--- dataset_info: features: - name: audio1 dtype: string - name: audio2 dtype: string - name: model1 dtype: string - name: model2 dtype: string - name: Friendly_weighted_score_audio_1 dtype: float64 - name: Friendly_weighted_score_audio_2 dtype: float64 - name: Friendly_detailedResults list: - name: userDetails struct: - name: age dtype: string - name: country dtype: string - name: gender dtype: string - name: language dtype: string - name: occupation dtype: string - name: userScores struct: - name: audio_on dtype: float64 - name: global dtype: float64 - name: votedFor dtype: string - name: Natural_weighted_score_audio_1 dtype: float64 - name: Natural_weighted_score_audio_2 dtype: float64 - name: Natural_detailedResults list: - name: userDetails struct: - name: age dtype: string - name: country dtype: string - name: gender dtype: string - name: language dtype: string - name: occupation dtype: string - name: userScores struct: - name: audio_on dtype: float64 - name: global dtype: float64 - name: votedFor dtype: string splits: - name: train num_bytes: 4487816 num_examples: 4269 download_size: 407878 dataset_size: 4487816 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - text-to-speech pretty_name: Text to Audio Human Preference Benchmark tags: - t2a - text-2-audio - minimax - google-gemini-2.5-pro-tts - elevenlabs - openai - openai-gpt-4o-tts - openai-gpt-4o-mini-tts --- # Text to Audio Human Benchmark

In this dataset, ~32k human responses collected in less than 1h using the [Rapidata Python API](https://docs.rapidata.ai/mri/), accessible to anyone and ideal for large scale evaluation. The annotators were asked **Which voice is more friendly?** and **Which voice sounds more natural?** respectively.