---
license: mit
---

# DreamVoice Dataset

## Introduction

The DreamVoice dataset is designed to support research in text-guided voice conversion and synthetic voice generation. The dataset is structured into three main files:

- [dreamvoice_s1_raw_label.csv](https://huggingface.co/datasets/Higobeatz/DreamVoiceDB/blob/main/dreamvoice_s1_raw_label.csv): Contains raw keyword votes from multiple experts. Each row represents the collective opinions of experts regarding various aspects of the speaker's voice characteristics.
- [dreamvoice_s2_keyword.csv](https://huggingface.co/datasets/Higobeatz/DreamVoiceDB/blob/main/dreamvoice_s2_keyword.csv): Provides verified keywords for each speaker, serving as a reliable reference for their voice profile based on expert consensus.
- [dreamvoice_s3_synthetic_prompt.csv](https://huggingface.co/datasets/Higobeatz/DreamVoiceDB/blob/main/dreamvoice_s3_synthetic_prompt.csv): Includes synthetic timbre description prompts. These prompts are designed to describe the unique timbre characteristics of each speaker, facilitating the generation of synthetic voices that match specific timbral qualities.

## Usage

To use the DreamVoice dataset in your research or projects, follow these steps:

1. **Download VCTK and LibriTTS-R Datasets**  
   The DreamVoice dataset is built to work with the VCTK and LibriTTS-R speech datasets. You can download them from the following links:
   - [VCTK Dataset](https://datashare.ed.ac.uk/handle/10283/2651)
   - [LibriTTS-R Dataset](https://openslr.org/141)

2. **Working with the Dataset**  
   Once the datasets are downloaded, you can combine them with the DreamVoice dataset based on the column speaker_id.


## Dataset Details

#### Survey Method
<img src="./data/survey_method.png" alt="Schematic diagram of DreamVoiceDB survey method" width="600"/>

*Figure 1: Schematic diagram of DreamVoiceDB survey method.*


### Annotation Strategy

- **Datasets Used**: LibriTTS-R and VCTK
- **Number of Speakers**: 900 [100 from VCTK and 800 from LibriTTS-R, sampled randomly]
- **Keyword Selection Process**:
  - Guided by an expert voice actor
  - Total of 10 keywords identified
  - Keywords categorized based on relative objectivity and subjectivity
- **Annotator Profile**:
  - Total Annotators: 8 (4 females and 4 males)
  - Expertise: Speech pathology, accent coaching, singing coaching, transcription, and translation

You can find our sample survey page used for data collection here - [DreamVoiceDB Survey Link](https://sm7orzxyhu.cognition.run/)

### Keywords and Descriptors

The DreamVoiceDB dataset utilizes a variety of keywords and descriptors to annotate voice timbre. These are grouped into relatively objective and subjective categories, along with additional aspects related to suitability for various voice-related professions.

### Relatively Objective Keywords [Multtple Options Single Choice]
- **Age**: 'Teenager or Young', 'Adult or Neutral', and 'Senior or Old'.
- **Gender**: 'Definitely Female', 'Somewhat Ambiguous / Neutral'.
- **Brightness**: Ranging from 'Very Dark (Rich)' to 'Very Bright (Thin)'.
- **Roughness**: From 'Rough (Raspy)' to 'Smooth (Mellow)'.

Reference Audio can be found in the following powerpoint presentation: [DreamVoiceDB Reference Audio](./data/reference_audio.pptx)

### Relatively Subjective Keywords [Multiple Options Multiple Choice]
- **Nasal**: A noticeable resonance or vibration in the nasal passages.
- **Breathy**: You can hear the flow of air as the person speaks.
- **Weak**: Lack of energy and confidence.
- **Strong**: Full, resonant, and confident.
- **Cute**: Perceived as young and endearing.
- **Attractive**: A voice that draws pleasure in listeners.
- **Cool**: Laid-back, confident, and collected.
- **Warm**: A voice that conveys kindness, sincerity, and comfort.
- **Authoritative**: A voice that carries weight and command.
- **Others**: Other characteristics but not related to the speaker's emotion.

### Additional Aspects
This includes evaluating the voice's fit for professions such as singing, acting, public speaking, and other vocations where vocal qualities are paramount. Specific examples include:
- **Public Presentation**: Public Speaker, Motivational Speaker, Teacher, Tour Guide
- **Storytelling**: Podcaster, Audiobook Narrator
- **Client and Public Interaction**: Customer Service, Healthcare Communicator, Retail Associate, Receptionist
- **Diplomacy and Judiciary**: Diplomat, Judge
- None of the above

Each keyword and descriptor plays a crucial role in accurately capturing and conveying the unique characteristics of each voice sample in the dataset.

### Analysis and Enhancement

- Agreement scores used to prioritize keywords
- Rigorous reassessment for moderately agreed keywords
- For each speaker we atleast have age and gender keywords
- Other keywords are selected based on the Agreement scores
- We do not use any fine grained annotations for darkness and roughness in the model training
- Use of OpenAI's GPT4 for natural language descriptors
- Generation of descriptors based on all keyword combinations


Generated Descriptors Examples:

| Speaker ID | WAV File        | Keywords              | Prompt                                              |
|------------|-----------------|-----------------------|-----------------------------------------------------|
| 2562       | [File 2562](#)  | Male, Adult, Dark     | An adult male voice, with a dark timbre.            |
| 30         | [File 30](#)    | Female, Teenager, Bright | A teenage girl's voice, radiating brightness and energy. |
| 2156       | [File 2156](#)  | Male, Senior, Strong  | A senior male voice, characterized by strength and power. |


### Dataset Statistics

![Distribution](./data/stats/combined_all_pie_charts_single_row.png)
![HeatMaps](./data/stats/updated_combined_heatmaps.png)
![Keyword Distribution](./data/stats/equal_sized_combined_keywords_bar_plot.png)
*Figure 2: Dataset statistics.*