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README.md

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+---
+language:
+  - mg
+license: agpl-3.0
+library_name: transformers
+tags:
+  - whisper
+  - automatic-speech-recognition
+  - speech
+  - malagasy
+  - low-resource
+  - fine-tuned
+datasets:
+  - badrex/malagasy-speech-full
+metrics:
+  - wer
+pipeline_tag: automatic-speech-recognition
+base_model: openai/whisper-medium
+model-index:
+  - name: whisper-malagasy-medium
+    results:
+      - task:
+          type: automatic-speech-recognition
+          name: Speech Recognition
+        dataset:
+          name: Malagasy Speech Full
+          type: badrex/malagasy-speech-full
+          split: validation
+        metrics:
+          - type: wer
+            value: 20.78
+            name: WER
+---
+
+<p align="center">
+  <img src="https://img.shields.io/badge/lang-malagasy-green?style=for-the-badge" alt="Malagasy"/>
+  <img src="https://img.shields.io/badge/WER-20.8%25-blue?style=for-the-badge" alt="WER"/>
+  <img src="https://img.shields.io/badge/base-whisper--medium-orange?style=for-the-badge" alt="Base model"/>
+</p>
+
+# Whisper Medium — Malagasy (mg)
+
+**Le premier modele Whisper fine-tune pour la transcription du malagasy.**
+
+Fine-tuning de [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) (769M params) sur 149h d'audio malagasy. Developpe dans le cadre du projet [Milo Voice](https://github.com/Flo976/milo), le premier assistant vocal IA en malagasy.
+
+## Resultats
+
+| Metrique | Validation | Baseline (sans fine-tuning) |
+|----------|-----------|---------------------------|
+| **WER** | **20.78%** | >80% |
+
+> Pour une langue low-resource comme le malagasy, un WER de ~21% est un excellent resultat.
+> A titre de comparaison, Whisper medium atteint ~15% WER sur l'anglais.
+
+### Courbe d'entrainement
+
+| Step | WER | Loss |
+|------|-----|------|
+| 1000 | 25.13% | 0.303 |
+| 2000 | 22.21% | 0.261 |
+| 3000 | 21.13% | 0.247 |
+| 4000 | 20.97% | 0.252 |
+| **5000** | **20.78%** | **0.247** |
+| 6000 | 21.21% | 0.266 |
+| 7000 | 21.10% | 0.270 |
+
+Le meilleur checkpoint est a l'etape 5000 (epoch ~2.8).
+
+## Utilisation
+
+### Avec Transformers
+
+```python
+import torch
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+
+# Le processor vient du modele de base (le checkpoint ne sauvegarde pas le vocab)
+processor = WhisperProcessor.from_pretrained("openai/whisper-medium")
+model = WhisperForConditionalGeneration.from_pretrained(
+    "Flo976/whisper-malagasy-medium",
+    torch_dtype=torch.float16,
+).to("cuda")
+
+# Transcrire
+inputs = processor(audio_array, sampling_rate=16000, return_tensors="pt")
+input_features = inputs.input_features.to("cuda", dtype=torch.float16)
+
+with torch.no_grad():
+    predicted_ids = model.generate(
+        input_features,
+        language="mg",
+        task="transcribe",
+        max_new_tokens=128,
+    )
+
+text = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+print(text)
+```
+
+### Avec pipeline
+
+```python
+from transformers import pipeline
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model="Flo976/whisper-malagasy-medium",
+    tokenizer="openai/whisper-medium",
+    feature_extractor="openai/whisper-medium",
+    device="cuda:0",
+    torch_dtype="float16",
+)
+
+result = pipe(
+    "audio.wav",
+    generate_kwargs={"language": "mg", "task": "transcribe"},
+)
+print(result["text"])
+```
+
+### Inference optimisee (FP16)
+
+Le modele tourne en FP16 sur GPU, ce qui reduit la VRAM de ~3 Go a ~1.5 Go sans perte de qualite mesurable.
+
+## Entrainement
+
+### Dataset
+
+| | Samples | Duree | Source |
+|--|---------|-------|--------|
+| **Train** | 28 371 | 149h | [badrex/malagasy-speech-full](https://huggingface.co/datasets/badrex/malagasy-speech-full) |
+| **Validation** | 3 099 | - | idem |
+| **Test** | 3 101 | - | idem |
+
+- 56 locuteurs uniques
+- Audio 16kHz mono
+- Transcriptions en malagasy (dialecte plateau / officiel)
+
+### Hyperparametres
+
+| Parametre | Valeur |
+|-----------|--------|
+| Modele de base | `openai/whisper-medium` (769M params) |
+| Epochs | 4 (~3.95) |
+| Batch size | 8 x 2 (gradient accumulation) = 16 effectif |
+| Learning rate | 1e-5 |
+| Warmup steps | 500 |
+| Optimizer | AdamW |
+| Precision | FP16 |
+| Gradient checkpointing | Oui |
+| Best checkpoint | Step 5000 (WER 20.78%) |
+
+### Materiel
+
+| | |
+|-|-|
+| GPU | NVIDIA RTX 5070 Ti (16 Go VRAM) |
+| Temps d'entrainement | ~12h |
+| Framework | HuggingFace Transformers 4.47 + PyTorch 2.5 |
+
+### Script d'entrainement
+
+```bash
+python scripts/03_train.py \
+  --model openai/whisper-medium \
+  --dataset badrex/malagasy-speech-full \
+  --output-dir models/whisper-mg-v1 \
+  --epochs 10 \
+  --batch-size 8 \
+  --grad-accum 2 \
+  --lr 1e-5 \
+  --warmup-steps 500 \
+  --eval-steps 500
+```
+
+## Limitations
+
+- **Dialectes** : entraine principalement sur le malagasy officiel (dialecte plateau / Merina). Les performances sur les dialectes cotiers (Betsimisaraka, Sakalava, etc.) ne sont pas evaluees.
+- **Bruit** : les performances se degradent avec du bruit de fond important. Le dataset d'entrainement contient majoritairement de l'audio propre.
+- **Phrases longues** : le modele est optimise pour des phrases courtes a moyennes (<30s). Les transcriptions longues peuvent contenir des hallucinations.
+- **Processor** : le checkpoint ne contient pas le tokenizer/processor — utiliser `openai/whisper-medium` pour le processor.
+
+## Cas d'usage
+
+- Assistant vocal malagasy ([Milo Voice](https://github.com/Flo976/milo))
+- Transcription de reunions / interviews en malagasy
+- Sous-titrage automatique de videos malagasy
+- Accessibilite pour les locuteurs malagasy
+
+## Citation
+
+```bibtex
+@misc{whisper-malagasy-medium-2026,
+  author = {Florent Didelot},
+  title = {Whisper Medium fine-tuned for Malagasy Speech Recognition},
+  year = {2026},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/Flo976/whisper-malagasy-medium}
+}
+```
+
+## Licence
+
+AGPL-3.0 — voir [LICENSE](https://github.com/Flo976/milo/blob/main/LICENSE)
+
+---
+
+Developpe dans le cadre du projet **[Milo Voice](https://github.com/Flo976/milo)** par [Sooatek](https://sooatek.com).

config.json

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+}

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