---
library_name: peft
pipeline_tag: automatic-speech-recognition
language:
- bm
tags:
- lora
- peft
- whisper
- automatic-speech-recognition
- tensorboard
---

# bm-whisper-large-v4-training-bm-lora-3a

A rank-8 LoRA adapter for Bambara speech recognition, sized for Whisper large-v3 geometry: hidden size 1280, 32 encoder + 32 decoder layers, 20 attention heads, 128 mel bins, 51,866-token vocabulary.

Adapter weights only. The base checkpoint it was trained against is not recorded in this repo, so supply your own Whisper large-v3-geometry model when loading.

## Usage

```python
from peft import PeftModel
from transformers import WhisperForConditionalGeneration, WhisperProcessor

base = WhisperForConditionalGeneration.from_pretrained(YOUR_BASE_MODEL)
model = PeftModel.from_pretrained(base, "djelia/bm-whisper-large-v4-training-bm-lora-3a")
model.eval()

# tokenizer and feature extractor ship with the adapter
processor = WhisperProcessor.from_pretrained("djelia/bm-whisper-large-v4-training-bm-lora-3a")

# Optional: fold the LoRA deltas into the base weights for inference.
# merged = model.merge_and_unload()
```

## Adapter configuration

| Key | Value |
|---|---|
| `peft_type` | `LORA` |
| `r` / `lora_alpha` | 8 / 8 (scaling 1.0) |
| `lora_dropout` | 0.05 |
| `bias` / `lora_bias` | `none` / false |
| `target_modules` | `["q_proj", "k_proj", "v_proj", "out_proj"]` |
| `base_model_class` | `WhisperForConditionalGeneration` |
| Adapter dtype | F32 |

## Notes

`target_modules` is a plain name list, so every matching projection in both towers is adapted: encoder self-attention, decoder self-attention and decoder cross-attention, layers 0-31 (768 tensors in total). MLP blocks, the convolutional front-end, embeddings, layer norms and `proj_out` are untouched.

Audio should be 16 kHz mono.