Model Description

• Model name: BLI ASR 0
• Task: Automatic Speech Recognition
• Language: Lingala
• Base model: openai/whisper-large-v3
• Adaptation method: LoRA / PEFT
• Training dataset: Waxal Lingala ASR
• Output: Lingala transcription from speech audio

This model transcribes Lingala speech into text. It is not a translation model.

Dataset

The model was trained on the Waxal Lingala ASR dataset.

The dataset was split into:

Text Post-processing

We applied a light normalization pipeline to the training and evaluation transcriptions.

The goal was not to impose a strict Lingala orthography, but to reduce noise and improve consistency. The post-processing included:

• Unicode normalization
• lowercasing
• whitespace normalization
• punctuation and symbol cleanup
• preservation of the original raw transcription when available
• creation of a normalized transcription field used for training/evaluation

We intentionally avoided aggressive spelling correction because Lingala has substantial orthographic variation across speakers, regions, and data sources.

Training Details

The model was fine-tuned from openai/whisper-large-v3 using LoRA.

Main training choices:

Performance

We report CER rather than WER for this release.

We do not report WER in this first release because WER is not fully fair for the current Lingala ASR setting. Lingala does not yet have a single widely enforced normalized orthography in our data, and WER strongly penalizes spelling variants, segmentation differences, and silence-related insertions/deletions. We plan to release a corrected WER metric that better accounts for linguistic and contextual variation.

Intended Use

This model can be used for:

• Lingala speech transcription
• research on low-resource ASR
• dataset bootstrapping
• assisted transcription before human correction
• evaluation of ASR pipelines for Bantu languages

The model is especially useful as a first-pass transcription model before review by human annotators.

Limitations

This is an early release and still has important limitations:

• silence handling still needs improvement
• the model may hallucinate text during long silent regions
• performance can degrade with music, jingles, intros, outros, and strong background noise
• performance in real-world media with overlapping speech is still limited
• the training data is not general enough to cover all common Lingala varieties
• the model may struggle with recent slang, popular urban expressions, and code-switching
• the model is not yet robust across all domains such as news, sermons, informal conversation, street interviews, and music-heavy content

Example Inference in a Notebook

python
Copy code
!pip install -U transformers peft accelerate soundfile librosa

import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration
from peft import PeftModel
import librosa

base_model = "openai/whisper-large-v3"
adapter_model = "BantuLanguagesInitiative/bli-asr-0"

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32

processor = WhisperProcessor.from_pretrained(
    base_model,
    language="lingala",
    task="transcribe",
)

model = WhisperForConditionalGeneration.from_pretrained(
    base_model,
    torch_dtype=dtype,
)

model = PeftModel.from_pretrained(model, adapter_model)
model = model.merge_and_unload()
model.to(device)
model.eval()

audio_path = "example.mp3"
audio, sr = librosa.load(audio_path, sr=16000)

inputs = processor.feature_extractor(
    audio,
    sampling_rate=16000,
    return_tensors="pt",
)

input_features = inputs.input_features.to(device=device, dtype=dtype)

forced_decoder_ids = processor.get_decoder_prompt_ids(
    language="lingala",
    task="transcribe",
)

with torch.no_grad():
    generated_ids = model.generate(
        input_features,
        forced_decoder_ids=forced_decoder_ids,
        max_new_tokens=225,
    )

text = processor.tokenizer.batch_decode(
    generated_ids,
    skip_special_tokens=True,
)[0]

print(text)

Debug

If you get a PEFT/torchao version error in Colab, run:

python
Copy code
!pip install -U torchao