Instructions to use yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full

SGLang

How to use yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full with Docker Model Runner:
```
docker model run hf.co/yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full
```

Step-Audio 2 Mini Luganda-English Bidirectional S2ST

This repository contains a full merged model for bidirectional Luganda-English speech-to-speech translation. It was created by merging the LoRA adapter yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-lora into stepfun-ai/Step-Audio-2-mini.

The separate adapter-only repository should remain available for users who prefer PEFT loading or want the smaller adapter artifact. This full repository is intended for simpler deployment and inference where loading a single model repo is preferable.

Intended Use

Research and development for Luganda-English speech translation in both directions:

Luganda speech input -> English text and English speech tokens
English speech input -> Luganda text and Luganda speech tokens

Validate outputs with native speakers before production or high-stakes use.

Source Model and Adapter

Base model: stepfun-ai/Step-Audio-2-mini
LoRA adapter: yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-lora
Merge script: scripts/push_full_model_to_hub.py

Training Data

The model was trained from yigagilbert/luganda-english-cleaned-v1-split by expanding each aligned speech pair into two direction-conditioned supervised examples. Each example uses a direction-specific system prompt so that one adapter can learn both translation directions.

Evaluation Summary

The checkpoint used for this merge was evaluated on 200 held-out validation examples per direction. The metrics below were generated with the adapter-loaded fine-tuned model before merge; the merged model contains the same adapted weights and is expected to match these results aside from normal deterministic or runtime differences. Re-run evaluation directly on this repository before a strict release if exact reproducibility is required.

Text Metrics

Direction	System	Count	BLEU higher	chrF higher	WER lower	COMET higher
Luganda -> English	Focused one-direction LoRA	200	32.530	54.535	0.574	0.717
Luganda -> English	This bidirectional model	200	19.032	42.447	0.737	0.640
English -> Luganda	Focused one-direction LoRA control	200	0.120	14.050	1.300	-
English -> Luganda	This bidirectional model	200	11.155	45.331	0.811	0.678
English -> Luganda	ASR + MT cascade baseline	200	4.026	34.734	2.039	0.624

Speech Metrics

Speech metrics were computed on 20 audio-aligned samples per direction using WavLM-large SpeechBERTScore-style similarity and MFCC+DTW MCD. These are supporting audio-channel diagnostics, not standalone translation-quality measures.

Direction	Count	SpeechBERT P higher	SpeechBERT R higher	SpeechBERT F1 higher	MCD lower
Luganda -> English	20	0.560	0.582	0.570	639.360
English -> Luganda	20	0.502	0.572	0.523	614.130

Retention and Composition Probes

The bidirectional model does not recover unrelated multilingual speech translation ability on small FLEURS probes: Spanish -> English BLEU is 0.293 and Japanese -> English BLEU is 0.163 on 20 samples each. However, it does recover target-script compliance on Luganda -> Chinese and Luganda -> Japanese prompt probes (100% target-script hit rate in both cases). The semantic quality of those composition outputs is poor, with roundtrip BLEU below 1, so these probes should not be read as successful Luganda -> Chinese or Luganda -> Japanese translation.

Interpretation

This model demonstrates that one direction-conditioned LoRA adaptation of Step-Audio-2-mini can learn both Luganda -> English and English -> Luganda speech translation. Compared with the earlier Luganda-to-English-only adapter, it adds English-to-Luganda capability but is weaker on Luganda-to-English. The bidirectional run was shorter (9,000 steps) than the focused one-direction run, so the Luganda-to-English gap combines multi-task interference and training duration.

Notes

If this repository includes token2wav/, those assets are provided to support waveform synthesis from generated audio tokens. Some inference clients may still use the official Step-Audio2 runtime code for token-to-waveform conversion.

License

The training code and adapter metadata are Apache-2.0. Because this merged repository contains base-model weights, users must also comply with the base model license and any dataset licensing constraints.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for yigagilbert/stepaudio2-mini-luganda-english-bidirectional-s2st-full

Base model

stepfun-ai/Step-Audio-2-mini

Quantized

(2)

this model

Evaluation results

BLEU on Luganda-English Cleaned v1 Split
validation set self-reported

11.155
chrF on Luganda-English Cleaned v1 Split
validation set self-reported

45.331
WER on generated Luganda text on Luganda-English Cleaned v1 Split
validation set self-reported

0.811
COMET on Luganda-English Cleaned v1 Split
validation set self-reported

0.678
BLEU on Luganda-English Cleaned v1 Split
validation set self-reported

19.032
chrF on Luganda-English Cleaned v1 Split
validation set self-reported

42.447
WER on generated English text on Luganda-English Cleaned v1 Split
validation set self-reported

0.737
COMET on Luganda-English Cleaned v1 Split
validation set self-reported

0.640