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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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rzuH5Cz4nXQ_853_863.wav | 3.976 | 5.842 | Blender | audio/rzuH5Cz4nXQ_853_863_3.976_5.842.wav | |
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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 |
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This dataset only contains test data, which is integrated into UltraEval-Audio(https://github.com/OpenBMB/UltraEval-Audio) framework.
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! 🚀
UltraEval-Audio： 🎙️ 开源语音大模型评测框架，赋能您的AI语音研究！
一站式评测，省时省力！ 🚀
UltraEval-Audio 为您集成 30+ benchmark，涵盖 speech, sound, medicine, music 四大领域，支持 10种语言，覆盖 12类任务，助您轻松驾驭语音评测！
无需繁琐操作，开箱即用！ 🎁
- 自动下载和管理 benchmark，告别手动下载和处理的烦恼！ 📥
- 内置 8 种常用评测方法，包括 wer, wer-zh, G-Eval 等，满足您的多样化评测需求！ 🛠️
灵活扩展，无缝衔接！ 🔗
- 快速集成内部数据集，自定义数据集功能，让您的评测更具针对性！ 📊
- 方便接入现有评测系统，良好的扩展性和规范性，让 UltraEval-Audio 轻松融入您的评测体系！ 🧩
立即体验： https://github.com/OpenBMB/UltraEval-Audio 🌐
UltraEval-Audio： 🎙️ 您的 AI 语音研究最佳伙伴，助您加速语音技术突破！ 🚀
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