TuKoResearch/HuBERT-KM200-Quantizer

HuBERT K-means Quantizer

This model implements HuBERT with k-means quantization for converting speech to discrete tokens.

Usage

from transformers import AutoModel
import torch
import torchaudio

# Load the processor/tokenizer
processor = AutoModel.from_pretrained("your-username/hubert-kmeans-200", trust_remote_code=True)

# Load audio
audio, sr = torchaudio.load("audio.wav")
if sr != 16000:
    resampler = torchaudio.transforms.Resample(sr, 16000)
    audio = resampler(audio)

# Process audio to get tokens
outputs = processor(audio, return_tensors="pt", sample_rate=16000)
tokens = outputs.input_values  # or outputs.input_ids

print(f"Tokens shape: {tokens.shape}")