π
tranS dataset by FDlalala
β 26
π¬Technical Deep Dive
Full Specifications [+]
26.0
Top 1% Overall Impact
π₯ Popularity (P) 0
π Velocity (V) 0
π‘οΈ Credibility (C) 0
π§ Utility (U) 0
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The Nexus Index for tranS aggregates Popularity (P:0), Velocity (V:0), and Credibility (C:0). The Utility score (U:0) represents deployment readiness, context efficiency, and structural reliability within the Nexus ecosystem.
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Dataset Specification
VFS Recovering Reference...
license: cc-by-nc-4.0
task_categories:
- audio-classification
- automatic-speech-recognition
language: - en
tags: - conversational
- emotions
- dialogues
- conversations
pretty_name: Deep Dialogue (XTTS-v2)
size_categories: - 100K<n<1M
dataset_info: - config_name: all
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num_bytes: 22200484
num_examples: 3768
download_size: 0
dataset_size: 22200484
configs:
- name: id
- config_name: default
data_files:- split: train
path: data/train-*
- split: train
DeepDialogue-xtts
DeepDialogue-xtts is a large-scale multimodal dataset containing 40,150 high-quality multi-turn dialogues spanning 41 domains and incorporating 20 distinct emotions with coherent emotional progressions.
This repository contains the XTTS-v2 variant of the dataset, where speech is generated using XTTS-v2 with explicit emotional conditioning.
π¨ Important
This dataset is large (~180GB) due to the inclusion of high-quality audio files. When cloning the repository, ensure you have sufficient disk space and a stable internet connection.
π¬ Dataset Overview
DeepDialogue pairs 9 different language models (4B-72B parameters) to generate dialogues with emotionally coherent trajectories. Each conversation includes:
- Multi-turn dialogues (3-10 turns) between two AI agents
- Domain-specific content across 41 topics
- Emotional annotations for each utterance (20 distinct emotions)
- High-quality synthesized speech with explicit emotion conditioning
- Paired audio-text data suitable for speech and dialogue research
Emotional Speech Generation
The XTTS-v2 variant uses reference audio samples from the RAVDESS dataset to explicitly condition the speech synthesis on specific emotions, creating natural-sounding emotional expressions in the spoken dialogues.
π¦ Installation
The dataset contains large audio files and uses Git LFS. To properly clone the repository:
# Install Git LFS if you haven't already
git lfs install
Clone the repository (be prepared for a large download, ~180GB)
git clone https://huggingface.co/datasets/SALT-Research/DeepDialogue-xtts
cd DeepDialogue-xtts
You can also access specific files through the Hugging Face web interface if you don't need the entire dataset.
ποΈ Dataset Structure
The dataset is organized as follows:
data/
βββ dialogues_[model_combination]/ # Folders grouped by model pairs
β βββ [dialogue_id].json # JSON representation of the dialogue
β βββ [dialogue_id]/ # Folder containing audio files
β βββ [dialogue_id]_full.wav # Complete dialogue audio
β βββ metadata.tsv # Metadata for the audio segments
β βββ segments/ # Individual utterance audio files
β βββ [segment_id]_[speaker]_[emotion].wav
βββ train-00000-of-00001.parquet # Metadata for all dialogues
Model Combinations
The dataset includes dialogues from the following model combinations:
dialogues_cohere7B dialogues_llama3-70B_qwen2.5-72B dialogues_phi4-14B_gemma3-27B
dialogues_cohere7B_gemma3-4B dialogues_llama3-8B dialogues_phi4-mini
dialogues_gemma3-27B dialogues_llama3-8B_cohere7B dialogues_qwen2.5-32B
dialogues_gemma3-4B dialogues_llama3-8B_gemma3-4B dialogues_qwen2.5-32B_gemma3-27B
dialogues_llama3-70B dialogues_phi4-14B dialogues_qwen2.5-32B_phi4-14B
dialogues_qwen2.5-72B
Domains
The dataset covers 41 distinct domains:
topics = [
"art", "books", "cars", "celebrities", "coding", "cooking", "education", "events",
"fashion", "finance", "fitness", "food", "gaming", "gardening", "health", "history",
"hobbies", "holidays", "home", "languages", "makeup", "movies", "music", "nature",
"news", "pets", "philosophy", "photography", "podcasts", "politics", "relationships",
"science", "shopping", "social_media", "spirituality", "sports", "technology",
"traditions", "travel", "weather", "work"
]
Metadata Structure
The metadata in metadata.tsv files includes:
| Field | Description |
|---|---|
| segment_id | Unique identifier for the audio segment |
| filename | Filename of the audio segment |
| speaker | Speaker identifier (LLM1 or LLM2) |
| model | LLM model that generated this turn |
| actor | Voice actor ID from RAVDESS |
| original_text | Original text generated by the LLM |
| substituted_text | Text after any processing |
| cleaned_text | Text after cleaning for TTS |
| dialogue_emotion | Emotion label from dialogue generation |
| ravdess_emotion | Mapped emotion from RAVDESS |
| ref_audio | Reference audio file used for emotion conditioning |
| ref_transcript | Transcript of the reference audio |
| start_time | Start time in the full audio (seconds) |
| end_time | End time in the full audio (seconds) |
| duration | Duration of the segment (seconds) |
| following_silence | Silence after the segment (seconds) |
| generation_time | Time taken to generate the audio (seconds) |
| realtime_factor | Ratio of audio duration to generation time |
π Usage Examples
1. Load the Full Conversations for a Single Model
import os
import json
import pandas as pd
from glob import glob
def load_conversations_for_model(model_dir):
"""Load all conversations for a specific model combination."""
model_path = os.path.join("data", model_dir)
if not os.path.exists(model_path):
print(f"Model directory {model_dir} not found.")
return None
conversations = []
# Get all JSON files in the model directory
json_files = glob(os.path.join(model_path, "*.json"))
for json_file in json_files:
with open(json_file, 'r') as f:
conversation = json.load(f)
# Add model information
conversation['model_dir'] = model_dir
conversations.append(conversation)
print(f"Loaded {len(conversations)} conversations from {model_dir}")
return conversations
Example usage:
conversations = load_conversations_for_model("dialogues_llama3-70B")
2. Load the Full Conversations for a Single Topic/Domain
import os
import json
import pandas as pd
from glob import glob
def load_conversations_for_domain(domain, base_path="data"):
"""Load all conversations for a specific domain."""
# First, we'll use the parquet file to find conversations in this domain
parquet_path = os.path.join(base_path, "train-00000-of-00001.parquet")
if os.path.exists(parquet_path):
# Use parquet file for efficient filtering
df = pd.read_parquet(parquet_path)
domain_convs = df[df['domain'] == domain]['conversation_id'].unique()
print(f"Found {len(domain_convs)} conversations in domain '{domain}'")
# Load each conversation JSON
conversations = []
for conv_id in domain_convs:
# Find the model directory for this conversation
model_dir = df[df['conversation_id'] == conv_id]['model_dir'].iloc[0]
json_path = os.path.join(base_path, model_dir, f"{conv_id}.json")
if os.path.exists(json_path):
with open(json_path, 'r') as f:
conversation = json.load(f)
conversations.append(conversation)
return conversations
else:
# Fallback: search through all model directories
print("Parquet file not found, searching through all model directories...")
all_model_dirs = [d for d in os.listdir(base_path) if d.startswith("dialogues_")]
conversations = []
for model_dir in all_model_dirs:
model_path = os.path.join(base_path, model_dir)
json_files = glob(os.path.join(model_path, "*.json"))
for json_file in json_files:
with open(json_file, 'r') as f:
conv = json.load(f)
if conv.get('domain') == domain:
# Add model directory information
conv['model_dir'] = model_dir
conversations.append(conv)
print(f"Found {len(conversations)} conversations in domain '{domain}'")
return conversations
Example usage:
music_conversations = load_conversations_for_domain("music")
3. Load All Full Conversations
import os
import json
from glob import glob
def load_all_conversations(base_path="data"):
"""Load all conversations from all model directories."""
# Get all model directories
model_dirs = [d for d in os.listdir(base_path) if d.startswith("dialogues_")]
all_conversations = []
for model_dir in model_dirs:
model_path = os.path.join(base_path, model_dir)
json_files = glob(os.path.join(model_path, "*.json"))
for json_file in json_files:
with open(json_file, 'r') as f:
conversation = json.load(f)
# Add model information
conversation['model_dir'] = model_dir
all_conversations.append(conversation)
print(f"Loaded {len(all_conversations)} conversations from all model directories")
return all_conversations
Example usage:
all_conversations = load_all_conversations()
4. Load the Segments of a Full Conversation
import os
import pandas as pd
from IPython.display import Audio
import matplotlib.pyplot as plt
import librosa
import librosa.display
import numpy as np
def load_conversation_segments(conversation_id, model_dir, base_path="data"):
"""Load all segments of a specific conversation with metadata."""
# Path to the conversation directory
conv_dir = os.path.join(base_path, model_dir, conversation_id)
if not os.path.exists(conv_dir):
print(f"Conversation directory not found: {conv_dir}")
return None
# Load metadata
metadata_path = os.path.join(conv_dir, "metadata.tsv")
if os.path.exists(metadata_path):
metadata = pd.read_csv(metadata_path, sep='\t')
else:
print(f"Metadata file not found: {metadata_path}")
return None
# Path to segments directory
segments_dir = os.path.join(conv_dir, "segments")
# Full audio path
full_audio_path = os.path.join(conv_dir, f"{conversation_id}_full.wav")
result = {
'conversation_id': conversation_id,
'model_dir': model_dir,
'metadata': metadata,
'segments_dir': segments_dir,
'full_audio_path': full_audio_path
}
return result
def play_segment(segment_info, index):
"""Play a specific segment from a conversation."""
if segment_info is None:
return
metadata = segment_info['metadata']
if index >= len(metadata):
print(f"Segment index {index} out of range. Max index: {len(metadata)-1}")
return
filename = metadata.iloc[index]['filename']
segment_path = os.path.join(segment_info['segments_dir'], filename)
if os.path.exists(segment_path):
print(f"Playing segment {index+1}/{len(metadata)}: {filename}")
print(f"Text: \"{metadata.iloc[index]['cleaned_text']}\"")
print(f"Emotion: {metadata.iloc[index]['dialogue_emotion']}")
return Audio(segment_path)
else:
print(f"Segment file not found: {segment_path}")
def visualize_segment_waveform(segment_info, index):
"""Visualize the waveform of a specific segment."""
if segment_info is None:
return
metadata = segment_info['metadata']
if index >= len(metadata):
print(f"Segment index {index} out of range. Max index: {len(metadata)-1}")
return
filename = metadata.iloc[index]['filename']
segment_path = os.path.join(segment_info['segments_dir'], filename)
if os.path.exists(segment_path):
# Load the audio file
y, sr = librosa.load(segment_path)
# Create a figure and plot the waveform
plt.figure(figsize=(12, 4))
librosa.display.waveshow(y, sr=sr)
plt.title(f"Waveform: {filename} | Emotion: {metadata.iloc[index]['dialogue_emotion']}")
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.tight_layout()
plt.show()
else:
print(f"Segment file not found: {segment_path}")
Example usage:
segment_info = load_conversation_segments("music_85_9", "dialogues_llama3-70B")
Play a specific segment (e.g., the first one)
if segment_info:
play_segment(segment_info, 0)
visualize_segment_waveform(segment_info, 0)
# Print all segments in this conversation
print("\nAll segments in conversation:")
for i, row in segment_info['metadata'].iterrows():
print(f"{i+1}. Speaker: {row['speaker']} | Emotion: {row['dialogue_emotion']} | Text: \"{row['cleaned_text']}\"")
π Related Resources
- DeepDialogue-orpheus: The companion dataset using Orpheus TTS instead of XTTS for speech synthesis
- Project Website: Additional information and resources
π Links
- TTS model: coqui/XTTS-v2
- Emotion source: RAVDESS Dataset on Zenodo
π Citation
If you use this dataset in your research, please cite our paper:
@misc{koudounas2025deepdialoguemultiturnemotionallyrichspoken,
title={DeepDialogue: A Multi-Turn Emotionally-Rich Spoken Dialogue Dataset},
author={Alkis Koudounas and Moreno La Quatra and Elena Baralis},
year={2025},
eprint={2505.19978},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.19978},
}
π License
This dataset is licensed under the CC BY-NC-SA 4.0 International License.
π Daily sync (03:00 UTC)
AI Summary: Based on Hugging Face metadata. Not a recommendation.
π‘οΈ Dataset Transparency Report
Verified data manifest for traceability and transparency.
100% Data Disclosure Active
π Identity & Source
- id
- hf-dataset--fdlalala--trans
- source
- huggingface
- author
- FDlalala
- tags
- task_categories:audio-classificationtask_categories:automatic-speech-recognitionlanguage:enlicense:cc-by-nc-4.0size_categories:100k
βοΈ Technical Specs
- architecture
- null
- params billions
- 72
- context length
- null
π Engagement & Metrics
- likes
- 0
- downloads
- 116,391
Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)