Evoxtral LoRA — Expressive Tagged Transcription

A LoRA adapter for Voxtral-Mini-3B-2507 that produces transcriptions enriched with inline expressive audio tags from the ElevenLabs v3 tag set.

Built for the Mistral AI Online Hackathon 2026 (W&B Fine-Tuning Track).

What It Does

Standard ASR:

So I was thinking maybe we could try that new restaurant downtown. I mean if you're free this weekend.

Evoxtral:

[nervous] So... [stammers] I was thinking maybe we could... [clears throat] try that new restaurant downtown? [laughs nervously] I mean, if you're free this weekend?

Evaluation Results

Metric	Base Voxtral	Evoxtral (finetuned)	Improvement
WER (Word Error Rate)	6.64%	4.47%	32.7% better
Tag F1 (Expressive Tag Accuracy)	22.0%	67.2%	3x better

Evaluated on 50 held-out test samples. The finetuned model dramatically improves expressive tag generation while also improving raw transcription accuracy.

Training Details

Parameter	Value
Base model	mistralai/Voxtral-Mini-3B-2507
Method	LoRA (PEFT)
LoRA rank	64
LoRA alpha	128
LoRA dropout	0.05
Target modules	q/k/v/o_proj, gate/up/down_proj, multi_modal_projector
Learning rate	2e-4
Scheduler	Cosine
Epochs	3
Batch size	2 (effective 16 with grad accum 8)
NEFTune noise alpha	5.0
Precision	bf16
GPU	NVIDIA A10G (24GB)
Training time	~25 minutes
Trainable params	124.8M / 4.8B (2.6%)

Dataset

Custom synthetic dataset of 1,010 audio samples generated with ElevenLabs TTS v3:

• 808 train / 101 validation / 101 test
• Each sample has audio + tagged transcription with inline ElevenLabs v3 expressive tags
• Tags include: [sighs], [laughs], [whispers], [nervous], [frustrated], [clears throat], [pause], [excited], and more
• Audio encoder (Whisper-based) was frozen during training

Usage

import torch
from transformers import VoxtralForConditionalGeneration, AutoProcessor
from peft import PeftModel

repo_id = "mistralai/Voxtral-Mini-3B-2507"
adapter_id = "YongkangZOU/evoxtral-lora"

processor = AutoProcessor.from_pretrained(repo_id)
base_model = VoxtralForConditionalGeneration.from_pretrained(
    repo_id, dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_id)

# Transcribe audio with expressive tags
inputs = processor.apply_transcription_request(
    language="en",
    audio=["path/to/audio.wav"],
    format=["WAV"],
    model_id=repo_id,
    return_tensors="pt",
)
inputs = inputs.to(model.device, dtype=torch.bfloat16)

outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
transcription = processor.batch_decode(
    outputs[:, inputs.input_ids.shape[1]:], skip_special_tokens=True
)[0]
print(transcription)
# [nervous] So... I was thinking maybe we could [clears throat] try that new restaurant downtown?

W&B Tracking

All training and evaluation runs are tracked on Weights & Biases:

• Training run
• Base model eval
• Finetuned model eval
• Project dashboard

Supported Tags

The model can produce any tag from the ElevenLabs v3 expressive tag set, including:

[laughs] [sighs] [gasps] [clears throat] [whispers] [sniffs] [pause] [nervous] [frustrated] [excited] [sad] [angry] [calm] [stammers] [yawns] and more.

Limitations

• Trained on synthetic (TTS-generated) audio, not natural speech recordings
• Tag F1 of 67.2% means ~1/3 of tags may be missed or misplaced
• English only
• Best results on conversational and emotionally expressive speech

Citation

@misc{evoxtral2026,
  title={Evoxtral: Expressive Tagged Transcription with Voxtral},
  author={Yongkang Zou},
  year={2026},
  url={https://huggingface.co/YongkangZOU/evoxtral-lora}
}