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license: mit |
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# DreamVoice Dataset |
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## Introduction |
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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: |
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- [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. |
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- [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. |
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- [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. |
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## Usage |
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To use the DreamVoice dataset in your research or projects, follow these steps: |
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1. **Download VCTK and LibriTTS-R Datasets** |
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The DreamVoice dataset is built to work with the VCTK and LibriTTS-R speech datasets. You can download them from the following links: |
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- [VCTK Dataset](https://datashare.ed.ac.uk/handle/10283/2651) |
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- [LibriTTS-R Dataset](https://openslr.org/141) |
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2. **Working with the Dataset** |
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Once the datasets are downloaded, you can combine them with the DreamVoice dataset based on the column speaker_id. |
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## DreamVoice Dataset Details |
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#### Survey Method |
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<img src="./data/survey_method.png" alt="Schematic diagram of DreamVoiceDB survey method" width="600"/> |
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*Figure 1: Schematic diagram of DreamVoiceDB survey method.* |
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### Annotation Strategy |
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- **Datasets Used**: LibriTTS-R and VCTK |
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- **Number of Speakers**: 900 [100 from VCTK and 800 from LibriTTS-R, sampled randomly] |
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- **Keyword Selection Process**: |
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- Guided by an expert voice actor |
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- Total of 10 keywords identified |
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- Keywords categorized based on relative objectivity and subjectivity |
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- **Annotator Profile**: |
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- Total Annotators: 8 (4 females and 4 males) |
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- Expertise: Speech pathology, accent coaching, singing coaching, transcription, and translation |
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You can find our sample survey page used for data collection here - [DreamVoiceDB Survey Link](https://sm7orzxyhu.cognition.run/) |
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### Keywords and Descriptors |
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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. |
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### Relatively Objective Keywords [Multtple Options Single Choice] |
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- **Age**: 'Teenager or Young', 'Adult or Neutral', and 'Senior or Old'. |
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- **Gender**: 'Definitely Female', 'Somewhat Ambiguous / Neutral'. |
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- **Brightness**: Ranging from 'Very Dark (Rich)' to 'Very Bright (Thin)'. |
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- **Roughness**: From 'Rough (Raspy)' to 'Smooth (Mellow)'. |
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Reference Audio can be found in the following powerpoint presentation: [DreamVoiceDB Reference Audio](./data/reference_audio.pptx) |
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### Relatively Subjective Keywords [Multiple Options Multiple Choice] |
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- **Nasal**: A noticeable resonance or vibration in the nasal passages. |
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- **Breathy**: You can hear the flow of air as the person speaks. |
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- **Weak**: Lack of energy and confidence. |
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- **Strong**: Full, resonant, and confident. |
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- **Cute**: Perceived as young and endearing. |
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- **Attractive**: A voice that draws pleasure in listeners. |
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- **Cool**: Laid-back, confident, and collected. |
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- **Warm**: A voice that conveys kindness, sincerity, and comfort. |
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- **Authoritative**: A voice that carries weight and command. |
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- **Others**: Other characteristics but not related to the speaker's emotion. |
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### Additional Aspects |
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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: |
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- **Public Presentation**: Public Speaker, Motivational Speaker, Teacher, Tour Guide |
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- **Storytelling**: Podcaster, Audiobook Narrator |
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- **Client and Public Interaction**: Customer Service, Healthcare Communicator, Retail Associate, Receptionist |
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- **Diplomacy and Judiciary**: Diplomat, Judge |
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- None of the above |
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Each keyword and descriptor plays a crucial role in accurately capturing and conveying the unique characteristics of each voice sample in the dataset. |
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### Analysis and Enhancement |
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- Agreement scores used to prioritize keywords |
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- Rigorous reassessment for moderately agreed keywords |
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- For each speaker we atleast have age and gender keywords |
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- Other keywords are selected based on the Agreement scores |
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- We do not use any fine grained annotations for darkness and roughness in the model training |
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- Use of OpenAI's GPT4 for natural language descriptors |
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- Generation of descriptors based on all keyword combinations |
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Generated Descriptors Examples: |
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| Speaker ID | WAV File | Keywords | Prompt | |
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|------------|-----------------|-----------------------|-----------------------------------------------------| |
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| 2562 | [File 2562](#) | Male, Adult, Dark | An adult male voice, with a dark timbre. | |
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| 30 | [File 30](#) | Female, Teenager, Bright | A teenage girl's voice, radiating brightness and energy. | |
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| 2156 | [File 2156](#) | Male, Senior, Strong | A senior male voice, characterized by strength and power. | |
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### Dataset Statistics |
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*Figure 2: Dataset statistics.* |
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