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c2tZi9ETw1I_0_10.wav | 6.363 | 8.55 | Speech | audio/c2tZi9ETw1I_0_10_6.363_8.55.wav | |
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NrdkLZFWwr0_102_112.wav | 1.788 | 7.733 | Frying | audio/NrdkLZFWwr0_102_112_1.788_7.733.wav | |
NrdkLZFWwr0_102_112.wav | 2.128 | 2.792 | Speech | audio/NrdkLZFWwr0_102_112_2.128_2.792.wav | |
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NrdkLZFWwr0_102_112.wav | 6.946 | 7.318 | Dishes | audio/NrdkLZFWwr0_102_112_6.946_7.318.wav | |
NrdkLZFWwr0_102_112.wav | 7.642 | 7.941 | Speech | audio/NrdkLZFWwr0_102_112_7.642_7.941.wav | |
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W60T03W7gsw_150_160.wav | 3.637 | 5.78 | Alarm_bell_ringing | audio/W60T03W7gsw_150_160_3.637_5.78.wav | |
W60T03W7gsw_150_160.wav | 4.045 | 4.585 | Speech | audio/W60T03W7gsw_150_160_4.045_4.585.wav | |
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skJfvYT0zPY_282_292.wav | 6.961 | 9.996 | Cat | audio/skJfvYT0zPY_282_292_6.961_9.996.wav | |
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pUt70ybOg10_0_10.wav | 3.612 | 4.803 | Dog | audio/pUt70ybOg10_0_10_3.612_4.803.wav | |
pUt70ybOg10_0_10.wav | 5.118 | 5.368 | Dog | audio/pUt70ybOg10_0_10_5.118_5.368.wav | |
pUt70ybOg10_0_10.wav | 5.576 | 5.826 | Dog | audio/pUt70ybOg10_0_10_5.576_5.826.wav | |
pUt70ybOg10_0_10.wav | 6.383 | 6.633 | Dog | audio/pUt70ybOg10_0_10_6.383_6.633.wav | |
pUt70ybOg10_0_10.wav | 7.083 | 7.861 | Dog | audio/pUt70ybOg10_0_10_7.083_7.861.wav | |
pUt70ybOg10_0_10.wav | 8.099 | 8.683 | Dog | audio/pUt70ybOg10_0_10_8.099_8.683.wav | |
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pUt70ybOg10_0_10.wav | 9.594 | 10 | Dog | audio/pUt70ybOg10_0_10_9.594_10.0.wav | |
rzuH5Cz4nXQ_853_863.wav | 0.681 | 1.343 | Speech | audio/rzuH5Cz4nXQ_853_863_0.681_1.343.wav | |
rzuH5Cz4nXQ_853_863.wav | 2.041 | 3.613 | Blender | audio/rzuH5Cz4nXQ_853_863_2.041_3.613.wav | |
rzuH5Cz4nXQ_853_863.wav | 3.976 | 5.842 | Blender | audio/rzuH5Cz4nXQ_853_863_3.976_5.842.wav | |
rzuH5Cz4nXQ_853_863.wav | 6.496 | 8.802 | Blender | audio/rzuH5Cz4nXQ_853_863_6.496_8.802.wav | |
rzuH5Cz4nXQ_853_863.wav | 8.864 | 10 | Speech | audio/rzuH5Cz4nXQ_853_863_8.864_10.0.wav | |
aRdB8Q7b3BE_182_192.wav | 0 | 1.261 | Speech | audio/aRdB8Q7b3BE_182_192_0.0_1.261.wav | |
aRdB8Q7b3BE_182_192.wav | 1.45 | 2.194 | Speech | audio/aRdB8Q7b3BE_182_192_1.45_2.194.wav | |
aRdB8Q7b3BE_182_192.wav | 1.829 | 7.773 | Electric_shaver_toothbrush | audio/aRdB8Q7b3BE_182_192_1.829_7.773.wav | |
aRdB8Q7b3BE_182_192.wav | 2.863 | 4.754 | Speech | audio/aRdB8Q7b3BE_182_192_2.863_4.754.wav | |
aRdB8Q7b3BE_182_192.wav | 5.784 | 6.367 | Speech | audio/aRdB8Q7b3BE_182_192_5.784_6.367.wav | |
aRdB8Q7b3BE_182_192.wav | 7.041 | 7.351 | Speech | audio/aRdB8Q7b3BE_182_192_7.041_7.351.wav | |
aRdB8Q7b3BE_182_192.wav | 7.573 | 10 | Speech | audio/aRdB8Q7b3BE_182_192_7.573_10.0.wav | |
l80bnVwIkn4_23_33.wav | 2.482 | 2.81 | Dog | audio/l80bnVwIkn4_23_33_2.482_2.81.wav | |
l80bnVwIkn4_23_33.wav | 4.125 | 4.45 | Dog | audio/l80bnVwIkn4_23_33_4.125_4.45.wav | |
l80bnVwIkn4_23_33.wav | 5.273 | 5.754 | Speech | audio/l80bnVwIkn4_23_33_5.273_5.754.wav | |
l80bnVwIkn4_23_33.wav | 8.14 | 8.524 | Dog | audio/l80bnVwIkn4_23_33_8.14_8.524.wav | |
X7b5DITgHCQ_174_184.wav | 0 | 10 | Frying | audio/X7b5DITgHCQ_174_184_0.0_10.0.wav | |
X7b5DITgHCQ_174_184.wav | 0.743 | 2.551 | Speech | audio/X7b5DITgHCQ_174_184_0.743_2.551.wav | |
X7b5DITgHCQ_174_184.wav | 3.025 | 5.423 | Speech | audio/X7b5DITgHCQ_174_184_3.025_5.423.wav | |
X7b5DITgHCQ_174_184.wav | 6.107 | 9.186 | Speech | audio/X7b5DITgHCQ_174_184_6.107_9.186.wav | |
X7b5DITgHCQ_174_184.wav | 9.363 | 9.818 | Speech | audio/X7b5DITgHCQ_174_184_9.363_9.818.wav | |
OamG3HMmwEs_175_185.wav | 1.91 | 2.369 | Cat | audio/OamG3HMmwEs_175_185_1.91_2.369.wav | |
OamG3HMmwEs_175_185.wav | 2.697 | 3.367 | Speech | audio/OamG3HMmwEs_175_185_2.697_3.367.wav | |
OamG3HMmwEs_175_185.wav | 5.787 | 6.13 | Cat | audio/OamG3HMmwEs_175_185_5.787_6.13.wav | |
OamG3HMmwEs_175_185.wav | 6.501 | 6.751 | Speech | audio/OamG3HMmwEs_175_185_6.501_6.751.wav | |
OamG3HMmwEs_175_185.wav | 8.353 | 10 | Cat | audio/OamG3HMmwEs_175_185_8.353_10.0.wav | |
RrxUPpJ9zaM_84_94.wav | 1.939 | 2.267 | Speech | audio/RrxUPpJ9zaM_84_94_1.939_2.267.wav | |
RrxUPpJ9zaM_84_94.wav | 2.792 | 3.892 | Blender | audio/RrxUPpJ9zaM_84_94_2.792_3.892.wav | |
RrxUPpJ9zaM_84_94.wav | 4.191 | 10 | Blender | audio/RrxUPpJ9zaM_84_94_4.191_10.0.wav | |
yc5N7ECY3b8_18_28.wav | 0.184 | 10 | Running_water | audio/yc5N7ECY3b8_18_28_0.184_10.0.wav | |
s_O7NfbFRog_24_34.wav | 1.292 | 10 | Alarm_bell_ringing | audio/s_O7NfbFRog_24_34_1.292_10.0.wav | |
s_O7NfbFRog_24_34.wav | 3.039 | 3.586 | Speech | audio/s_O7NfbFRog_24_34_3.039_3.586.wav | |
s_O7NfbFRog_24_34.wav | 5.168 | 7.258 | Speech | audio/s_O7NfbFRog_24_34_5.168_7.258.wav | |
k-2RZwDArWw_288_298.wav | 0.64 | 2.194 | Blender | audio/k-2RZwDArWw_288_298_0.64_2.194.wav | |
k-2RZwDArWw_288_298.wav | 2.937 | 10 | Blender | audio/k-2RZwDArWw_288_298_2.937_10.0.wav | |
LU0ZWTmamKI_118_128.wav | 1.589 | 7.631 | Blender | audio/LU0ZWTmamKI_118_128_1.589_7.631.wav |
End of preview. Expand
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This dataset only contains test data, which is integrated into UltraEval-Audio(https://github.com/OpenBMB/UltraEval-Audio) framework.
python audio_evals/main.py --dataset desed --model gpt4o_audio
🚀超凡体验，尽在UltraEval-Audio🚀
UltraEval-Audio——全球首个同时支持语音理解和语音生成评估的开源框架，专为语音大模型评估打造，集合了34项权威Benchmark，覆盖语音、声音、医疗及音乐四大领域，支持十种语言，涵盖十二类任务。选择UltraEval-Audio，您将体验到前所未有的便捷与高效：
- 一键式基准管理 📥：告别繁琐的手动下载与数据处理，UltraEval-Audio为您自动化完成这一切，轻松获取所需基准测试数据。
- 内置评估利器 ⚙️：无需再四处搜寻评估工具，UltraEval-Audio内置八种常用的评估方法（如WER、WER-ZH、BLEU、G-Eval），无论是基于规则还是模型驱动，都能满足您的需求。
- 功能强大，灵活易用 🛠️：支持预览测试、随机样本、错误重试、断点重跑等功能，确保评估过程灵活可控，提升效率与准确性。
- 无缝集成自定义数据集 💼：不仅支持公开benchmark，还提供强大的自定义数据集功能，让您在各种工程场景下也能迅速应用。
- 轻松对接现有系统 🔗：具备优秀的扩展性和标准化设计，即使您已拥有一套完善的评估体系，UltraEval-Audio也能无缝对接，简化项目管理流程，输出结果统一规范。
UltraEval-Audio: 🎙️ Open-Source Speech Model Evaluation Framework, Empowering Your AI Voice Research!
One-Stop Evaluation, Time-Saving and Effortless! 🚀
UltraEval-Audio integrates 30+ benchmarks, covering speech, sound, medicine, music four major domains, supporting 10 languages, and encompassing 12 types of tasks, helping you easily master speech evaluation!
No Tedious Operations, Ready to Use Out of the Box! 🎁
- Automatic Download and Management of Benchmarks, Say Goodbye to Manual Download and Processing! 📥
- Built-in 8 Common Evaluation Methods, Including wer, wer-zh, G-Eval, Meeting Your Diverse Evaluation Needs! 🛠️
Flexible Expansion, Seamless Integration! 🔗
- Quick Integration of Internal Datasets, Custom Dataset Functionality, Making Your Evaluation More Targeted! 📊
- Easy Access to Existing Evaluation Systems, Excellent Scalability and Standardization, Allowing UltraEval-Audio to Easily Integrate into Your Evaluation Ecosystem! 🧩
Experience Now: https://github.com/OpenBMB/UltraEval-Audio 🌐
UltraEval-Audio: 🎙️ Your Best Partner in AI Voice Research, Accelerating Your Breakthroughs in Speech Technology! 🚀
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