Sarir

Small Data Generative AI for Culturally Specific Artistic Creation: A Case Study on the Sonic Space of sarīr

ISEA 2026 Anonymous Submission


1. Results from Open-Source Text-to-Audio Models


AudioLDM2 Stable Audio Open AudioGen
prompt: "Arabic calligraphy sarir."
prompt: "Persian calligraphy sarir."
prompt: "The creaking sound of a reed pen during Arabic calligraphy writing."
prompt: "The creaking sound of a reed pen during Persian calligraphy writing."
prompt: "The sound of a reed pen writing in Arabic calligraphy."
prompt: "The sound of a reed pen writing in Persian calligraphy."
prompt: "صدای قلم نی هنگام خوشنویسی"

2. Audio and Spectrogram Samples from the sarīr Dataset

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3. Results from RAVE Model Training

3.1 Generated Samples from RAVE v2 Configuration+Prior (our preferred results)

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3.2 Generated Samples from RAVE v2 Configuration

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3.3 Generated Samples from RAVE v2-small Configuration

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3.4 Generated Samples from RAVE v1 Configuration

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4. Results from StyleGAN2 Model Training

4.1 Generated Spectrograms → Audio Reconstruction → RAVE Correction

Example 1 Generated Spectrogram Reconstructed Audio RAVE-Corrected Audio
Example 2 Generated Spectrogram Reconstructed Audio RAVE-Corrected Audio
Example 3 Generated Spectrogram Reconstructed Audio RAVE-Corrected Audio
Example 4 Generated Spectrogram Reconstructed Audio RAVE-Corrected Audio
Example 5 Generated Spectrogram Reconstructed Audio RAVE-Corrected Audio
Example 6 Generated Spectrogram Reconstructed Audio RAVE-Corrected Audio

4.2 StyleGAN2 Latent Space Interpolation


4.3 Generated Samples Based on Latent Space Interpolation Using Concatenative algorithm

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5. Artistic Integration