---
datasets:
- rsalshalan/QASR
- DynamicSuperb/DialectIdentification_ADI17
language:
- ar
- en
metrics:
- bleu
- wer
- accuracy
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
pipeline_tag: audio-text-to-text
---
# TinyOctopus: Bilingual Audio Language Model 🐙🔊
## 📢 Overview
**TinyOctopus** is a **Bilingual Audio Language Model (Audio-LLM)** designed to process and generate text from audio inputs. The model leverages **Distil-Whisper (distil-large-v3)** for audio encoding, a **cross-attention projection layer** for alignment, and **DeepSeek 1.5B** for text generation. TinyOctopus is optimized for tasks such as:
- **Bilingual Automatic Speech Recognition (ASR)** 🗣️
- **Arabic to English Speech Translation** 🌍
- **Spoken Arabic Dialect Identification**
TinyOctopus maintaining the architectural principles of the following structure:
## 🏗 Model Architecture
### **TinyOctopus integrates:**
1. **Distil-Whisper (distil-large-v3)** for encoding audio inputs.
2. **Cross-Attention Projection Layer** (trainable) to align audio features with textual representations.
3. **DeepSeek 1.5B** as the core language model for text generation.
## 📂 Dataset
The model has been trained on multiple datasets to optimize its performance across different tasks:
- **[QASR Dataset](https://arxiv.org/pdf/2106.13000)**: QASR is the largest transcribed Arabic speech corpus, collected from the broadcast domain. It contains **2,000 hours of multi-dialect speech** sampled at **16kHz** from **Al Jazeera News Channel**, with lightly supervised transcriptions aligned with the audio segments. Unlike previous datasets, QASR includes **linguistically motivated segmentation, punctuation, speaker information**, and more. The dataset is suitable for **ASR, Arabic dialect identification, punctuation restoration, speaker identification, and NLP applications**. Additionally, a **130M-word language model dataset** is available to aid language modeling. Speech recognition models trained on QASR achieve competitive **WER** compared to the MGB-2 corpus, and it has been used for downstream tasks like **Named Entity Recognition (NER)** and **punctuation restoration**.
- **[ADI17 Dataset](https://swshon.github.io/pdf/shon_2020_adi17.pdf)**: ADI17 is a **large-scale Arabic Dialect Identification (DID) dataset**, collected from **YouTube videos** across **17 Arabic-speaking countries in the Middle East and North Africa**. It contains **3,000 hours of speech** for training DID systems and an additional **57 hours** for development and testing. The dataset is categorized into **short (<5s), medium (5-20s), and long (>20s) speech segments** for detailed evaluation. ADI17 enables state-of-the-art **dialect identification** and provides a robust evaluation platform. It has been benchmarked on **domain-mismatched conditions** using the Multi-Genre Broadcast 3 (MGB-3) test set.
## ⚙️ Installation & Usage
### **💻 Install Dependencies**
```bash
pip install -r requirements.txt
```
## Inference
```bash
from inference import transcribe
audio_path = "path/to/audio.wav" # Replace with your actual audio file
output = transcribe(audio_path, task="asr") # Options: "dialect", "asr", "translation"
print("Generated Text:", output)
```
## Evaluation Results
## ASR Performance (WER & Error Breakdown)
| **Tasks** | **WER (%)** | **Substitution (%)** | **Deletion (%)** | **Insertion (%)** |
|:------------------------------------:|:----------:|:--------------------:|:----------------:|:----------------:|
| **ASR_QASR (Arabic)** | **16.00** | **9.5** | **2.7** | **3.8** |
| **ASR_ibrispeech&tedlium (English)** | **4.50** | **3.0** | **0.8** | **0.7** |
---
## Translation Performance (BLEU Scores)
| **Tasks** | **BLEU (GPT-4o)** | **BLEU (Google)** |
|:--------------:|:----------------:|:----------------:|
| **Translation** | **55.05** | **43.23** |
---
## Dialect Identification Accuracy
| **Tasks** | **Accuracy (%)** |
|:--------------------------:|:---------------:|
| **Dialect Identification** | **70.59** |
