titaniumbones/whisper-small-mt-commonvoice

openai/whisper-small fine-tuned on Common Voice 26.0 — Maltese

openai/whisper-small fine-tuned for Maltese (mt) automatic speech recognition on Mozilla Common Voice Scripted Speech 26.0, trained on a laptop (Apple M3 Pro, PyTorch MPS backend, fp32).

Results

Model	Raw WER	Normalized WER	Test clips
whisper-base fine-tuned	105.7%	94.1%	300
whisper-base zero-shot	107.6%	134.7%	300
whisper-small fine-tuned	46.7%	38.9%	300
whisper-small zero-shot	110.1%	100.6%	300

Zero-shot Whisper barely functions on Maltese (≈1 h of it in the original training mix); the WER delta above is what a few hours of community-donated Common Voice speech and a laptop fine-tune buy. WER computed on the official Common Voice test split (speaker-disjoint from training data — note the training split draws on only 6 speakers, which limits generalization; see Limitations). "Normalized" applies BasicTextNormalizer (lowercase, strip punctuation) to both reference and hypothesis.

Data provenance

• Source: Mozilla Common Voice Scripted Speech 26.0, Maltese (mt), obtained via the Mozilla Data Collective (since October 2025 the exclusive distribution channel for Common Voice datasets — the former mozilla-foundation/common_voice_* datasets on this Hub are empty shells).
• License: the underlying speech data is CC0.
• Used here: 3000 training clips (~3.77 h, 6 speakers), 200 dev clips, 300 test clips, official splits, clips 1–15 s.
• The prepared audio dataset is deliberately not re-uploaded to this Hub; get the data from the Mozilla Data Collective.

Training

Standard Hugging Face Seq2SeqTrainer recipe (fine-tune-whisper): batch 4 × grad-accum 4, lr 1e-05, warmup 50, 1000 steps, fp32 (MPS), gradient checkpointing, greedy decoding, best-checkpoint-by-WER selection.

Limitations

• Single low-resource language, ~3.77 h of scripted (read) speech from only 6 speakers (the corpus's many one-clip contributors are concentrated in the held-out splits) — expect degradation on spontaneous/conversational audio and unseen voices.
• Small test set (300 clips); WER has meaningful variance.
• No punctuation/casing guarantees; laptop-scale training run, not a production model.

Usage

from transformers import pipeline
asr = pipeline("automatic-speech-recognition", model="titaniumbones/whisper-small-mt-commonvoice")
print(asr("clip.mp3", generate_kwargs={"language": "maltese"}))