Spark TTS: Arabic fine-tuning and local voice app setup

This Arabic-language tutorial, uploaded June 11, 2026, follows Arabic fine-tuning of Spark TTS with an Unsloth-based notebook in Google Colab. The speaker reports completing the experiment on Colab's free T4 GPU. Training happens in the cloud; the later section uses downloaded model files in a local browser app.

The explanation starts with paired recordings and transcripts. The speaker stresses clean audio, a consistent speaker, and text that matches pronunciation, including written-out numbers and Arabic spellings of spoken English words. The notebook downloads an Arabic dataset from Hugging Face, converts audio into tokens, and includes an optional audio check before training. The demonstrated one-epoch run finishes 1,188 steps in roughly 75 minutes, then saves the model to Hugging Face.

The examples expose a practical limitation: the training text contains Arabic diacritics. A familiar training sentence sounds better than a new sentence, while text without diacritics produces unclear speech. These are the speaker's sample results, not evidence of reliable Arabic synthesis across inputs.

OpenCode helps build a Python web interface around the saved model and its dependencies. The speaker says it can work offline after setup. An unnamed open source dataset tool then demonstrates audio splitting, transcript review and CSV/WAV export. Its local transcription option uses Whisper large-v3; setup requires Python and FFmpeg, with CUDA/GPU selected in the demo. Groq provides an optional hosted transcription route.