Audio-to-Audio
MambaSSM
Safetensors
speech-enhancement
universal speech enhancement
multiple input sampling rates
language-agnostic
Instructions to use nvidia/RE-USE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MambaSSM
How to use nvidia/RE-USE with MambaSSM:
from mamba_ssm import MambaLMHeadModel model = MambaLMHeadModel.from_pretrained("nvidia/RE-USE") - Notebooks
- Google Colab
- Kaggle
Upload generator_SEMamba_time_d4.py
Browse files
models/generator_SEMamba_time_d4.py
CHANGED
|
@@ -9,10 +9,11 @@
|
|
| 9 |
import torch
|
| 10 |
import torch.nn as nn
|
| 11 |
from einops import rearrange
|
|
|
|
| 12 |
from .mamba_block2_SEMamba import TFMambaBlock
|
| 13 |
from .codec_module_time_d4 import DenseEncoder, MagDecoder, PhaseDecoder
|
| 14 |
|
| 15 |
-
class SEMamba(nn.Module):
|
| 16 |
"""
|
| 17 |
SEMamba model for speech enhancement using Mamba blocks.
|
| 18 |
|
|
|
|
| 9 |
import torch
|
| 10 |
import torch.nn as nn
|
| 11 |
from einops import rearrange
|
| 12 |
+
from huggingface_hub import PyTorchModelHubMixin
|
| 13 |
from .mamba_block2_SEMamba import TFMambaBlock
|
| 14 |
from .codec_module_time_d4 import DenseEncoder, MagDecoder, PhaseDecoder
|
| 15 |
|
| 16 |
+
class SEMamba(nn.Module, PyTorchModelHubMixin):
|
| 17 |
"""
|
| 18 |
SEMamba model for speech enhancement using Mamba blocks.
|
| 19 |
|