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
File size: 1,135 Bytes
e044cab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | {
"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
}
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