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npy list | ref.npy list | json dict | __key__ string | __url__ string |
|---|---|---|---|---|
[[0.333251953125,0.375732421875,0.7685546875,-0.96923828125,0.42236328125,1.3134765625,0.8837890625,(...TRUNCATED) | [[-0.86767578125,0.421142578125,-0.349853515625,0.145263671875,0.2978515625,-0.12432861328125,-1.222(...TRUNCATED) | {"__key__":"881953_00080216","annotation_scores":{"Affection":4.2266,"Age":2.4414,"Amusement":0.8633(...TRUNCATED) | 881953_00080216 | "hf://datasets/TTS-AGI/emotion-conditioning-test-dacvae@32113e5736f611a641e8d792bc9f60327a0b939b/aff(...TRUNCATED) |
[[-0.008819580078125,1.1005859375,0.091064453125,-0.6337890625,-0.480712890625,0.658203125,-0.294189(...TRUNCATED) | [[-0.00328826904296875,0.515625,0.37841796875,0.11907958984375,0.245849609375,-0.1904296875,0.514160(...TRUNCATED) | {"__key__":"334607_00109280","annotation_scores":{"Affection":3.543,"Age":3.2344,"Amusement":-0.0007(...TRUNCATED) | 334607_00109280 | "hf://datasets/TTS-AGI/emotion-conditioning-test-dacvae@32113e5736f611a641e8d792bc9f60327a0b939b/aff(...TRUNCATED) |
[[-0.0277252197265625,-0.5966796875,0.69775390625,-0.2152099609375,0.023773193359375,0.349609375,-1.(...TRUNCATED) | [[-0.8701171875,1.5576171875,0.884765625,-1.0126953125,0.261474609375,0.2164306640625,-0.86767578125(...TRUNCATED) | {"__key__":"780777_00252952","annotation_scores":{"Affection":3.3379,"Age":2.8223,"Amusement":1.208,(...TRUNCATED) | 780777_00252952 | "hf://datasets/TTS-AGI/emotion-conditioning-test-dacvae@32113e5736f611a641e8d792bc9f60327a0b939b/aff(...TRUNCATED) |
[[-0.67919921875,0.5390625,0.27294921875,0.92724609375,0.1502685546875,0.63427734375,-0.88427734375,(...TRUNCATED) | [[-0.465087890625,0.0229034423828125,-0.677734375,-0.48095703125,0.86962890625,-0.57177734375,-0.887(...TRUNCATED) | {"__key__":"458524_00184528","annotation_scores":{"Affection":3.3047,"Age":3.0723,"Amusement":0.0365(...TRUNCATED) | 458524_00184528 | "hf://datasets/TTS-AGI/emotion-conditioning-test-dacvae@32113e5736f611a641e8d792bc9f60327a0b939b/aff(...TRUNCATED) |
[[0.21337890625,0.027435302734375,0.36376953125,-0.67626953125,1.16015625,0.875,0.35791015625,-0.098(...TRUNCATED) | [[2.361328125,-1.3330078125,0.7216796875,-0.94189453125,1.2900390625,1.771484375,0.35791015625,0.166(...TRUNCATED) | {"__key__":"779627_00123223","annotation_scores":{"Affection":3.3008,"Age":2.582,"Amusement":1.7939,(...TRUNCATED) | 779627_00123223 | "hf://datasets/TTS-AGI/emotion-conditioning-test-dacvae@32113e5736f611a641e8d792bc9f60327a0b939b/aff(...TRUNCATED) |
[[-0.87890625,0.318359375,-0.27099609375,0.5556640625,0.080078125,0.07672119140625,-1.0751953125,0.4(...TRUNCATED) | [[-0.394287109375,0.24169921875,-1.015625,0.425537109375,0.52099609375,-0.2398681640625,-0.305908203(...TRUNCATED) | {"__key__":"663991_00065057","annotation_scores":{"Affection":3.2598,"Age":3.375,"Amusement":-0.0007(...TRUNCATED) | 663991_00065057 | "hf://datasets/TTS-AGI/emotion-conditioning-test-dacvae@32113e5736f611a641e8d792bc9f60327a0b939b/aff(...TRUNCATED) |
[[-0.6005859375,-0.60595703125,-0.486572265625,-0.420166015625,0.09423828125,0.2078857421875,-0.0344(...TRUNCATED) | [[-1.5390625,0.86328125,0.53173828125,-0.429931640625,-1.2080078125,0.07666015625,-0.14404296875,0.9(...TRUNCATED) | {"__key__":"1160_00110159","annotation_scores":{"Affection":3.2422,"Age":3.0742,"Amusement":0.0023,"(...TRUNCATED) | 1160_00110159 | "hf://datasets/TTS-AGI/emotion-conditioning-test-dacvae@32113e5736f611a641e8d792bc9f60327a0b939b/aff(...TRUNCATED) |
[[0.428955078125,1.5234375,-0.64697265625,0.330078125,-0.451904296875,-0.57861328125,1.26953125,1.43(...TRUNCATED) | [[1.8583984375,0.09521484375,-1.2431640625,-1.974609375,0.287841796875,-1.041015625,0.56787109375,0.(...TRUNCATED) | {"__key__":"892448_00078816","annotation_scores":{"Affection":3.2363,"Age":3.082,"Amusement":0.0096,(...TRUNCATED) | 892448_00078816 | "hf://datasets/TTS-AGI/emotion-conditioning-test-dacvae@32113e5736f611a641e8d792bc9f60327a0b939b/aff(...TRUNCATED) |
[[0.26513671875,0.1641845703125,-0.44873046875,-0.744140625,0.184326171875,0.59912109375,-0.35839843(...TRUNCATED) | [[-1.267578125,0.030242919921875,0.259033203125,-0.11798095703125,-1.20703125,-0.6044921875,0.478515(...TRUNCATED) | {"__key__":"169282_00117096","annotation_scores":{"Affection":3.2305,"Age":2.8066,"Amusement":1.8096(...TRUNCATED) | 169282_00117096 | "hf://datasets/TTS-AGI/emotion-conditioning-test-dacvae@32113e5736f611a641e8d792bc9f60327a0b939b/aff(...TRUNCATED) |
[[-1.0576171875,0.65283203125,0.2325439453125,0.1715087890625,0.12469482421875,-0.07379150390625,-0.(...TRUNCATED) | [[-0.576171875,0.27392578125,-0.00014007091522216797,-0.2196044921875,0.06072998046875,0.48657226562(...TRUNCATED) | {"__key__":"204082_00192720","annotation_scores":{"Affection":3.1934,"Age":3.0918,"Amusement":-0.000(...TRUNCATED) | 204082_00192720 | "hf://datasets/TTS-AGI/emotion-conditioning-test-dacvae@32113e5736f611a641e8d792bc9f60327a0b939b/aff(...TRUNCATED) |
End of preview.
Emotion Conditioning Test - DAC-VAE
This dataset contains emotion-ranked subsets of speech samples encoded as DAC-VAE latents.
Contents
For each of the 40 emotion categories, the top 1000 samples (ranked by annotation score) are packaged into a separate tar file. Each tar file contains triplets of files per sample:
{sample_key}.npy— audio latent (DAC-VAE encoded){sample_key}.ref.npy— speaker reference latent{sample_key}.json— metadata including text, caption, and annotation scores
Emotion Categories
Affection, Amusement, Anger, Astonishment/Surprise, Awe, Bitterness, Concentration, Confusion, Contemplation, Contempt, Contentment, Disappointment, Disgust, Distress, Doubt, Elation, Embarrassment, Emotional Numbness, Fatigue/Exhaustion, Fear, Helplessness, Hope/Enthusiasm/Optimism, Impatience and Irritability, Infatuation, Interest, Intoxication/Altered States, Jealousy & Envy, Longing, Malevolence/Malice, Pain, Pleasure/Ecstasy, Pride, Relief, Sadness, Sexual Lust, Shame, Sourness, Teasing, Thankfulness/Gratitude, Triumph
File Naming
Each tar file is named {emotion}_{count}.tar where count is the number of samples
(up to 1000).
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