, which can be used to train and evaluate speech synthesis models.

VCTK-Corpus: VCTK-Corpus is an open source dataset for speech synthesis research, containing speech samples from different languages such as English, French, and German. This dataset provides high-quality recordings and annotation information and is suitable for various speech synthesis tasks.

WSJ0-2mix: WSJ0-2mix is an open source dataset for text-to-speech conversion, based on a Wall Street Journal article. This dataset contains a variety of different speaking styles and accents and can be used to train and evaluate text-to-speech models.

TIMIT: TIMIT is an open source dataset for speech recognition and speech synthesis research. It contains 6,300 sentences and corresponding sound waveforms from American English, as well as corresponding annotation information. This dataset is widely used in speech recognition and speech synthesis tasks.

UrbanSound8K: UrbanSound8K is an open source dataset for sound recognition in urban environments. It contains 8732 sound clips from urban environments, covering various traffic sounds, human voices, musical instruments, etc. This dataset is suitable for urban environment sound recognition and classification tasks.

MUSAN: MUSAN is an open source dataset for music audio analysis. It contains a large number of audio clips from different music styles and can be used for tasks such as music classification and music recommendation. This dataset provides high-quality audio samples and corresponding annotation information.

ESC-50: ESC-50 is an open source dataset for emotion recognition. It contains 50 news broadcasts from the European Broadcasting Union covering different emotion categories. This dataset is suitable for emotion recognition and sentiment analysis tasks.

There are some guidelines to follow when using these open source speech synthesis datasets:

Data quality: Make sure the recordings in your dataset are of good quality, with no noticeable noise or distortion. At the same time, the annotation information should be accurate for accurate training and evaluation.

Data diversity: Data sets should contain speech samples from different languages, accents, and speaking styles to cover a wide range of application scenarios. This can improve the generalization ability and adaptability of the model.

Data privacy: When using speech synthesis data sets provided by others, you need to ensure data privacy protection. If the data set contains personally identifiable or sensitive information, appropriate anonymization is required.

Data license: When using open source speech synthesis data sets, you need to comply with the corresponding license agreement. Typically, open source datasets provide a clear license agreement, including scope of use, modification and distribution restrictions, etc.

In summary, open source speech synthesis datasets provide researchers and developers with valuable resources to help them train and evaluate speech synthesis models. When using these data sets, you need to follow relevant specifications to ensure data quality and privacy protection, and comply with the corresponding license agreement.