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Datasets:
TTS-AGI
/
Emotion-Voice-Attribute-Reference-Snippets-DACVAE-Wave

Tasks:
Text-to-Speech
Audio Classification
Modalities:
Audio
Text
Formats:
webdataset
Size:
100K - 1M
Tags:
emotion
voice-attributes
dacvae
speech
tts
audio
Libraries:
Datasets
WebDataset
License:
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Emotion-Voice-Attribute-Reference-Snippets-DACVAE-Wave / README.md
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metadata 
license: cc-by-4.0
task_categories:
  - text-to-speech
  - audio-classification
tags:
  - emotion
  - voice-attributes
  - dacvae
  - speech
  - tts
  - audio
pretty_name: Emotion and Voice Attribute Reference Snippets DACVAE and Wave
size_categories:
  - 100K<n<1M

Emotion and Voice Attribute Reference Snippets - DACVAE and Wave

Merged dataset combining TTS-AGI/enhanced-emo-snippets-balanced-DACVAE and TTS-AGI/emotion-attribute-conditioning-dacvae with decoded WAV audio.

Overview

  • Total samples: 606,178
  • Filtered out: 363,331 (samples with speech_quality < 1.8)
  • Total tar files: 328
  • Total size: 1.54 TB
  • Audio format: WAV, 48kHz, PCM 16-bit mono
  • Latents: DAC-VAE float16 [T, 128] at 25 frames/sec
  • Dimensions: 57 (40 emotions + 15 voice attributes + 2 additional attributes)

File Structure

Each tar file is named {Dimension}_{bucket_range}.tar and contains WebDataset-formatted samples:

{key}.json          # Full metadata (scores, text, captions, etc.)
{key}.target.npy    # DACVAE latent for target speech [T, 128] float16
{key}.target.wav    # Decoded target audio (48kHz WAV)
{key}.ref.npy       # DACVAE latent for speaker reference [T, 128] float16 (if available)
{key}.ref.wav       # Decoded reference audio (48kHz WAV) (if available)

Samples prefixed with emo_ come from DS1 (enhanced-emo-snippets-balanced), samples prefixed with cond_ come from DS2 (emotion-attribute-conditioning). DS2 samples include speaker reference audio (.ref.npy / .ref.wav), while DS1 samples include speaker embeddings in the JSON metadata.

Dimensions

Emotions (40)

Dimension Buckets Tar Files
Affection [0,1) to [4,5) 5
Amusement [0,1) to [4,5) 5
Anger [0,1) to [5,6) 6
Astonishment_Surprise [0,1) to [4,5) 5
Awe [0,1) to [4,5) 5
Bitterness [0,1) to [4,5) 5
Concentration [0,1) to [4,5) 5
Confusion [0,1) to [4,5) 5
Contemplation [0,1) to [3,4) 4
Contempt [0,1) to [4,5) 5
Contentment [0,1) to [3,4) 4
Disappointment [0,1) to [4,5) 5
Disgust [0,1) to [3,4) 4
Distress [0,1) to [4,5) 5
Doubt [0,1) to [4,5) 5
Elation [0,1) to [5,6) 6
Embarrassment [0,1) to [2,3) 3
Emotional_Numbness [0,1) to [3,4) 4
Fatigue_Exhaustion [1,2) to [4,5) 4
Fear [0,1) to [3,4) 4
Helplessness [0,1) to [3,4) 4
Hope_Enthusiasm_Optimism [0,1) to [6,7) 7
Impatience_and_Irritability [0,1) to [4,5) 5
Infatuation [0,1) to [4,5) 5
Interest [0,1) to [3,4) 4
Intoxication_Altered_States_of_Consciousness [0,1) to [4,5) 5
Jealousy_and_Envy [0,1) to [4,5) 5
Longing [0,1) to [3,4) 4
Malevolence_Malice [0,1) to [3,4) 4
Pain [0,1) to [5,6) 6
Pleasure_Ecstasy [0,1) to [3,4) 4
Pride [0,1) to [4,5) 5
Relief [0,1) to [5,6) 6
Sadness [0,1) to [4,5) 5
Sexual_Lust [0,1) to [4,5) 5
Shame [0,1) to [5,6) 6
Sourness [0,1) to [3,4) 4
Teasing [0,1) to [3,4) 4
Thankfulness_Gratitude [0,1) to [4,5) 5
Triumph [0,1) to [4,5) 5

Voice Attributes (15 from DS1 + 2 from DS2)

Attributes from DS1 use integer bucket ranges. Attributes from DS2 use float-valued bucket ranges derived from the conditioning pipeline.

Dimension Bucket Type Tar Files
Age Integer [0,6) + Float [0.00, 5.14) 12
Arousal Integer [0,6) + Float [0.00, 4.00) 13
Authenticity Integer [1,5) 4
Background_Noise Integer [0,3) 3
Confident_vs._Hesitant Integer [0,5) + Float [0.00, 4.00) 12
Gender Integer [0,3) + Float [0.29, 2.00) 6
High-Pitched_vs._Low-Pitched Integer [0,5) + Float [0.00, 3.43) 11
Monotone_vs._Expressive Integer [0,5) + Float [0.00, 4.00) 12
Recording_Quality Integer [0,5) 5
Serious_vs._Humorous Integer [0,6) + Float [0.00, 4.00) 13
Soft_vs._Harsh Integer [0,2) + Float [0.29, 2.00) 5
Submissive_vs._Dominant Integer [0,3) + Float [0.43, 3.00) 6
Valence Integer [0,4) + Float [0.43, 3.00) 7
Vulnerable_vs._Emotionally_Detached Integer [0,5) 5
Warm_vs._Cold Integer [0,3) + Float [0.29, 2.00) 6
duration Float [1.00, 30.00) 7
talking_speed Float [5.00, 25.00) 7

Metadata Fields

Each sample's .json contains:

From DS1 (enhanced-emo-snippets-balanced):

  • transcription — Speech transcript
  • caption, detailed_caption, bude_whisper_caption — Natural language audio descriptions
  • empathic_insight_scores — 59 float scores (40 emotions + 15 attributes + 4 quality)
  • speaker_embedding — 128-dim speaker embedding vector
  • emotion_vector — Encoded emotion vector
  • enhancement_model — Speech enhancement model used (MossFormer2_SE_48K)
  • duration — Audio duration in seconds

From DS2 (emotion-attribute-conditioning):

  • text — Speech transcript
  • caption — Natural language audio description
  • annotation_scores — 59 float scores (same dimensions as DS1)
  • target_duration, context_duration — Target and reference durations
  • speaker, language — Speaker ID and language code

Added by merge pipeline:

  • _source_dataset — "enhanced-emo-snippets-balanced" or "emotion-attribute-conditioning"
  • _dimension — The emotion/attribute dimension name
  • _bucket — The bucket label
  • has_reference — Whether reference audio is available

Quality Scores

All samples include 59 annotation scores from Empathic Insight Voice Plus:

  • 40 emotion scores: Amusement, Anger, Fear, Sadness, etc.
  • 15 attribute scores: Valence, Arousal, Age, Gender, etc.
  • 4 quality scores: score_overall_quality, score_speech_quality, score_content_enjoyment, score_background_quality

Only samples with score_speech_quality >= 1.8 are included in this dataset.

Sources

Usage 

import webdataset as wds
import numpy as np
import json, io, soundfile as sf

url = "https://huggingface.co/datasets/TTS-AGI/Emotion-Voice-Attribute-Reference-Snippets-DACVAE-Wave/resolve/main/data/Anger_4to5.tar"
ds = wds.WebDataset(url).decode()

for sample in ds:
    meta = json.loads(sample["json"])
    target_wav = sample["target.wav"]       # decoded 48kHz audio
    target_latent = np.load(io.BytesIO(sample["target.npy"]))  # [T, 128] float16

    if "ref.wav" in sample:
        ref_wav = sample["ref.wav"]         # speaker reference audio
        ref_latent = np.load(io.BytesIO(sample["ref.npy"]))    # [T, 128] float16

    # Access emotion scores
    scores = meta.get("empathic_insight_scores") or meta.get("annotation_scores", {})
    speech_quality = scores.get("score_speech_quality", 0)
    anger_score = scores.get("Anger", 0)

DACVAE Encode/Decode

Audio was decoded from DAC-VAE latents at 48kHz, 25 latent frames/sec:

import torch
from dacvae import DACVAE
from huggingface_hub import hf_hub_download

model = DACVAE.load(hf_hub_download("mrfakename/dacvae-watermarked", "weights.pth")).cuda().eval()

# Decode: latent -> audio
z = torch.from_numpy(latent.T).unsqueeze(0).float().cuda()  # [1, 128, T_latent]
audio_48k = model.decode(z).squeeze().cpu()                  # [T_audio] at 48kHz

# Encode: audio -> latent
audio = torch.from_numpy(wav).unsqueeze(0).unsqueeze(0).float().cuda()  # [1, 1, T_audio]
z_encoded = model.encode(audio)                                          # [1, 128, T_latent]
latent = z_encoded.squeeze(0).T.cpu().half().numpy()                     # [T_latent, 128] float16