Phi-4-multimodal-finetune-ko-speech

Phi-4-multimodal-finetune-ko-speech

This is a fine-tuned model for Korean speech-to-text translation, from microsoft/Phi-4-multimodal-instruct on the following datasets:

• kresnik/zeroth_korean
• mozilla-foundation/common_voice_17_0 (Used Korean speech only)
• PolyAI/minds14 (Used Korean speech only)
• Custom dataset on my own. The speech was a mix of fast and slow speech (Technical blog contents and presentations I have posted), with some modulation using audiomentations and this script

Total 35K samples. Each sample is a pair of Korean speech and its transcription. Dataset was sampled 16kHz.

The model was trained on a single A100 80GB GPU for 4 epochs with a batch size of 16 using the sample_finetune_speech.py script from microsoft/Phi-4-multimodal-instruct

The latest version of the model currently uploaded was fine-tuned by unfreezing the audio encoder, and the ASR performance was significantly improved compared to the baseline LoRA adapter-based fine-tuning. Comparing the full fine-tuning and LoRA fine-tuning, the CER on zeroth-test set is 1.61% and 2.72%, and the WER on zeroth-test set is 3.54% and 7.19%, respectively.

Note that this model is just a PoC/experimental purpose, and not intended to be used in production. More high-quality data, tuning, ablation studies, and experiments are needed.

Phi-4-multimodal model is strong in multimodal tasks, especially in speech-to-text and high potential in Korean language tasks. Thus if you are interested in Korean speech-to-text task, this model can be a good starting point.

Evaluation

Evaluation was done on the following datasets:

• ASR (Automatic Speech Recognition): Evaluated with CER (Character Error Rate) and WER (Word Error Rate) on zeroth-test set (457 samples).
• AST (Automatic Speech Translation): Evaluated with BLEU score on fleurs ko <-> en speech translation test set (270 samples).

Script is retrieved from here.

Compared to Phi-4-mm-inst-zeroth-kor, ASR is significantly improved with more high-quality voice data and my own voice. However, the quality of AST deteriorates for fleurs-ko2en-cot, so appropriate data should be inserted in between to improve catastrophic forgetting.

Usage

Requirements

Works with the following packages. Please make sure to install them before using the model.

flash_attn==2.7.4.post1
torch==2.6.0
transformers==4.48.2
accelerate==1.4.0
soundfile==0.13.1
pillow==11.1.0
scipy==1.15.2
torchvision==0.21.0
backoff==2.2.1
peft==0.14.0
datasets==3.3.2
librosa==0.10.2.post1
pandas==2.2.3

Sample code

from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoProcessor, GenerationConfig

max_new_tokens = 256
ft_model_path = "daekeun-ml/Phi-4-multimodal-finetune-ko-speech"
generation_config = GenerationConfig.from_pretrained(ft_model_path, 'generation_config.json')
processor = AutoProcessor.from_pretrained(ft_model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    ft_model_path,
    trust_remote_code=True,
    torch_dtype='auto',
    _attn_implementation='flash_attention_2',
).cuda()

user_prompt = '<|user|>'
assistant_prompt = '<|assistant|>'
prompt_suffix = '<|end|>'

# task prompt is from technical report
asr_prompt = f'{user_prompt}<|audio_1|>Transcribe the audio clip into text.{prompt_suffix}{assistant_prompt}'
ast_ko_prompt = f'{user_prompt}<|audio_1|>Translate the audio to Korean.{prompt_suffix}{assistant_prompt}'
ast_cot_ko_prompt = f'{user_prompt}<|audio_1|>Transcribe the audio to text, and then translate the audio to Korean. Use <sep> as a separator between the original transcript and the translation.{prompt_suffix}{assistant_prompt}'
ast_en_prompt = f'{user_prompt}<|audio_1|>Translate the audio to English.{prompt_suffix}{assistant_prompt}'
ast_cot_en_prompt = f'{user_prompt}<|audio_1|>Transcribe the audio to text, and then translate the audio to English. Use <sep> as a separator between the original transcript and the translation.{prompt_suffix}{assistant_prompt}'

asr_ds = load_dataset("kresnik/zeroth_korean", split="test")

# ASR
item = asr_ds[0]
audio = (item["audio"]["array"], item["audio"]["sampling_rate"])
inputs = processor(text=asr_prompt, audios=[audio], return_tensors='pt').to(model.device)
generate_ids = model.generate(
    **inputs,
    max_new_tokens=max_new_tokens,
    generation_config=generation_config,
)
generate_ids = generate_ids[:, inputs['input_ids'].shape[1] :]
response = processor.batch_decode(
    generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0] 
print(response) # "몬터규는 자녀들이 사랑을 제대로 못 받고 크면 매우 심각한 결과가 초래된다는 결론을 내렸습니다"

Demos

Please refer to the Jupyter notebook and video clips in the demo folder. They are not production-quality as they were simply fine-tuned for PoC purposes, but you can see that they transcribe and translate with high accuracy even when a native speaker speaks quite quickly.

References

• https://huggingface.co/microsoft/Phi-4-multimodal-instruct
• https://huggingface.co/seastar105/Phi-4-mm-inst-zeroth-kor