Duplicate from AIDC-AI/CSEMOTIONS

Co-authored-by: Chenyang Lyu <ChenyangLyu@users.noreply.huggingface.co>

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+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
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+# Audio files - compressed
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NOTICE

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+Copyright (C) 2025 AIDC-AI
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License.
+You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
+
+The dataset incorporates the following third-party open source dataset:
+1. Emotional-Speech-Data (https://github.com/HLTSingapore/Emotional-Speech-Data), licensed under MIT License (https://github.com/HLTSingapore/Emotional-Speech-Data/blob/main/LICENSE, SPDX-License-Identifier: MIT). Copyright (c) 2020 HLTSingapore. This database can only be used for research purpose.
+All rights remain with the original owners.

README.md

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+---
+task_categories:
+- text-to-speech
+library_name: datasets
+license: apache-2.0
+language:
+- zh
+tags:
+- speech
+- emotional-speech
+- voice-cloning
+- mandarin
+dataset_info:
+  features:
+  - name: audio
+    dtype: audio
+  - name: text
+    dtype: string
+  - name: emotion
+    dtype: string
+  - name: speaker
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 3610108297.64
+    num_examples: 4160
+  download_size: 3077432286
+  dataset_size: 3610108297.64
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+---
+
+# CSEMOTIONS: High-Quality Mandarin Emotional Speech Dataset
+
+[Paper](https://huggingface.co/papers/2508.02038) | [Code](https://github.com/AIDC-AI/Marco-Voice)
+
+[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
+
+**CSEMOTIONS** is a high-quality Mandarin emotional speech dataset designed for expressive speech synthesis, emotion recognition, and voice cloning research. The dataset contains studio-quality recordings from six professional voice actors across seven carefully curated emotional categories, supporting research in controllable and natural language speech generation.
+
+
+## Dataset Summary
+
+- **Name:** CSEMOTIONS
+- **Total Duration:** ~10 hours
+- **Speakers:** 10 (5 male, 5 female) native Mandarin speakers, all professional voice actors
+- **Emotions:** Neutral, Happy, Angry, Sad, Surprise, Playfulness, Fearful
+- **Language:** Mandarin Chinese
+- **Sampling Rate:** 48kHz, 24-bit PCM
+- **Recording Setting:** Professional studio environment
+- **Evaluation Prompts:** 100 per emotion, in both English and Chinese
+
+## Dataset Structure
+
+Each data sample includes:
+
+- **audio**: The speech waveform (48kHz, 24-bit, WAV)
+- **transcript**: The transcribed sentence in Mandarin
+- **emotion**: One of {Neutral, Happy, Angry, Sad, Surprise, Playfulness, Fearful}
+- **speaker_id**: An anonymized speaker identifier (e.g., `S01`)
+- **gender**: Male/Female
+- **prompt_id**: Unique identifier for each utterance
+
+
+## Intended Uses
+
+CSEMOTIONS is intended for:
+
+- Expressive text-to-speech (TTS) and voice cloning systems
+- Speech emotion recognition (SER) research
+- Cross-lingual and cross-emotional synthesis experiments
+- Benchmarking emotion transfer or disentanglement models
+
+## Dataset Details
+
+| Property                | Value                                 |
+|-------------------------|---------------------------------------|
+| Total audio hours       | ~10                                   |
+| Number of speakers      | 6 (3♂, 3♀, anonymized IDs)           |
+| Emotions                | Neutral, Happy, Angry, Sad, Surprise, Playfulness, Fearful |
+| Language                | Mandarin Chinese                      |
+| Format                  | WAV, mono, 48kHz/24bit                |
+| Studio quality          | Yes                                   |
+
+| Label    | Duration | Sentences |
+| -------- | -------- | --------- |
+| Sad      | 1.73h    | 546       |
+| Angry    | 1.43h    | 769       |
+| Happy    | 1.51h    | 603       |
+| Surprise | 1.25h    | 508       |
+| Fearful  | 1.92h    | 623       |
+| Playfulness   | 1.23h    | 621       |
+| Neutral  | 1.14h    | 490       |
+| **Total**| **10.24h**| **4160**  |
+
+## Download and Usage
+
+To use CSEMOTIONS with [🤗 Datasets](https://huggingface.co/docs/datasets):
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("AIDC-AI/CSEMOTIONS")
+```
+
+## Acknowledgements
+
+We would like to thank our professional voice actors and the recording studio staff for their contributions.
+
+
+## License
+
+The project is licensed under the Apache License 2.0 (http://www.apache.org/licenses/LICENSE-2.0, SPDX-License-identifier: Apache-2.0).
+
+## 📜 Citation
+```bibtex
+@misc{tian2025marcovoicetechnicalreport,
+      title={Marco-Voice Technical Report}, 
+      author={Fengping Tian and Chenyang Lyu and Xuanfan Ni and Haoqin Sun and Qingjuan Li and Zhiqiang Qian and Haijun Li and Longyue Wang and Zhao Xu and Weihua Luo and Kaifu Zhang},
+      year={2025},
+      eprint={2508.02038},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2508.02038}, 
+}
+```
+
+## Disclaimer
+                                     
+We used compliance checking algorithms during the training process, to ensure the compliance of the trained model and dataset to the best of our ability. Due to the complexity of the data and the diversity of language model usage scenarios, we cannot guarantee that the dataset is completely free of copyright issues or improper content. If you believe anything infringes on your rights or contains improper content, please contact us, and we will promptly address the matter.
+                                     
+---

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