Feature Extraction
Transformers
Safetensors
AuriStreamParallel
audio
speech
language-model
auristream
discrete-diffusion
custom_code
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TuKoResearch/AuriStreamParallel100M_Group4_BigAudioDataset_500k", trust_remote_code=True, dtype="auto")Quick Links
AuriStreamParallel100M_Group4_BigAudioDataset_500k
AuriStream Parallel is a discrete diffusion speech language model by Greta Tuckute and Klemen Kotar.
Model Details
| Parameter | Value |
|---|---|
| Parameters | ~0.12B |
| Layers | 12 |
| Hidden Size | 768 |
| Attention Heads | 12 |
| Vocab Size | 8193 |
| Group Size | 4 |
| Mask Schedule | linear_text_prime |
Architecture
- Bidirectional transformer attention
- Grouped token latent projection
- Parallel token heads for group-wise prediction
- Partial masking diffusion objective
Usage
from transformers import AutoModel
model = AutoModel.from_pretrained(
"TuKoResearch/AuriStreamParallel100M_Group4_BigAudioDataset_500k",
trust_remote_code=True,
)
Base Model Code
This checkpoint uses shared model code from TuKoResearch/AuriStreamParallel-base.
Tokenizer
This model is intended for cochlear tokens, e.g. from WavCochCausalV8192.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="TuKoResearch/AuriStreamParallel100M_Group4_BigAudioDataset_500k", trust_remote_code=True)