Update model card
Browse files
README.md
CHANGED
|
@@ -40,18 +40,23 @@ nhead = 8
|
|
| 40 |
num_layers = 4
|
| 41 |
dim_feedforward = 1024
|
| 42 |
dropout = 0.1
|
|
|
|
| 43 |
|
| 44 |
-
The segment-level discriminators use segment_len values of 10, 25, and 50.
|
| 45 |
-
The strided discriminators use segment_len=50 with scales 10 and 25.
|
| 46 |
|
| 47 |
-
|
|
|
|
|
|
|
| 48 |
|
| 49 |
Install the Hugging Face Hub package:
|
| 50 |
|
|
|
|
| 51 |
pip install -U huggingface_hub
|
|
|
|
| 52 |
|
| 53 |
Download a checkpoint:
|
| 54 |
|
|
|
|
| 55 |
from huggingface_hub import hf_hub_download
|
| 56 |
|
| 57 |
repo_id = "Chanson-0803/MSpoofTTS"
|
|
@@ -63,9 +68,11 @@ ckpt_path = hf_hub_download(
|
|
| 63 |
)
|
| 64 |
|
| 65 |
print(ckpt_path)
|
|
|
|
| 66 |
|
| 67 |
Then load the checkpoint using the corresponding discriminator class from the MSpoofTTS codebase:
|
| 68 |
|
|
|
|
| 69 |
import torch
|
| 70 |
|
| 71 |
# Import this from the official MSpoofTTS codebase.
|
|
@@ -74,9 +81,11 @@ import torch
|
|
| 74 |
state = torch.load(ckpt_path, map_location="cpu")
|
| 75 |
model.load_state_dict(state["model_state_dict"])
|
| 76 |
model.eval()
|
|
|
|
| 77 |
|
| 78 |
For hierarchical decoding, use the following checkpoint files:
|
| 79 |
|
|
|
|
| 80 |
checkpoint_files = {
|
| 81 |
"segment_len50": "checkpoints/segment_len50.ckpt",
|
| 82 |
"segment_len25": "checkpoints/segment_len25.ckpt",
|
|
@@ -84,19 +93,23 @@ checkpoint_files = {
|
|
| 84 |
"strided_seg50_scale10": "checkpoints/strided_seg50_scale10.ckpt",
|
| 85 |
"strided_seg50_scale25": "checkpoints/strided_seg50_scale25.ckpt",
|
| 86 |
}
|
| 87 |
-
|
|
|
|
|
|
|
| 88 |
|
| 89 |
These checkpoints are intended for research on discrete speech synthesis, neural codec language models, inference-time decoding guidance, spoof detection for generated speech tokens, and hierarchical multi-resolution decoding.
|
| 90 |
|
| 91 |
-
Limitations
|
| 92 |
|
| 93 |
These checkpoints are designed for the speech-token vocabulary and discriminator architectures used in MSpoofTTS. They may not be directly compatible with other codec tokenizers, vocabulary layouts, or speech language models without adaptation.
|
| 94 |
|
| 95 |
-
Citation
|
|
|
|
|
|
|
| 96 |
@article{zhao2026hierarchical,
|
| 97 |
title={Hierarchical Decoding for Discrete Speech Synthesis with Multi-Resolution Spoof Detection},
|
| 98 |
author={Zhao, Junchuan and Vu, Minh Duc and Wang, Ye},
|
| 99 |
journal={arXiv preprint arXiv:2603.05373},
|
| 100 |
year={2026}
|
| 101 |
}
|
| 102 |
-
|
|
|
|
| 40 |
num_layers = 4
|
| 41 |
dim_feedforward = 1024
|
| 42 |
dropout = 0.1
|
| 43 |
+
```
|
| 44 |
|
| 45 |
+
The segment-level discriminators use `segment_len` values of 10, 25, and 50.
|
|
|
|
| 46 |
|
| 47 |
+
The strided discriminators use `segment_len=50` with scales 10 and 25.
|
| 48 |
+
|
| 49 |
+
## Usage
|
| 50 |
|
| 51 |
Install the Hugging Face Hub package:
|
| 52 |
|
| 53 |
+
```bash
|
| 54 |
pip install -U huggingface_hub
|
| 55 |
+
```
|
| 56 |
|
| 57 |
Download a checkpoint:
|
| 58 |
|
| 59 |
+
```python
|
| 60 |
from huggingface_hub import hf_hub_download
|
| 61 |
|
| 62 |
repo_id = "Chanson-0803/MSpoofTTS"
|
|
|
|
| 68 |
)
|
| 69 |
|
| 70 |
print(ckpt_path)
|
| 71 |
+
```
|
| 72 |
|
| 73 |
Then load the checkpoint using the corresponding discriminator class from the MSpoofTTS codebase:
|
| 74 |
|
| 75 |
+
```python
|
| 76 |
import torch
|
| 77 |
|
| 78 |
# Import this from the official MSpoofTTS codebase.
|
|
|
|
| 81 |
state = torch.load(ckpt_path, map_location="cpu")
|
| 82 |
model.load_state_dict(state["model_state_dict"])
|
| 83 |
model.eval()
|
| 84 |
+
```
|
| 85 |
|
| 86 |
For hierarchical decoding, use the following checkpoint files:
|
| 87 |
|
| 88 |
+
```python
|
| 89 |
checkpoint_files = {
|
| 90 |
"segment_len50": "checkpoints/segment_len50.ckpt",
|
| 91 |
"segment_len25": "checkpoints/segment_len25.ckpt",
|
|
|
|
| 93 |
"strided_seg50_scale10": "checkpoints/strided_seg50_scale10.ckpt",
|
| 94 |
"strided_seg50_scale25": "checkpoints/strided_seg50_scale25.ckpt",
|
| 95 |
}
|
| 96 |
+
```
|
| 97 |
+
|
| 98 |
+
## Intended Use
|
| 99 |
|
| 100 |
These checkpoints are intended for research on discrete speech synthesis, neural codec language models, inference-time decoding guidance, spoof detection for generated speech tokens, and hierarchical multi-resolution decoding.
|
| 101 |
|
| 102 |
+
## Limitations
|
| 103 |
|
| 104 |
These checkpoints are designed for the speech-token vocabulary and discriminator architectures used in MSpoofTTS. They may not be directly compatible with other codec tokenizers, vocabulary layouts, or speech language models without adaptation.
|
| 105 |
|
| 106 |
+
## Citation
|
| 107 |
+
|
| 108 |
+
```bibtex
|
| 109 |
@article{zhao2026hierarchical,
|
| 110 |
title={Hierarchical Decoding for Discrete Speech Synthesis with Multi-Resolution Spoof Detection},
|
| 111 |
author={Zhao, Junchuan and Vu, Minh Duc and Wang, Ye},
|
| 112 |
journal={arXiv preprint arXiv:2603.05373},
|
| 113 |
year={2026}
|
| 114 |
}
|
| 115 |
+
```
|