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djelia/
bm-whisper-large-v4-training-bm-lora-3a

Name: bm-whisper-large-v4-training-bm-lora-3a
Availability: InStock

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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.