Text-to-Speech
Transformers
Safetensors
hifi_gan
feature-extraction
audio
vocoder
hifi-gan
bigvgan
neural-vocoder
speaker-conditioning
audio-watermarking
custom_code
Instructions to use mlr2000/vocoder-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlr2000/vocoder-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="mlr2000/vocoder-small", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mlr2000/vocoder-small", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # Adapted from https://github.com/junjun3518/alias-free-torch under the Apache License 2.0 | |
| # LICENSE is in incl_licenses directory. | |
| import torch.nn as nn | |
| from .alias_free_resample import UpSample1d, DownSample1d | |
| class Activation1d(nn.Module): | |
| def __init__( | |
| self, | |
| activation, | |
| up_ratio: int = 2, | |
| down_ratio: int = 2, | |
| up_kernel_size: int = 12, | |
| down_kernel_size: int = 12, | |
| ): | |
| super().__init__() | |
| self.up_ratio = up_ratio | |
| self.down_ratio = down_ratio | |
| self.act = activation | |
| self.upsample = UpSample1d(up_ratio, up_kernel_size) | |
| self.downsample = DownSample1d(down_ratio, down_kernel_size) | |
| # x: [B,C,T] | |
| def forward(self, x): | |
| x = self.upsample(x) | |
| x = self.act(x) | |
| x = self.downsample(x) | |
| return x | |