Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper investigates fine-tuning strategies for Whisper models on Pashto ASR, a language absent from Whisper's pre-training data. The authors compared vanilla fine-tuning, LoRA, frozen-encoder, and Urdu-to-Pashto transfer learning, finding that vanilla fine-tuning significantly outperforms the other methods. Scaling to whisper-small and whisper-large-v3-turbo on a larger dataset showed diminishing returns, suggesting whisper-small is the practical optimum for Pashto ASR at this scale.
Naive fine-tuning of Whisper beats more parameter-efficient methods by a mile for low-resource Pashto ASR, highlighting the importance of full model adaptation when pre-training is lacking.
Pashto is absent from Whisper's pre-training corpus despite being one of CommonVoice's largest language collections, leaving off-the-shelf models unusable: all Whisper sizes output Arabic, Dari, or Urdu script on Pashto audio, achieving word error rates above 100%. We compare four fine-tuning strategies for whisper-base on CommonVoice Pashto v20: vanilla full fine-tuning, LoRA (rank 64), frozen-encoder (2/6 layers), and multistage Urdu-to-Pashto transfer. We extend vanilla fine-tuning to whisper-small and whisper-large-v3-turbo on CommonVoice Pashto v24 (113 hours). Vanilla fine-tuning achieves WER 21.22% on CV20, outperforming LoRA by 33.36 pp, frozen-encoder by 14.76 pp, and Urdu transfer by 44.56 pp. Frozen-encoder fine-tuning degrades performance on whisper-base (6 encoder layers): layer-function separation does not hold at this depth, and freezing removes a third of trainable capacity. Urdu-to-Pashto transfer fails due to an unverified intermediate checkpoint, phonological mismatch, and insufficient training. On CV24, whisper-small achieves WER 24.89% (2.24 pp over whisper-base at 3.3x parameters); whisper-large-v3-turbo achieves 23.37% (a further 1.52 pp). Diminishing returns indicate whisper-small is the practical optimum at 113 hours. Online augmentation provides 7.25 pp WER benefit over matched training. Error analysis identifies word-final suffix confusion (masculine -ay vs. feminine -a) and retroflex substitutions involving the Pashto-unique consonant /ts/ as dominant failure modes. Fine-tuned checkpoints and evaluation scripts are released on HuggingFace.