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language: mt
license: apache-2.0
library_name: transformers
tags:
- whisper
- automatic-speech-recognition
- common-voice
- mozilla-data-collective
metrics:
- wer
base_model: openai/whisper-base
---
# openai/whisper-base fine-tuned on Common Voice 26.0 — Maltese
`openai/whisper-base` 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 |
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](https://datacollective.mozillafoundation.org/)
(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](https://huggingface.co/blog/fine-tune-whisper)):
batch 4 × grad-accum 4, lr 1e-05, warmup 50,
500 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
```python
from transformers import pipeline
asr = pipeline("automatic-speech-recognition", model="titaniumbones/whisper-base-mt-commonvoice")
print(asr("clip.mp3", generate_kwargs={"language": "maltese"}))
```
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