BUD-E-Whisper_V1.1

BUD-E Whisper: Emotional Speech Captioning Model

BUD-E Whisper is a suite of Whisper models fine-tuned for direct emotional speech captioning. The core models are built upon OpenAI's Whisper architecture, with the current primary variant being a fine-tune of OpenAI Whisper Small. These models are designed to generate text captions that not only transcribe speech but also inherently reflect its emotional content.

V 1.1 Update: This version was finetuned on 20k samples from the Emolia dataset, that score high in the 40 categories & had been recaptioned with Gemini 2.5 Pro to describe reverb, gender & background events better.

License

This model is released under the CC-by-4.0 license. Please give attribution to Maurice Kraus & Christoph Schuhmann, who made this model.

Colab

Training Data

BUD-E Whisper was trained on a combination of:

• The Laion's Got Talent (Enhanced Flash Annotations and Long Captions) dataset.
• An internal dataset comprising approximately 5,000 hours of public Vlogs and similar audio content.

Training Procedure & Caption Generation

A key aspect of BUD-E Whisper's development was a multi-step caption refinement process to create rich training targets:

1. Initial Score Generation: An iterative process using Gemini Flash 2.0 generated initial 40-dimensional emotion scores (0-4 scale) and 15 additional dimensions like age, arousal, valence, dominance, harshness, vocalbursts,... for all audio snippets.
2. Templated Captions: These scores were converted into templated string captions.
3. Paraphrasing for Richness: Gemini Flash 2.0 was then used to paraphrase these templated captions, creating diverse and semantically rich training targets.
4. Fine-tuning: Various Whisper model sizes (including the aforementioned fine-tune of OpenAI Whisper Small) were fine-tuned on these refined, emotionally-aware captions.

This multi-step caption refinement was crucial for performance. Direct score regression or simple templated captions were found to lead to suboptimal performance for emotional speech captioning with Whisper models.

Intended Use

• Generating emotionally nuanced captions for audio content.
• Providing rich embeddings for downstream emotion recognition tasks (e.g., with Empathic Insight - Voice).