Initial model upload
Browse files- .ipynb_checkpoints/README-checkpoint.md +203 -0
- .ipynb_checkpoints/config-checkpoint.json +180 -0
- README.md +203 -0
- config.json +180 -0
- model.safetensors +3 -0
- preprocessor_config.json +9 -0
- trainer_state.json +552 -0
.ipynb_checkpoints/README-checkpoint.md
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| 1 |
+
---
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| 2 |
+
language:
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| 3 |
+
- ak # Akuapim Twi
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| 4 |
+
- tw # Asante Twi
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| 5 |
+
- aeb # Tunisian Arabic
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| 6 |
+
- af # Afrikaans
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| 7 |
+
- am # Amharic
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| 8 |
+
- ar # Arabic
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| 9 |
+
- bas # Basaa
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| 10 |
+
- bem # Bemba
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| 11 |
+
- dav # Taita
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| 12 |
+
- dyu # Dyula
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| 13 |
+
- en # English
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| 14 |
+
- pcm # Nigerian Pidgin
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| 15 |
+
- ee # Ewe
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| 16 |
+
- fat # Fanti
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| 17 |
+
- fon # Fon
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| 18 |
+
- fuc # Pulaar
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| 19 |
+
- ff # Pular
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| 20 |
+
- gaa # Ga
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| 21 |
+
- ha # Hausa
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| 22 |
+
- ig # Igbo
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| 23 |
+
- kab # Kabyle
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| 24 |
+
- rw # Kinyarwanda
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| 25 |
+
- kln # Kalenjin
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| 26 |
+
- ln # Lingala
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| 27 |
+
- loz # Lozi
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| 28 |
+
- lg # Luganda
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| 29 |
+
- luo # Luo
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| 30 |
+
- mlq # Western Maninkakan
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| 31 |
+
- nr # South Ndebele
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| 32 |
+
- nso # Northern Sotho
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| 33 |
+
- ny # Chichewa
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| 34 |
+
- st # Southern Sotho
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| 35 |
+
- srr # Serer
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| 36 |
+
- ss # Swati
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| 37 |
+
- sus # Susu
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| 38 |
+
- sw # Kiswahili/Swahili
|
| 39 |
+
- tig # Tigre
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| 40 |
+
- ti # Tigrinya
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| 41 |
+
- toi # Tonga
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| 42 |
+
- tn # Tswana
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| 43 |
+
- ts # Tsonga
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| 44 |
+
- tw # Twi
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| 45 |
+
- ve # Venda
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| 46 |
+
- wo # Wolof
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| 47 |
+
- xh # Xhosa
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| 48 |
+
- yo # Yoruba
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| 49 |
+
- zgh # Standard Moroccan Tamazight
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| 50 |
+
- zu # Zulu
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| 51 |
+
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| 52 |
+
license: cc-by-4.0
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| 53 |
+
tags:
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| 54 |
+
- automatic-speech-recognition
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| 55 |
+
- audio
|
| 56 |
+
- speech
|
| 57 |
+
- african-languages
|
| 58 |
+
- multilingual
|
| 59 |
+
- simba
|
| 60 |
+
- low-resource
|
| 61 |
+
- speech-recognition
|
| 62 |
+
- asr
|
| 63 |
+
- spoken-language-identification
|
| 64 |
+
- language-identification
|
| 65 |
+
datasets:
|
| 66 |
+
- UBC-NLP/SimbaBench
|
| 67 |
+
metrics:
|
| 68 |
+
- wer
|
| 69 |
+
- cer
|
| 70 |
+
- accuracy
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| 71 |
+
library_name: transformers
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| 72 |
+
pipeline_tag: automatic-speech-recognition
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| 73 |
+
---
|
| 74 |
+
|
| 75 |
+
<div align="center">
|
| 76 |
+
|
| 77 |
+
<img src="https://africa.dlnlp.ai/simba/images/VoC_logo.png" alt="VoC Logo">
|
| 78 |
+
|
| 79 |
+
[](https://aclanthology.org/2025.emnlp-main.559/)
|
| 80 |
+
[](https://africa.dlnlp.ai/simba/)
|
| 81 |
+
[](https://huggingface.co/spaces/UBC-NLP/SimbaBench)
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| 82 |
+
[](https://github.com/UBC-NLP/simba)
|
| 83 |
+
[](https://huggingface.co/collections/UBC-NLP/simba-speech-series)
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| 84 |
+
[](https://huggingface.co/datasets/UBC-NLP/SimbaBench_dataset)
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| 85 |
+
|
| 86 |
+
</div>
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| 87 |
+
|
| 88 |
+
## *Bridging the Digital Divide for African AI*
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| 89 |
+
|
| 90 |
+
**Voice of a Continent** is a comprehensive open-source ecosystem designed to bring African languages to the forefront of artificial intelligence. By providing a unified suite of benchmarking tools and state-of-the-art models, we ensure that the future of speech technology is inclusive, representative, and accessible to over a billion people.
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| 91 |
+
|
| 92 |
+
## Best-in-Class Multilingual Models
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| 93 |
+
|
| 94 |
+
<img src="https://africa.dlnlp.ai/simba/images/VoC_simba" alt="VoC Simba Models Logo">
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| 95 |
+
|
| 96 |
+
Introduced in our EMNLP 2025 paper *[Voice of a Continent](https://aclanthology.org/2025.emnlp-main.559/)*, the **Simba Series** represents the current state-of-the-art for African speech AI.
|
| 97 |
+
|
| 98 |
+
- **Unified Suite:** Models optimized for African languages.
|
| 99 |
+
- **Superior Accuracy:** Outperforms generic multilingual models by leveraging SimbaBench's high-quality, domain-diverse datasets.
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| 100 |
+
- **Multitask Capability:** Designed for high performance in ASR (Automatic Speech Recognition) and TTS (Text-to-Speech).
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| 101 |
+
- **Inclusion-First:** Specifically built to mitigate the "digital divide" by empowering speakers of underrepresented languages.
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| 102 |
+
|
| 103 |
+
The **Simba** family consists of state-of-the-art models fine-tuned using SimbaBench. These models achieve superior performance by leveraging dataset quality, domain diversity, and language family relationships.
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| 104 |
+
|
| 105 |
+
|
| 106 |
+
### 🔍 Simba-SLID (Spoken Language Identification)
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| 107 |
+
* **🎯 Task:** `Spoken Language Identification` — Intelligent input routing.
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| 108 |
+
* **🌍 Language Coverage (49 African languages)**
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| 109 |
+
> **Akuapim Twi** (`Akuapim-twi`), **Asante Twi** (`Asante-twi`), **Tunisian Arabic** (`aeb`), **Afrikaans** (`afr`), **Amharic** (`amh`), **Arabic** (`ara`), **Basaa** (`bas`), **Bemba** (`bem`), **Taita** (`dav`), **Dyula** (`dyu`), **English** (`eng`), **Nigerian Pidgin** (`eng-zul`), **Ewe** (`ewe`), **Fanti** (`fat`), **Fon** (`fon`), **Pulaar** (`fuc`), **Pular** (`fuf`), **Ga** (`gaa`), **Hausa** (`hau`), **Igbo** (`ibo`), **Kabyle** (`kab`), **Kinyarwanda** (`kin`), **Kalenjin** (`kln`), **Lingala** (`lin`), **Lozi** (`loz`), **Luganda** (`lug`), **Luo** (`luo`), **Western Maninkakan** (`mlq`), **South Ndebele** (`nbl`), **Northern Sotho** (`nso`), **Chichewa** (`nya`), **Southern Sotho** (`sot`), **Serer** (`srr`), **Swati** (`ssw`), **Susu** (`sus`), **Kiswahili** (`swa`), **Swahili** (`swh`), **Tigre** (`tig`), **Tigrinya** (`tir`), **Tonga** (`toi`), **Tswana** (`tsn`), **Tsonga** (`tso`), **Twi** (`twi`), **Venda** (`ven`), **Wolof** (`wol`), **Xhosa** (`xho`), **Yoruba** (`yor`), **Standard Moroccan Tamazight** (`zgh`), **Zulu** (`zul`)
|
| 110 |
+
|
| 111 |
+
| **SLID Model** | **Architecture** | **Hugging Face Card** | **Status** |
|
| 112 |
+
| :--- | :--- | :---: | :---: |
|
| 113 |
+
| **Simba-SLID-49** 🔍 | HuBERT | 🤗 [https://huggingface.co/UBC-NLP/Simba-SLIS-49](https://huggingface.co/UBC-NLPSimba-SLIS-49) | ✅ Released |
|
| 114 |
+
|
| 115 |
+
|
| 116 |
+
**🧩 Usage Example**
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| 117 |
+
|
| 118 |
+
You can easily run inference using the Hugging Face `transformers` library.
|
| 119 |
+
|
| 120 |
+
```python
|
| 121 |
+
from transformers import (
|
| 122 |
+
HubertForSequenceClassification,
|
| 123 |
+
AutoFeatureExtractor,
|
| 124 |
+
AutoProcessor
|
| 125 |
+
)
|
| 126 |
+
import torch
|
| 127 |
+
|
| 128 |
+
model_id = "UBC-NLP/Simba-SLIS_49"
|
| 129 |
+
model = HubertForSequenceClassification.from_pretrained(model_id).to("cuda")
|
| 130 |
+
# HuBERT models can use either processor or feature extractor depending on the specific model
|
| 131 |
+
try:
|
| 132 |
+
processor = AutoProcessor.from_pretrained(model_id)
|
| 133 |
+
print("Loaded Simba-SLIS_49 model with AutoProcessor")
|
| 134 |
+
except:
|
| 135 |
+
processor = AutoFeatureExtractor.from_pretrained(model_id)
|
| 136 |
+
print("Loaded Simba-SLIS_49 model with AutoFeatureExtractor")
|
| 137 |
+
|
| 138 |
+
# Optimize model for inference
|
| 139 |
+
model.eval()
|
| 140 |
+
audio_arrays = [] ### add your audio array
|
| 141 |
+
sample_rate=16000
|
| 142 |
+
|
| 143 |
+
nputs = processor(audio_arrays, sampling_rate=sample_rate, return_tensors="pt", padding=True).to("cuda")
|
| 144 |
+
|
| 145 |
+
# Different models might have slightly different input formats
|
| 146 |
+
try:
|
| 147 |
+
logits = model(**inputs).logits
|
| 148 |
+
except Exception as e:
|
| 149 |
+
# Try alternative input format if the first attempt fails
|
| 150 |
+
if "input_values" in inputs:
|
| 151 |
+
logits = model(input_values=inputs.input_values).logits
|
| 152 |
+
else:
|
| 153 |
+
raise e
|
| 154 |
+
|
| 155 |
+
# Calculate softmax probabilities
|
| 156 |
+
probs = torch.nn.functional.softmax(logits, dim=-1)
|
| 157 |
+
|
| 158 |
+
# Get the maximum probability (confidence) for each prediction
|
| 159 |
+
confidence_values, pred_ids = torch.max(probs, dim=-1)
|
| 160 |
+
|
| 161 |
+
# Convert to Python lists
|
| 162 |
+
pred_ids = pred_ids.tolist()
|
| 163 |
+
confidence_values = confidence_values.cpu().tolist()
|
| 164 |
+
# Get labels from IDs
|
| 165 |
+
pred_labels = [model.config.id2label[i] for i in pred_ids]
|
| 166 |
+
|
| 167 |
+
|
| 168 |
+
print(pred_labels, confidence_values)
|
| 169 |
+
```
|
| 170 |
+
|
| 171 |
+
|
| 172 |
+
## Citation
|
| 173 |
+
|
| 174 |
+
If you use the Simba models or SimbaBench benchmark for your scientific publication, or if you find the resources in this website useful, please cite our paper.
|
| 175 |
+
|
| 176 |
+
```bibtex
|
| 177 |
+
|
| 178 |
+
@inproceedings{elmadany-etal-2025-voice,
|
| 179 |
+
title = "Voice of a Continent: Mapping {A}frica{'}s Speech Technology Frontier",
|
| 180 |
+
author = "Elmadany, AbdelRahim A. and
|
| 181 |
+
Kwon, Sang Yun and
|
| 182 |
+
Toyin, Hawau Olamide and
|
| 183 |
+
Alcoba Inciarte, Alcides and
|
| 184 |
+
Aldarmaki, Hanan and
|
| 185 |
+
Abdul-Mageed, Muhammad",
|
| 186 |
+
editor = "Christodoulopoulos, Christos and
|
| 187 |
+
Chakraborty, Tanmoy and
|
| 188 |
+
Rose, Carolyn and
|
| 189 |
+
Peng, Violet",
|
| 190 |
+
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
|
| 191 |
+
month = nov,
|
| 192 |
+
year = "2025",
|
| 193 |
+
address = "Suzhou, China",
|
| 194 |
+
publisher = "Association for Computational Linguistics",
|
| 195 |
+
url = "https://aclanthology.org/2025.emnlp-main.559/",
|
| 196 |
+
doi = "10.18653/v1/2025.emnlp-main.559",
|
| 197 |
+
pages = "11039--11061",
|
| 198 |
+
ISBN = "979-8-89176-332-6",
|
| 199 |
+
}
|
| 200 |
+
|
| 201 |
+
```
|
| 202 |
+
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| 203 |
+
|
.ipynb_checkpoints/config-checkpoint.json
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "ajesujoba/AfriHuBERT",
|
| 3 |
+
"activation_dropout": 0.1,
|
| 4 |
+
"apply_spec_augment": true,
|
| 5 |
+
"architectures": [
|
| 6 |
+
"HubertForSequenceClassification"
|
| 7 |
+
],
|
| 8 |
+
"attention_dropout": 0.1,
|
| 9 |
+
"bos_token_id": 1,
|
| 10 |
+
"classifier_proj_size": 256,
|
| 11 |
+
"conv_bias": false,
|
| 12 |
+
"conv_dim": [
|
| 13 |
+
512,
|
| 14 |
+
512,
|
| 15 |
+
512,
|
| 16 |
+
512,
|
| 17 |
+
512,
|
| 18 |
+
512,
|
| 19 |
+
512
|
| 20 |
+
],
|
| 21 |
+
"conv_kernel": [
|
| 22 |
+
10,
|
| 23 |
+
3,
|
| 24 |
+
3,
|
| 25 |
+
3,
|
| 26 |
+
3,
|
| 27 |
+
2,
|
| 28 |
+
2
|
| 29 |
+
],
|
| 30 |
+
"conv_pos_batch_norm": false,
|
| 31 |
+
"conv_stride": [
|
| 32 |
+
5,
|
| 33 |
+
2,
|
| 34 |
+
2,
|
| 35 |
+
2,
|
| 36 |
+
2,
|
| 37 |
+
2,
|
| 38 |
+
2
|
| 39 |
+
],
|
| 40 |
+
"ctc_loss_reduction": "sum",
|
| 41 |
+
"ctc_zero_infinity": false,
|
| 42 |
+
"do_stable_layer_norm": false,
|
| 43 |
+
"eos_token_id": 2,
|
| 44 |
+
"feat_extract_activation": "gelu",
|
| 45 |
+
"feat_extract_dropout": 0.0,
|
| 46 |
+
"feat_extract_norm": "group",
|
| 47 |
+
"feat_proj_dropout": 0.1,
|
| 48 |
+
"feat_proj_layer_norm": true,
|
| 49 |
+
"final_dropout": 0.1,
|
| 50 |
+
"finetuning_task": "audio-classification",
|
| 51 |
+
"gradient_checkpointing": false,
|
| 52 |
+
"hidden_act": "gelu",
|
| 53 |
+
"hidden_dropout": 0.1,
|
| 54 |
+
"hidden_dropout_prob": 0.1,
|
| 55 |
+
"hidden_size": 768,
|
| 56 |
+
"id2label": {
|
| 57 |
+
"0": "Akuapim-twi",
|
| 58 |
+
"1": "Asante-twi",
|
| 59 |
+
"10": "eng",
|
| 60 |
+
"11": "eng-zul",
|
| 61 |
+
"12": "ewe",
|
| 62 |
+
"13": "fat",
|
| 63 |
+
"14": "fon",
|
| 64 |
+
"15": "fuc",
|
| 65 |
+
"16": "fuf",
|
| 66 |
+
"17": "gaa",
|
| 67 |
+
"18": "hau",
|
| 68 |
+
"19": "ibo",
|
| 69 |
+
"2": "aeb",
|
| 70 |
+
"20": "kab",
|
| 71 |
+
"21": "kin",
|
| 72 |
+
"22": "kln",
|
| 73 |
+
"23": "lin",
|
| 74 |
+
"24": "loz",
|
| 75 |
+
"25": "lug",
|
| 76 |
+
"26": "luo",
|
| 77 |
+
"27": "mlq",
|
| 78 |
+
"28": "nbl",
|
| 79 |
+
"29": "nso",
|
| 80 |
+
"3": "afr",
|
| 81 |
+
"30": "nya",
|
| 82 |
+
"31": "sot",
|
| 83 |
+
"32": "srr",
|
| 84 |
+
"33": "ssw",
|
| 85 |
+
"34": "sus",
|
| 86 |
+
"35": "swa",
|
| 87 |
+
"36": "swh",
|
| 88 |
+
"37": "tig",
|
| 89 |
+
"38": "tir",
|
| 90 |
+
"39": "toi",
|
| 91 |
+
"4": "amh",
|
| 92 |
+
"40": "tsn",
|
| 93 |
+
"41": "tso",
|
| 94 |
+
"42": "twi",
|
| 95 |
+
"43": "ven",
|
| 96 |
+
"44": "wol",
|
| 97 |
+
"45": "xho",
|
| 98 |
+
"46": "yor",
|
| 99 |
+
"47": "zgh",
|
| 100 |
+
"48": "zul",
|
| 101 |
+
"5": "ara",
|
| 102 |
+
"6": "bas",
|
| 103 |
+
"7": "bem",
|
| 104 |
+
"8": "dav",
|
| 105 |
+
"9": "dyu"
|
| 106 |
+
},
|
| 107 |
+
"initializer_range": 0.02,
|
| 108 |
+
"intermediate_size": 3072,
|
| 109 |
+
"label2id": {
|
| 110 |
+
"Akuapim-twi": "0",
|
| 111 |
+
"Asante-twi": "1",
|
| 112 |
+
"aeb": "2",
|
| 113 |
+
"afr": "3",
|
| 114 |
+
"amh": "4",
|
| 115 |
+
"ara": "5",
|
| 116 |
+
"bas": "6",
|
| 117 |
+
"bem": "7",
|
| 118 |
+
"dav": "8",
|
| 119 |
+
"dyu": "9",
|
| 120 |
+
"eng": "10",
|
| 121 |
+
"eng-zul": "11",
|
| 122 |
+
"ewe": "12",
|
| 123 |
+
"fat": "13",
|
| 124 |
+
"fon": "14",
|
| 125 |
+
"fuc": "15",
|
| 126 |
+
"fuf": "16",
|
| 127 |
+
"gaa": "17",
|
| 128 |
+
"hau": "18",
|
| 129 |
+
"ibo": "19",
|
| 130 |
+
"kab": "20",
|
| 131 |
+
"kin": "21",
|
| 132 |
+
"kln": "22",
|
| 133 |
+
"lin": "23",
|
| 134 |
+
"loz": "24",
|
| 135 |
+
"lug": "25",
|
| 136 |
+
"luo": "26",
|
| 137 |
+
"mlq": "27",
|
| 138 |
+
"nbl": "28",
|
| 139 |
+
"nso": "29",
|
| 140 |
+
"nya": "30",
|
| 141 |
+
"sot": "31",
|
| 142 |
+
"srr": "32",
|
| 143 |
+
"ssw": "33",
|
| 144 |
+
"sus": "34",
|
| 145 |
+
"swa": "35",
|
| 146 |
+
"swh": "36",
|
| 147 |
+
"tig": "37",
|
| 148 |
+
"tir": "38",
|
| 149 |
+
"toi": "39",
|
| 150 |
+
"tsn": "40",
|
| 151 |
+
"tso": "41",
|
| 152 |
+
"twi": "42",
|
| 153 |
+
"ven": "43",
|
| 154 |
+
"wol": "44",
|
| 155 |
+
"xho": "45",
|
| 156 |
+
"yor": "46",
|
| 157 |
+
"zgh": "47",
|
| 158 |
+
"zul": "48"
|
| 159 |
+
},
|
| 160 |
+
"layer_norm_eps": 1e-05,
|
| 161 |
+
"layerdrop": 0.1,
|
| 162 |
+
"mask_feature_length": 10,
|
| 163 |
+
"mask_feature_min_masks": 0,
|
| 164 |
+
"mask_feature_prob": 0.0,
|
| 165 |
+
"mask_time_length": 10,
|
| 166 |
+
"mask_time_min_masks": 2,
|
| 167 |
+
"mask_time_prob": 0.05,
|
| 168 |
+
"model_type": "hubert",
|
| 169 |
+
"num_attention_heads": 12,
|
| 170 |
+
"num_conv_pos_embedding_groups": 16,
|
| 171 |
+
"num_conv_pos_embeddings": 128,
|
| 172 |
+
"num_feat_extract_layers": 7,
|
| 173 |
+
"num_hidden_layers": 12,
|
| 174 |
+
"pad_token_id": 0,
|
| 175 |
+
"tokenizer_class": "Wav2Vec2CTCTokenizer",
|
| 176 |
+
"torch_dtype": "float32",
|
| 177 |
+
"transformers_version": "4.48.1",
|
| 178 |
+
"use_weighted_layer_sum": false,
|
| 179 |
+
"vocab_size": 32
|
| 180 |
+
}
|
README.md
ADDED
|
@@ -0,0 +1,203 @@
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- ak # Akuapim Twi
|
| 4 |
+
- tw # Asante Twi
|
| 5 |
+
- aeb # Tunisian Arabic
|
| 6 |
+
- af # Afrikaans
|
| 7 |
+
- am # Amharic
|
| 8 |
+
- ar # Arabic
|
| 9 |
+
- bas # Basaa
|
| 10 |
+
- bem # Bemba
|
| 11 |
+
- dav # Taita
|
| 12 |
+
- dyu # Dyula
|
| 13 |
+
- en # English
|
| 14 |
+
- pcm # Nigerian Pidgin
|
| 15 |
+
- ee # Ewe
|
| 16 |
+
- fat # Fanti
|
| 17 |
+
- fon # Fon
|
| 18 |
+
- fuc # Pulaar
|
| 19 |
+
- ff # Pular
|
| 20 |
+
- gaa # Ga
|
| 21 |
+
- ha # Hausa
|
| 22 |
+
- ig # Igbo
|
| 23 |
+
- kab # Kabyle
|
| 24 |
+
- rw # Kinyarwanda
|
| 25 |
+
- kln # Kalenjin
|
| 26 |
+
- ln # Lingala
|
| 27 |
+
- loz # Lozi
|
| 28 |
+
- lg # Luganda
|
| 29 |
+
- luo # Luo
|
| 30 |
+
- mlq # Western Maninkakan
|
| 31 |
+
- nr # South Ndebele
|
| 32 |
+
- nso # Northern Sotho
|
| 33 |
+
- ny # Chichewa
|
| 34 |
+
- st # Southern Sotho
|
| 35 |
+
- srr # Serer
|
| 36 |
+
- ss # Swati
|
| 37 |
+
- sus # Susu
|
| 38 |
+
- sw # Kiswahili/Swahili
|
| 39 |
+
- tig # Tigre
|
| 40 |
+
- ti # Tigrinya
|
| 41 |
+
- toi # Tonga
|
| 42 |
+
- tn # Tswana
|
| 43 |
+
- ts # Tsonga
|
| 44 |
+
- tw # Twi
|
| 45 |
+
- ve # Venda
|
| 46 |
+
- wo # Wolof
|
| 47 |
+
- xh # Xhosa
|
| 48 |
+
- yo # Yoruba
|
| 49 |
+
- zgh # Standard Moroccan Tamazight
|
| 50 |
+
- zu # Zulu
|
| 51 |
+
|
| 52 |
+
license: cc-by-4.0
|
| 53 |
+
tags:
|
| 54 |
+
- automatic-speech-recognition
|
| 55 |
+
- audio
|
| 56 |
+
- speech
|
| 57 |
+
- african-languages
|
| 58 |
+
- multilingual
|
| 59 |
+
- simba
|
| 60 |
+
- low-resource
|
| 61 |
+
- speech-recognition
|
| 62 |
+
- asr
|
| 63 |
+
- spoken-language-identification
|
| 64 |
+
- language-identification
|
| 65 |
+
datasets:
|
| 66 |
+
- UBC-NLP/SimbaBench
|
| 67 |
+
metrics:
|
| 68 |
+
- wer
|
| 69 |
+
- cer
|
| 70 |
+
- accuracy
|
| 71 |
+
library_name: transformers
|
| 72 |
+
pipeline_tag: automatic-speech-recognition
|
| 73 |
+
---
|
| 74 |
+
|
| 75 |
+
<div align="center">
|
| 76 |
+
|
| 77 |
+
<img src="https://africa.dlnlp.ai/simba/images/VoC_logo.png" alt="VoC Logo">
|
| 78 |
+
|
| 79 |
+
[](https://aclanthology.org/2025.emnlp-main.559/)
|
| 80 |
+
[](https://africa.dlnlp.ai/simba/)
|
| 81 |
+
[](https://huggingface.co/spaces/UBC-NLP/SimbaBench)
|
| 82 |
+
[](https://github.com/UBC-NLP/simba)
|
| 83 |
+
[](https://huggingface.co/collections/UBC-NLP/simba-speech-series)
|
| 84 |
+
[](https://huggingface.co/datasets/UBC-NLP/SimbaBench_dataset)
|
| 85 |
+
|
| 86 |
+
</div>
|
| 87 |
+
|
| 88 |
+
## *Bridging the Digital Divide for African AI*
|
| 89 |
+
|
| 90 |
+
**Voice of a Continent** is a comprehensive open-source ecosystem designed to bring African languages to the forefront of artificial intelligence. By providing a unified suite of benchmarking tools and state-of-the-art models, we ensure that the future of speech technology is inclusive, representative, and accessible to over a billion people.
|
| 91 |
+
|
| 92 |
+
## Best-in-Class Multilingual Models
|
| 93 |
+
|
| 94 |
+
<img src="https://africa.dlnlp.ai/simba/images/VoC_simba" alt="VoC Simba Models Logo">
|
| 95 |
+
|
| 96 |
+
Introduced in our EMNLP 2025 paper *[Voice of a Continent](https://aclanthology.org/2025.emnlp-main.559/)*, the **Simba Series** represents the current state-of-the-art for African speech AI.
|
| 97 |
+
|
| 98 |
+
- **Unified Suite:** Models optimized for African languages.
|
| 99 |
+
- **Superior Accuracy:** Outperforms generic multilingual models by leveraging SimbaBench's high-quality, domain-diverse datasets.
|
| 100 |
+
- **Multitask Capability:** Designed for high performance in ASR (Automatic Speech Recognition) and TTS (Text-to-Speech).
|
| 101 |
+
- **Inclusion-First:** Specifically built to mitigate the "digital divide" by empowering speakers of underrepresented languages.
|
| 102 |
+
|
| 103 |
+
The **Simba** family consists of state-of-the-art models fine-tuned using SimbaBench. These models achieve superior performance by leveraging dataset quality, domain diversity, and language family relationships.
|
| 104 |
+
|
| 105 |
+
|
| 106 |
+
### 🔍 Simba-SLID (Spoken Language Identification)
|
| 107 |
+
* **🎯 Task:** `Spoken Language Identification` — Intelligent input routing.
|
| 108 |
+
* **🌍 Language Coverage (49 African languages)**
|
| 109 |
+
> **Akuapim Twi** (`Akuapim-twi`), **Asante Twi** (`Asante-twi`), **Tunisian Arabic** (`aeb`), **Afrikaans** (`afr`), **Amharic** (`amh`), **Arabic** (`ara`), **Basaa** (`bas`), **Bemba** (`bem`), **Taita** (`dav`), **Dyula** (`dyu`), **English** (`eng`), **Nigerian Pidgin** (`eng-zul`), **Ewe** (`ewe`), **Fanti** (`fat`), **Fon** (`fon`), **Pulaar** (`fuc`), **Pular** (`fuf`), **Ga** (`gaa`), **Hausa** (`hau`), **Igbo** (`ibo`), **Kabyle** (`kab`), **Kinyarwanda** (`kin`), **Kalenjin** (`kln`), **Lingala** (`lin`), **Lozi** (`loz`), **Luganda** (`lug`), **Luo** (`luo`), **Western Maninkakan** (`mlq`), **South Ndebele** (`nbl`), **Northern Sotho** (`nso`), **Chichewa** (`nya`), **Southern Sotho** (`sot`), **Serer** (`srr`), **Swati** (`ssw`), **Susu** (`sus`), **Kiswahili** (`swa`), **Swahili** (`swh`), **Tigre** (`tig`), **Tigrinya** (`tir`), **Tonga** (`toi`), **Tswana** (`tsn`), **Tsonga** (`tso`), **Twi** (`twi`), **Venda** (`ven`), **Wolof** (`wol`), **Xhosa** (`xho`), **Yoruba** (`yor`), **Standard Moroccan Tamazight** (`zgh`), **Zulu** (`zul`)
|
| 110 |
+
|
| 111 |
+
| **SLID Model** | **Architecture** | **Hugging Face Card** | **Status** |
|
| 112 |
+
| :--- | :--- | :---: | :---: |
|
| 113 |
+
| **Simba-SLID-49** 🔍 | HuBERT | 🤗 [https://huggingface.co/UBC-NLP/Simba-SLIS-49](https://huggingface.co/UBC-NLPSimba-SLIS-49) | ✅ Released |
|
| 114 |
+
|
| 115 |
+
|
| 116 |
+
**🧩 Usage Example**
|
| 117 |
+
|
| 118 |
+
You can easily run inference using the Hugging Face `transformers` library.
|
| 119 |
+
|
| 120 |
+
```python
|
| 121 |
+
from transformers import (
|
| 122 |
+
HubertForSequenceClassification,
|
| 123 |
+
AutoFeatureExtractor,
|
| 124 |
+
AutoProcessor
|
| 125 |
+
)
|
| 126 |
+
import torch
|
| 127 |
+
|
| 128 |
+
model_id = "UBC-NLP/Simba-SLIS_49"
|
| 129 |
+
model = HubertForSequenceClassification.from_pretrained(model_id).to("cuda")
|
| 130 |
+
# HuBERT models can use either processor or feature extractor depending on the specific model
|
| 131 |
+
try:
|
| 132 |
+
processor = AutoProcessor.from_pretrained(model_id)
|
| 133 |
+
print("Loaded Simba-SLIS_49 model with AutoProcessor")
|
| 134 |
+
except:
|
| 135 |
+
processor = AutoFeatureExtractor.from_pretrained(model_id)
|
| 136 |
+
print("Loaded Simba-SLIS_49 model with AutoFeatureExtractor")
|
| 137 |
+
|
| 138 |
+
# Optimize model for inference
|
| 139 |
+
model.eval()
|
| 140 |
+
audio_arrays = [] ### add your audio array
|
| 141 |
+
sample_rate=16000
|
| 142 |
+
|
| 143 |
+
nputs = processor(audio_arrays, sampling_rate=sample_rate, return_tensors="pt", padding=True).to("cuda")
|
| 144 |
+
|
| 145 |
+
# Different models might have slightly different input formats
|
| 146 |
+
try:
|
| 147 |
+
logits = model(**inputs).logits
|
| 148 |
+
except Exception as e:
|
| 149 |
+
# Try alternative input format if the first attempt fails
|
| 150 |
+
if "input_values" in inputs:
|
| 151 |
+
logits = model(input_values=inputs.input_values).logits
|
| 152 |
+
else:
|
| 153 |
+
raise e
|
| 154 |
+
|
| 155 |
+
# Calculate softmax probabilities
|
| 156 |
+
probs = torch.nn.functional.softmax(logits, dim=-1)
|
| 157 |
+
|
| 158 |
+
# Get the maximum probability (confidence) for each prediction
|
| 159 |
+
confidence_values, pred_ids = torch.max(probs, dim=-1)
|
| 160 |
+
|
| 161 |
+
# Convert to Python lists
|
| 162 |
+
pred_ids = pred_ids.tolist()
|
| 163 |
+
confidence_values = confidence_values.cpu().tolist()
|
| 164 |
+
# Get labels from IDs
|
| 165 |
+
pred_labels = [model.config.id2label[i] for i in pred_ids]
|
| 166 |
+
|
| 167 |
+
|
| 168 |
+
print(pred_labels, confidence_values)
|
| 169 |
+
```
|
| 170 |
+
|
| 171 |
+
|
| 172 |
+
## Citation
|
| 173 |
+
|
| 174 |
+
If you use the Simba models or SimbaBench benchmark for your scientific publication, or if you find the resources in this website useful, please cite our paper.
|
| 175 |
+
|
| 176 |
+
```bibtex
|
| 177 |
+
|
| 178 |
+
@inproceedings{elmadany-etal-2025-voice,
|
| 179 |
+
title = "Voice of a Continent: Mapping {A}frica{'}s Speech Technology Frontier",
|
| 180 |
+
author = "Elmadany, AbdelRahim A. and
|
| 181 |
+
Kwon, Sang Yun and
|
| 182 |
+
Toyin, Hawau Olamide and
|
| 183 |
+
Alcoba Inciarte, Alcides and
|
| 184 |
+
Aldarmaki, Hanan and
|
| 185 |
+
Abdul-Mageed, Muhammad",
|
| 186 |
+
editor = "Christodoulopoulos, Christos and
|
| 187 |
+
Chakraborty, Tanmoy and
|
| 188 |
+
Rose, Carolyn and
|
| 189 |
+
Peng, Violet",
|
| 190 |
+
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
|
| 191 |
+
month = nov,
|
| 192 |
+
year = "2025",
|
| 193 |
+
address = "Suzhou, China",
|
| 194 |
+
publisher = "Association for Computational Linguistics",
|
| 195 |
+
url = "https://aclanthology.org/2025.emnlp-main.559/",
|
| 196 |
+
doi = "10.18653/v1/2025.emnlp-main.559",
|
| 197 |
+
pages = "11039--11061",
|
| 198 |
+
ISBN = "979-8-89176-332-6",
|
| 199 |
+
}
|
| 200 |
+
|
| 201 |
+
```
|
| 202 |
+
|
| 203 |
+
|
config.json
ADDED
|
@@ -0,0 +1,180 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "ajesujoba/AfriHuBERT",
|
| 3 |
+
"activation_dropout": 0.1,
|
| 4 |
+
"apply_spec_augment": true,
|
| 5 |
+
"architectures": [
|
| 6 |
+
"HubertForSequenceClassification"
|
| 7 |
+
],
|
| 8 |
+
"attention_dropout": 0.1,
|
| 9 |
+
"bos_token_id": 1,
|
| 10 |
+
"classifier_proj_size": 256,
|
| 11 |
+
"conv_bias": false,
|
| 12 |
+
"conv_dim": [
|
| 13 |
+
512,
|
| 14 |
+
512,
|
| 15 |
+
512,
|
| 16 |
+
512,
|
| 17 |
+
512,
|
| 18 |
+
512,
|
| 19 |
+
512
|
| 20 |
+
],
|
| 21 |
+
"conv_kernel": [
|
| 22 |
+
10,
|
| 23 |
+
3,
|
| 24 |
+
3,
|
| 25 |
+
3,
|
| 26 |
+
3,
|
| 27 |
+
2,
|
| 28 |
+
2
|
| 29 |
+
],
|
| 30 |
+
"conv_pos_batch_norm": false,
|
| 31 |
+
"conv_stride": [
|
| 32 |
+
5,
|
| 33 |
+
2,
|
| 34 |
+
2,
|
| 35 |
+
2,
|
| 36 |
+
2,
|
| 37 |
+
2,
|
| 38 |
+
2
|
| 39 |
+
],
|
| 40 |
+
"ctc_loss_reduction": "sum",
|
| 41 |
+
"ctc_zero_infinity": false,
|
| 42 |
+
"do_stable_layer_norm": false,
|
| 43 |
+
"eos_token_id": 2,
|
| 44 |
+
"feat_extract_activation": "gelu",
|
| 45 |
+
"feat_extract_dropout": 0.0,
|
| 46 |
+
"feat_extract_norm": "group",
|
| 47 |
+
"feat_proj_dropout": 0.1,
|
| 48 |
+
"feat_proj_layer_norm": true,
|
| 49 |
+
"final_dropout": 0.1,
|
| 50 |
+
"finetuning_task": "audio-classification",
|
| 51 |
+
"gradient_checkpointing": false,
|
| 52 |
+
"hidden_act": "gelu",
|
| 53 |
+
"hidden_dropout": 0.1,
|
| 54 |
+
"hidden_dropout_prob": 0.1,
|
| 55 |
+
"hidden_size": 768,
|
| 56 |
+
"id2label": {
|
| 57 |
+
"0": "Akuapim-twi",
|
| 58 |
+
"1": "Asante-twi",
|
| 59 |
+
"10": "eng",
|
| 60 |
+
"11": "eng-zul",
|
| 61 |
+
"12": "ewe",
|
| 62 |
+
"13": "fat",
|
| 63 |
+
"14": "fon",
|
| 64 |
+
"15": "fuc",
|
| 65 |
+
"16": "fuf",
|
| 66 |
+
"17": "gaa",
|
| 67 |
+
"18": "hau",
|
| 68 |
+
"19": "ibo",
|
| 69 |
+
"2": "aeb",
|
| 70 |
+
"20": "kab",
|
| 71 |
+
"21": "kin",
|
| 72 |
+
"22": "kln",
|
| 73 |
+
"23": "lin",
|
| 74 |
+
"24": "loz",
|
| 75 |
+
"25": "lug",
|
| 76 |
+
"26": "luo",
|
| 77 |
+
"27": "mlq",
|
| 78 |
+
"28": "nbl",
|
| 79 |
+
"29": "nso",
|
| 80 |
+
"3": "afr",
|
| 81 |
+
"30": "nya",
|
| 82 |
+
"31": "sot",
|
| 83 |
+
"32": "srr",
|
| 84 |
+
"33": "ssw",
|
| 85 |
+
"34": "sus",
|
| 86 |
+
"35": "swa",
|
| 87 |
+
"36": "swh",
|
| 88 |
+
"37": "tig",
|
| 89 |
+
"38": "tir",
|
| 90 |
+
"39": "toi",
|
| 91 |
+
"4": "amh",
|
| 92 |
+
"40": "tsn",
|
| 93 |
+
"41": "tso",
|
| 94 |
+
"42": "twi",
|
| 95 |
+
"43": "ven",
|
| 96 |
+
"44": "wol",
|
| 97 |
+
"45": "xho",
|
| 98 |
+
"46": "yor",
|
| 99 |
+
"47": "zgh",
|
| 100 |
+
"48": "zul",
|
| 101 |
+
"5": "ara",
|
| 102 |
+
"6": "bas",
|
| 103 |
+
"7": "bem",
|
| 104 |
+
"8": "dav",
|
| 105 |
+
"9": "dyu"
|
| 106 |
+
},
|
| 107 |
+
"initializer_range": 0.02,
|
| 108 |
+
"intermediate_size": 3072,
|
| 109 |
+
"label2id": {
|
| 110 |
+
"Akuapim-twi": "0",
|
| 111 |
+
"Asante-twi": "1",
|
| 112 |
+
"aeb": "2",
|
| 113 |
+
"afr": "3",
|
| 114 |
+
"amh": "4",
|
| 115 |
+
"ara": "5",
|
| 116 |
+
"bas": "6",
|
| 117 |
+
"bem": "7",
|
| 118 |
+
"dav": "8",
|
| 119 |
+
"dyu": "9",
|
| 120 |
+
"eng": "10",
|
| 121 |
+
"eng-zul": "11",
|
| 122 |
+
"ewe": "12",
|
| 123 |
+
"fat": "13",
|
| 124 |
+
"fon": "14",
|
| 125 |
+
"fuc": "15",
|
| 126 |
+
"fuf": "16",
|
| 127 |
+
"gaa": "17",
|
| 128 |
+
"hau": "18",
|
| 129 |
+
"ibo": "19",
|
| 130 |
+
"kab": "20",
|
| 131 |
+
"kin": "21",
|
| 132 |
+
"kln": "22",
|
| 133 |
+
"lin": "23",
|
| 134 |
+
"loz": "24",
|
| 135 |
+
"lug": "25",
|
| 136 |
+
"luo": "26",
|
| 137 |
+
"mlq": "27",
|
| 138 |
+
"nbl": "28",
|
| 139 |
+
"nso": "29",
|
| 140 |
+
"nya": "30",
|
| 141 |
+
"sot": "31",
|
| 142 |
+
"srr": "32",
|
| 143 |
+
"ssw": "33",
|
| 144 |
+
"sus": "34",
|
| 145 |
+
"swa": "35",
|
| 146 |
+
"swh": "36",
|
| 147 |
+
"tig": "37",
|
| 148 |
+
"tir": "38",
|
| 149 |
+
"toi": "39",
|
| 150 |
+
"tsn": "40",
|
| 151 |
+
"tso": "41",
|
| 152 |
+
"twi": "42",
|
| 153 |
+
"ven": "43",
|
| 154 |
+
"wol": "44",
|
| 155 |
+
"xho": "45",
|
| 156 |
+
"yor": "46",
|
| 157 |
+
"zgh": "47",
|
| 158 |
+
"zul": "48"
|
| 159 |
+
},
|
| 160 |
+
"layer_norm_eps": 1e-05,
|
| 161 |
+
"layerdrop": 0.1,
|
| 162 |
+
"mask_feature_length": 10,
|
| 163 |
+
"mask_feature_min_masks": 0,
|
| 164 |
+
"mask_feature_prob": 0.0,
|
| 165 |
+
"mask_time_length": 10,
|
| 166 |
+
"mask_time_min_masks": 2,
|
| 167 |
+
"mask_time_prob": 0.05,
|
| 168 |
+
"model_type": "hubert",
|
| 169 |
+
"num_attention_heads": 12,
|
| 170 |
+
"num_conv_pos_embedding_groups": 16,
|
| 171 |
+
"num_conv_pos_embeddings": 128,
|
| 172 |
+
"num_feat_extract_layers": 7,
|
| 173 |
+
"num_hidden_layers": 12,
|
| 174 |
+
"pad_token_id": 0,
|
| 175 |
+
"tokenizer_class": "Wav2Vec2CTCTokenizer",
|
| 176 |
+
"torch_dtype": "float32",
|
| 177 |
+
"transformers_version": "4.48.1",
|
| 178 |
+
"use_weighted_layer_sum": false,
|
| 179 |
+
"vocab_size": 32
|
| 180 |
+
}
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b093d79e77272669d34041d5b010e0d79c6fa0e0222b94cf300f24019786eb14
|
| 3 |
+
size 378350268
|
preprocessor_config.json
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"do_normalize": true,
|
| 3 |
+
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
|
| 4 |
+
"feature_size": 1,
|
| 5 |
+
"padding_side": "right",
|
| 6 |
+
"padding_value": 0,
|
| 7 |
+
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| 8 |
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| 9 |
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ADDED
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