You can deploy this model and start earning money today!

adalat-ai/
whisper-small-ml-curated-reverse-mft-1-1-1

Name: whisper-small-ml-curated-reverse-mft-1-1-1
Availability: InStock

Views

No views yet

whisper-small-ml-curated-reverse-mft-1-1-1

Malayalam rich-transcription ASR — emits text with punctuation and numerals natively. Released alongside SCRIBE (Interspeech 2026, under review).

Model details

Architecture: Whisper-small (244M, encoder-decoder)
Base model: openai/whisper-small
Language: Malayalam (ml)
Output style: rich transcription (punctuation + formatted numerals)
Training: three-stage curriculum fine-tune on LLM-curated rich-transcription data (diversity → pace/style → precision); this is the stage-3 checkpoint.

Evaluation

Evaluated with SCRIBE. Metrics: WER (lexical, sandhi-aware), LER (legal entities), NER (numerals), PER (punctuation), TER = sum of categorical rates.

Metric	FLEURS-RO	IN22-Legal
WER (lexical)	14.77 %	19.47 %
LER (legal entities)	–	1.59 %
NER (numeral)	0.59 %	1.20 %
PER (punctuation)	14.03 %	12.96 %
TER	29.39 %	35.22 %
Sandhi resolutions	449	75

FLEURS-RO LER (0.02%) is folded into WER — too sparse on a general-domain set to warrant a row. SCRIBE WER ≠ jiwer WER: SCRIBE's WER is the lexical-category rate after sandhi-tolerant alignment, not monolithic edit distance. Paper uses ERlex / ERnum / ERpunc / ERent; this library uses WER / NER / PER / LER for the same quantities. Paper Table 1 reports the whisper-medium counterpart of this checkpoint.

Usage

python

from transformers import pipeline
asr = pipeline(
    "automatic-speech-recognition",
    model="adalat-ai/whisper-small-ml-curated-reverse-mft-1-1-1",
    generate_kwargs={"language": "ml", "task": "transcribe"},
)
print(asr("sample.wav")["text"])

CTranslate2 build: adalat-ai/ct2-whisper-small-ml-curated-reverse-mft-1-1-1-ct2-fp16.

Intended use

Malayalam dictation in legal, medical, and classroom settings where rich-transcription output is required.

License

Apache-2.0. Base model openai/whisper-small is MIT.