Text-to-Speech
Transformers
Safetensors
Kabyle
matoub
feature-extraction
kabyle
taqbaylit
berber
amazigh
speech-synthesis
styletts2
low-resource
custom_code
Instructions to use agbalu/Matoub-82M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use agbalu/Matoub-82M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="agbalu/Matoub-82M", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("agbalu/Matoub-82M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "architectures": [ | |
| "MatoubForTextToWaveform" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_matoub.MatoubConfig", | |
| "AutoModel": "modeling_matoub.MatoubForTextToWaveform", | |
| "AutoModelForTextToWaveform": "modeling_matoub.MatoubForTextToWaveform" | |
| }, | |
| "dropout": 0.2, | |
| "dtype": "float32", | |
| "gen_istft_hop_size": 5, | |
| "gen_istft_n_fft": 20, | |
| "hidden_size": 512, | |
| "max_duration": 50, | |
| "model_type": "matoub", | |
| "num_layers": 3, | |
| "num_mels": 80, | |
| "pad_token_id": 0, | |
| "plbert_hidden_size": 768, | |
| "plbert_intermediate_size": 2048, | |
| "plbert_max_position_embeddings": 512, | |
| "plbert_num_attention_heads": 12, | |
| "plbert_num_hidden_layers": 12, | |
| "resblock_dilation_sizes": [ | |
| [ | |
| 1, | |
| 3, | |
| 5 | |
| ], | |
| [ | |
| 1, | |
| 3, | |
| 5 | |
| ], | |
| [ | |
| 1, | |
| 3, | |
| 5 | |
| ] | |
| ], | |
| "resblock_kernel_sizes": [ | |
| 3, | |
| 7, | |
| 11 | |
| ], | |
| "sampling_rate": 24000, | |
| "style_dim": 128, | |
| "text_encoder_kernel_size": 5, | |
| "transformers_version": "5.12.1", | |
| "upsample_initial_channel": 512, | |
| "upsample_kernel_sizes": [ | |
| 20, | |
| 12 | |
| ], | |
| "upsample_rates": [ | |
| 10, | |
| 6 | |
| ], | |
| "vocab_size": 178 | |
| } | |