Unnamed Skill

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name: unsloth-stt description: Fine-tuning Speech-to-Text models like Whisper using Unsloth's optimized LoRA pipeline. Triggers: stt, whisper, transcription, audio fine-tuning, speech-to-text, audio normalization.

Overview

Unsloth supports fine-tuning for Speech-to-Text (STT) models like OpenAI Whisper. By applying its optimized LoRA pipeline to Whisper architecture, Unsloth achieves 1.5x faster training with 50% less memory usage compared to standard methods.

When to Use

• When you need to capture specialized terminology (medical, legal) that base Whisper misses.
• When adapting models to specific accents or dialects.
• When fine-tuning large models (like whisper-large-v3) on limited consumer hardware.

Decision Tree

1. Is transcription accuracy low on zero-shot inference?
   • Yes: Proceed with STT fine-tuning.
2. Is audio recorded at a non-standard sample rate?
   • Yes: Resample to 16kHz before training.
3. Using large-v3?
   • Yes: Load in 4-bit and apply LoRA to cross-attention layers.

Workflows

Whisper STT Data Preprocessing

1. Load audio files using datasets.Audio feature to handle on-the-fly decoding.
2. Resample all training audio to 16kHz to avoid sample rate mismatch.
3. Normalize transcripts to remove characters not present in Whisper's vocabulary.

Fine-tuning Whisper with Unsloth

1. Load whisper-large-v3 using FastLanguageModel.from_pretrained with load_in_4bit=True.
2. Apply LoRA weights to cross-attention and self-attention layers of the encoder and decoder.
3. Train using Seq2SeqTrainer or standard Trainer if audio is flattened to tokens.

Non-Obvious Insights

• Whisper fine-tuning in Unsloth uses the same PEFT/LoRA pipeline as LLMs, which is why it can handle large models on consumer GPUs.
• Capturing vocal nuances like accents is significantly more effective through fine-tuning than zero-shot, as the model learns to map specific acoustic features to text tokens.
• Proper audio normalization (16kHz resampling) is the single most important preprocessing step; Whisper's architecture is hard-coded for this sample rate.

Evidence

• "Unsloth supports any transformers compatible TTS/STT model... 1.5x faster with 50% less memory." Source
• "Fine-tuning delivers far more accurate and realistic voice replication than zero-shot." Source

Scripts

• scripts/unsloth-stt_tool.py: Script for resampling and preprocessing audio datasets for Whisper.
• scripts/unsloth-stt_tool.js: Utility for cleaning transcription strings.

Dependencies

• unsloth
• datasets
• librosa
• transformers

References

• [[references/README.md]]