metadata
library_name: peft
pipeline_tag: automatic-speech-recognition
language:
- bm
- fr
- en
tags:
- lora
- peft
- whisper
- automatic-speech-recognition
- tensorboard
datasets:
- djelia/bambara-asr
- djelia/bambara-synthetic-audio
- djelia/bambara-audio-b
- sudoping01/bambara-speech-recognition-benchmark
bm-whisper-large-v4-training-bm-lora-3c
A decoder-only, rank-8 LoRA adapter for Bambara speech recognition, sized for Whisper large-v3 geometry: hidden size 1280, 32 encoder + 32 decoder layers, 128 mel bins, 51,866-token vocabulary.
Adapter weights only. The base checkpoint is not recorded in this repo, so supply your own Whisper large-v3-geometry model when loading.
Usage
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-3c")
model.eval()
# tokenizer and feature extractor ship with the adapter
processor = WhisperProcessor.from_pretrained("djelia/bm-whisper-large-v4-training-bm-lora-3c")
# 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 |
regex model.decoder.layers.[\d]+.(self_attn|encoder_attn).(q_proj|k_proj|v_proj|out_proj) |
| Trainable parameters | 5,242,880 |
| Adapter dtype | F32 |
Notes
target_modules is a regex scoped to model.decoder.layers, so the adapter covers decoder self-attention and cross-attention q/k/v/out_proj across all 32 decoder layers (512 tensors) and nothing else. The encoder, every MLP block, all embeddings and all layer norms are untouched: the acoustic representation is inherited from the base.
Audio should be 16 kHz mono.