---
license: apache-2.0
base_model: stepfun-ai/Step-Audio-2-mini
library_name: transformers
tags:
- audio
- speech-translation
- speech-to-speech
- luganda
- english
- bidirectional
- stepaudio2
- merged-lora
model-index:
- name: Step-Audio 2 Mini Luganda-English Bidirectional S2ST
  results:
  - task:
      type: speech-translation
      name: English-to-Luganda speech translation
    dataset:
      type: yigagilbert/luganda-english-cleaned-v1-split
      name: Luganda-English Cleaned v1 Split
      split: validation
    metrics:
    - type: bleu
      name: BLEU
      value: 11.155
    - type: chrf
      name: chrF
      value: 45.331
    - type: wer
      name: WER on generated Luganda text
      value: 0.811
    - type: comet
      name: COMET
      value: 0.678
  - task:
      type: speech-translation
      name: Luganda-to-English speech translation
    dataset:
      type: yigagilbert/luganda-english-cleaned-v1-split
      name: Luganda-English Cleaned v1 Split
      split: validation
    metrics:
    - type: bleu
      name: BLEU
      value: 19.032
    - type: chrf
      name: chrF
      value: 42.447
    - type: wer
      name: WER on generated English text
      value: 0.737
    - type: comet
      name: COMET
      value: 0.640
---

# 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.