README.md

---
license: apache-2.0
language: en
tags: [speech-llm, desta, librispeech, tutorial]
---

# Interspeech tutorial — DeSTA-style SpeechLLM checkpoints

Whisper-large-v3 encoder (frozen) → concat+MLP adapter → Qwen3-4B-Instruct-2507 + LoRA r32.
Only the adapter and the LoRA weights are trained, so each checkpoint is ~147 MB; the base
models are downloaded from their own repos at load time.

Code, configs and the full recipe: <https://github.com/kehanlu/interspeech-tutorial>

| folder | training data | test-clean ASR | test-clean gender |
|---|---|---|---|
| `asr_gender` | 281k ASR + a fresh 30% of the gender rows each epoch | **1.81** WER | **98.85** |
| `selfgen` | 281k self-generated conversational replies, no task labels | 3.93 WER | 98.24 |

`asr_gender` is ordinary task SFT, and it matches whisper-large-v3 on ASR (1.89) while also
answering the gender question. It is the baseline.

`selfgen` is the interesting one: its targets were written by Qwen3-4B given only the
transcript and the speaker's gender, so **it has never seen a transcription or a gender
label as a training target**. The self-generation prompt was

    <audio>{transcription} (Gender: {gender})</audio>

    The audio is a passage read aloud from a book. Respond directly as a natural
    conversation partner. Do not mention the audio, the transcription, or the speaker
    attributes.

It can still do both tasks, but only if the prompt leaves room for a short answer. Asked the
way `asr_gender` was trained ("Transcribe the speech into text") it replies with an essay
about the passage and scores 52.35 WER; asked for a format it reaches 3.93:

| prompt | ASR |
|---|---|
| `Transcribe the speech into text` | 52.35 → 16.54 after clean-up |
| `Transcribe the speech word for word. Output only the transcription, with no explanation, in this format:\nAnswer: "<transcription>"` | 8.94 → **3.93** |

Gender goes 81.87 → **98.24** the same way, with "The audio is a passage read aloud from a
book. Is the speaker male or female? Answer with one word." The clean-up is the rule-based
`postprocess()` in the tutorial repo's `example/evaluate/evaluate_asr.py`; it is a no-op on
`asr_gender`, which already answers with a bare transcript.

## Usage

The model code is in the GitHub repo:

```bash
git clone https://github.com/kehanlu/interspeech-tutorial
```

```python
from huggingface_hub import hf_hub_download
import sys, torch

sys.path.insert(0, "interspeech-tutorial")
from inference import SpeechLLMForInference

ckpt = hf_hub_download("kehanlu/interspeech-tutorial", "selfgen/model.ckpt")
pipe = SpeechLLMForInference.from_checkpoint(ckpt, dtype=torch.float16)   # float16 for a Colab T4
print(pipe.generate([{"role": "user",
                      "content": "<audio><|AUDIO|></audio>\n\nTranscribe the speech into text",
                      "audios": [{"audio": "sample.flac"}]}]))
```

A few LibriSpeech dev-clean clips are in `samples/` with their transcripts in
`samples/samples.json`.