README.md · seastar105/Phi-4-mm-inst-zeroth-kor at main

metadata

datasets:
  - kresnik/zeroth_korean
metrics:
  - bleu
  - cer
base_model:
  - microsoft/Phi-4-multimodal-instruct
model-index:
  - name: Phi-4-mm-inst-zeroth-kor
    results:
      - task:
          type: speech-to-text-translation
        dataset:
          type: seastar105/fleurs_ko_en_test
          name: fleurs (ko-en test intersection)
        metrics:
          - type: bleu
            name: ko2en
            value: 7.07
          - type: bleu
            name: ko2en-cot
            value: 9.19
          - type: bleu
            name: en2ko (ko-mecab)
            value: 13.08
          - type: bleu
            name: en2ko-cot (ko-mecab)
            value: 9.35
      - task:
          type: automatic-speech-recognition
        dataset:
          type: kresnik/zeroth_korean
          name: zeroth_korean test
        metrics:
          - type: cer
            name: test CER
            value: 7.02
language:
  - ko

This model is fine-tuned from microsoft/Phi-4-multimodal-instruct on kresnik/zeroth_korean dataset only 1 epoch.

script for fine-tuning is here, adapted from phi-4 repository example

model is trained only 174 steps on zeroth train set, and main purpose is to check if only korean ASR training can expand to other speech tasks(e.g. speech-to-text-translation)

Evaluation

ASR on zeroth-test set and Speech translation on fleurs ko <-> en speech translation result. script is here, and used 1 A40.

Model	zeroth-test	fleurs-ko2en	fleurs-ko2en-cot	fleurs-en2ko	fleurs-en2ko-cot
original	195.92	5.62	2.45	6.87	4.35
finetune (this model)	7.02	7.07	9.19	13.08	9.35

Example script

orig_model_path = "microsoft/Phi-4-multimodal-instruct"
ft_model_path = "seastar105/Phi-4-mm-inst-zeroth-kor"
generation_config = GenerationConfig.from_pretrained(orig_model_path, 'generation_config.json')
processor = AutoProcessor.from_pretrained(orig_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")
ast_ds = load_dataset("seastar105/fleurs_ko_en_test", split="train")

# 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] # "몬토 킬은 자녀들이 사랑을 제대로 못 받고 크면 매우 심각한 결과가 초래된다는 결론을 내렸습니다"

# AST, EN -> KO
item = ast_ds[-1]
audio = (item["en_audio"]["array"], item["en_audio"]["sampling_rate"])
inputs = processor(text=ast_en, 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] # "가장 쉽게 접근 가능한 식물 자원은 잎과 légumes에서 접근 가능한 단백질이었을 것이다가요 하지만 이것들은 고형상 동물처럼 우리에게 소화하기 어렵습니다만 그것들이 끓여 있다면요"