Add link to ClearerVoice-Studio paper and Github, text-to-speech task (#3)

- Add link to ClearerVoice-Studio paper and Github, text-to-speech task (ace84d80e2d9fa407911306f88365706de005762)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -2,13 +2,14 @@
 license: apache-2.0
 task_categories:
 - audio-to-audio
+- text-to-speech
 tags:
 - audio-super-resolution
 ---
 
 # LJSpeech-1.1 High-Resolution Dataset (48,000 Hz)
 
-This dataset was created using the method described in [HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial Network for High-Fidelity Speech Super-Resolution](https://huggingface.co/papers/2501.10045).
+This dataset was created using the method described in [HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial Network for High-Fidelity Speech Super-Resolution](https://huggingface.co/papers/2501.10045) and is part of [ClearerVoice-Studio: Bridging Advanced Speech Processing Research and Practical Deployment](https://huggingface.co/papers/2506.19398) ([Github](https://github.com/modelscope/ClearerVoice-Studio)).
 
 The LJSpeech-1.1 dataset, widely recognized for its utility in text-to-speech (TTS) and other speech processing tasks, has now been enhanced through a cutting-edge speech 
 super-resolution algorithm. The original dataset, which featured a sampling rate of 22,050 Hz, has been upscaled to 48,000 Hz using [**ClearerVoice-Studio**](https://github.com/modelscope/ClearerVoice-Studio), providing a high-fidelity version suitable 
@@ -80,4 +81,4 @@ The dataset retains the original LJSpeech-1.1 structure, ensuring ease of use:
 
 The LJSpeech-1.1 High-Resolution Dataset is released under the same open license as the original LJSpeech-1.1 dataset (https://keithito.com/LJ-Speech-Dataset/). Users are free to use, modify, and share the dataset for academic and non-commercial purposes, provided proper attribution is given.
 
-[1] Shengkui Zhao, Kun Zhou, Zexu Pan, Yukun Ma, Chong Zhang, Bin Ma, "[HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial Network for High-Fidelity Speech Super-Resolution](https://arxiv.org/abs/2501.10045)", ICASSP 2025.
+[1] Shengkui Zhao, Kun Zhou, Zexu Pan, Yukun Ma, Chong Zhang, Bin Ma, "[HiFi-SR: A Unified Generative Transformer-Convolutional Adversarial Network for High-Fidelity Speech Super-Resolution](https://arxiv.org/abs/2501.10045)", ICASSP 2025.