R-MFT trains in three stages with a decreasing learning rate schedule, presenting the hardest acoustic data first during the highest-plasticity phase:

Stage	Data	LR
1	Tier C — Spontaneous (~512.5 hrs)	2e-4
2	Tier B — Broadcast (~200 hrs)	1e-4
3	Tier A — Studio + Tier C mix (~182.2 hrs)	1e-5

Training uses AdamW (weight decay 0.1), linear warmup for the first 10% of steps, and cosine annealing to zero. Trained on NVIDIA H100 GPUs using HuggingFace Transformers.

Benchmark Results (Vividh-ASR)

Benchmark WER is measured using faster-whisper with 7s VAD segmentation for long-form audio. See the blogpost for full evaluation details.

Model	Tier A (Studio)	Tier B (Broadcast)	Tier C (Spontaneous)	Tier D (Noise)	Global
whisper-medium-ml-high-lr	35.04	30.48	50.30	50.78	40.85
whisper-medium-ml-rmft (This model)	37.56	31.66	46.10	45.73	39.64
whisper-small-ml-high-lr	39.05	32.50	54.39	51.08	43.93
whisper-small-ml-rmft	40.26	35.05	53.77	48.04	44.53
IndicWhisper	38.07	32.43	65.74	46.92	47.96
Vegam Whisper	38.74	55.10	58.53	54.46	53.39

WER %. Lower is better. See Vividh-ASR benchmark for full evaluation details.

Usage

python

from transformers import pipeline

asr = pipeline(
    "automatic-speech-recognition",
    model="adalat-ai/whisper-medium-ml-rmft",
    chunk_length_s=30,
    device="cuda"
)

result = asr("audio.wav")
print(result["text"])

Note: For long-form audio, benchmark results use faster-whisper with 7s VAD segmentation. For short clips, the HuggingFace pipeline above will produce equivalent results.

Training Data

Training data is a superset of the Vividh-ASR benchmark evaluation splits. Sources used:

Tier	Hours	Sources
A (Studio)	182.2	Fleurs, IndicTTS, OpenSLR, IMASC
B (Broadcast)	200.0	Shrutilipi
C (Spontaneous)	512.5	IndicVoices, Common Voice
Total	894.7

Intended Use & Limitations

This model is intended as a general-purpose Malayalam ASR model optimised for verbatim transcription accuracy across diverse acoustic conditions.

Limitations:

Evaluated on Hindi and Malayalam only; generalisation to other Indic languages is untested
Tier D evaluation uses synthetic noise profiles; performance on real-world degraded audio may differ

Citation

If you use this model or the Vividh-ASR benchmark, please cite:

bibtex

@misc{vividhasr2025,
  title   = {Vividh-ASR: Diagnosing and Fixing Studio-Bias in Whisper
             for Indic Languages},
  author  = {Kush Juvekar, Kavya Manohar, Kumaramanas Nethil},
  year    = {2026},
  url     = {https://huggingface.co/blog/adalat-ai/vividh-benchmark}
}

bibtex

@misc{vividh2026,
      title={Vividh-ASR: A Complexity-Tiered Benchmark and Optimization Dynamics for Robust Indic Speech Recognition}, 
      author={Kush Juvekar, Kavya Manohar, Aditya Srinivas Menon, Arghya Bhattacharya, Kumarmanas Nethil},
      year={2026},
      eprint={2605.13087},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2605.13087}, 
}

Related Models and Datasets

See the Vividh collection.

Developed by Adalat AI. Released under Apache 2.0.