Sartify/sukuma-voices

# Sukuma Voices 🎙️ **The first publicly available speech corpus for Sukuma (Kisukuma)**, a Bantu language spoken by approximately 10 million people in northern Tanzania. --- ## 📊 Dataset Overview | Metric | Value | |--------|-------| | Total Samples | 6,871 | | Total Duration | 19.56 hours | | Average Duration | 10.25 ± 4.15 seconds | | Duration Range | 1.40 - 30.36 seconds | | Total Words | 140,325 | | Unique Vocabulary | 21,366 | | Average Words/Sample | 20.4 | | Speaking Rate | 121.6 WPM | ## 🎯 Supported Tasks - **Automatic Speech Recognition (ASR)** — Converting Sukuma speech to text - **Text-to-Speech (TTS)** — Synthesizing natural-sounding Sukuma speech - **Cross-lingual Speech Processing** — Research between Swahili and Sukuma ## 🚀 Quick Start ### Installation ```bash pip install datasets transformers librosa ``` ### Load the Dataset ```python from datasets import load_dataset # Load the dataset from HuggingFace dataset = load_dataset("sartifyllc/Sukuma-Voices", split='train') # View a sample print(dataset[0]) ``` ### ASR Inference Example ```python from transformers import WhisperProcessor, WhisperForConditionalGeneration import torch # Load model and processor model = WhisperForConditionalGeneration.from_pretrained("sartifyllc/sukuma-voices-asr") processor = WhisperProcessor.from_pretrained("sartifyllc/sukuma-voices-asr") # Load and preprocess audio audio_array = ... # Your audio as numpy array at 16kHz input_features = processor( audio_array, sampling_rate=16000, return_tensors="pt" ).input_features # Generate transcription with torch.no_grad(): predicted_ids = model.generate(input_features) # Decode transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] print(transcription) ``` ## 📁 Repository Structure ``` sukuma-voices/ ├── README.md ├── LICENSE ├── scripts/ ├── train_asr.py └── train_tts.py ``` ## 📈 Baseline Results ### ASR Performance (Whisper Large V3) | Metric | Original Speech | Synthetic …