---
language:
  - dv
  - ar
  - en
license: cc-by-nc-4.0
tags:
  - automatic-speech-recognition
  - mms
  - ctc
  - trilingual
  - dhivehi
  - arabic
  - english
  - madhaha
datasets:
  - shiimi/dhivehi-audio-casts-processed
  - Serialtechlab/dhivehi-mms-v5-combined
metrics:
  - wer
base_model: Serialtechlab/mms-trilingual-dv-ar-en-v2
---

# MMS Trilingual ASR v3 - Dhivehi + Arabic + English (Madhaha Fix)

Fine-tuned version of mms-trilingual-dv-ar-en-v2 with **fixed Madhaha recognition**.

## Problem Solved
v2 model confused melodic Dhivehi (Madhaha/religious songs) with Arabic,
outputting Arabic script instead of Thaana. This version fixes that issue.

## Training Strategy
- Started from v2 model (preserves improved English/Arabic recognition)
- Trained ONLY on Dhivehi data (no Arabic interference)
- Oversampled melodic Dhivehi 3x to emphasize the pattern
- Higher learning rate (3e-05) to change associations aggressively
- 5 epochs for stronger reinforcement

## Training Data
- Melodic Dhivehi: ~3000 samples (oversampled from audio casts)
- Normal Dhivehi: ~1500 samples

## Performance
- Final WER: 0.2153

## Usage
```python
from transformers import AutoProcessor, Wav2Vec2ForCTC
import torch

processor = AutoProcessor.from_pretrained("Serialtechlab/mms-trilingual-dv-ar-en-v3")
model = Wav2Vec2ForCTC.from_pretrained("Serialtechlab/mms-trilingual-dv-ar-en-v3")

# Process audio (16kHz)
inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
```

## Supported Languages
- Dhivehi (Thaana script) - including melodic/Madhaha
- Arabic (Arabic script) - preserved from v2
- English (Latin script) - preserved from v2 (improved Thaana transliteration)

## Changes from v2
- v3 specifically targets the Madhaha confusion issue
- Melodic Dhivehi now correctly outputs Thaana script
- Preserves v2's improved English and Arabic recognition