Comments (4)
Currently, our text annotations are only available for audio clips recorded with DIRECTIONAL devices. The reason for this is that we focus on annotating clear and distinct audio rather than using audio data that is not as clear, such as those from far-field recordings or in dialects. Our dataset is more focused on speaker-related tasks. If further text annotation releases, we will update the information on our website.
from 3d-speaker.
Thanks for your explanation.
Ok, maybe off topic, if not appropriate, pls close it.
I read LAURAGPT, It says the the trainning data of TTS is LibriTTS and 3D-Speaker, and copied it 2 times, so the number of samples is 5.0M.
LibriTTS train set is about 206K, and all 3D-Speaker's train set is about 643k, if count annotations, it will be less.
So the number of samples for trainning TTS is wrong? should be 500k?
from 3d-speaker.
In the experiment with LauraGPT, data from the highest quality device of 3D-Speaker Datasets was utilized, and certain data augmentation was performed. For specific data details, please refer to the original paper.
from 3d-speaker.
After double-checking with the authors, it appears that the LibriTTS data you provided seems to be smaller than expected. Additionally, we have also utilized data from aishell-1,2,3 in the TTS tasks, which was inadvertently omitted in the current preprint version of our paper. We will rectify this detail in our subsequent revisions.
from 3d-speaker.
Related Issues (20)
- 数据集 HOT 3
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- SELF-DISTILLATION NETWORK WITH ENSEMBLE PROTOTYPES: LEARNING ROBUST SPEAKER REPRESENTATIONS WITHOUT SUPERVISION HOT 2
- 流式说话人识别可以实现吗? HOT 1
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- Assertion error
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- Numbers of speakers HOT 1
- GPU requirement for sv-eres2netv2 HOT 3
- 请教language-identification语料时数问题 HOT 12
- 训练问题 HOT 3
- 请教多模态说话人日志处理问题 HOT 1
- 训练CAM++时的问题 HOT 1
- 如何使用自己的声纹文件进行模型训练 HOT 1
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from 3d-speaker.