Fine-tune Whisper-small for Korean Speech Recognition sample data (PoC)
Fine-tuning was performed using sample voices recorded from this csv data(https://github.com/hyeonsangjeon/job-transcribe/blob/main/meta_voice_data_3922.csv). We do not publish sample voices, so if you want to fine-tune yourself from scratch, please record separately or use a public dataset.
Fine tuning training based on the guide at https://huggingface.co/blog/fine-tune-whisper
[Note] In the voice recording data used for training, the speaker spoke clearly and slowly as if reading a textbook.
Training
Base model
OpenAI's whisper-small (https://huggingface.co/openai/whisper-small)
Parameters
We used heuristic parameters without separate hyperparameter tuning. The sampling rate is set to 16,000Hz.
- learning_rate = 2e-5
- epochs = 5
- gradient_accumulation_steps = 4
- per_device_train_batch_size = 4
- fp16 = True
- gradient_checkpointing = True
- generation_max_length = 225
Usage
You need to install librosa package in order to convert wave to Mel Spectrogram. (pip install librosa)
inference.py
import librosa
import torch
from transformers import WhisperProcessor, WhisperForConditionalGeneration
# prepare your sample data (.wav)
file = "nlp-voice-3922/data/0002d3428f0ddfa5a48eec5cc351daa8.wav"
# Convert to Mel Spectrogram
arr, sampling_rate = librosa.load(file, sr=16000)
# Load whisper model and processor
processor = WhisperProcessor.from_pretrained("openai/whisper-small")
model = WhisperForConditionalGeneration.from_pretrained("daekeun-ml/whisper-small-ko-finetuned-single-speaker-3922samples")
# Preprocessing
input_features = processor(arr, return_tensors="pt", sampling_rate=sampling_rate).input_features
# Prediction
forced_decoder_ids = processor.get_decoder_prompt_ids(language="ko", task="transcribe")
predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
print(transcription)
