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README.md

@@ -1,22 +1,174 @@
 ---
+language:
+- tw
+license: cc-by-4.0
+task_categories:
+- automatic-speech-recognition
+- text-to-speech
+task_ids:
+- keyword-spotting
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+modalities:
+- audio
+- text
 dataset_info:
   features:
   - name: audio
     dtype: audio
   - name: text
     dtype: string
+  config_name: default
   splits:
   - name: train
-    num_bytes: 5239359293.898
+    num_bytes: 0
     num_examples: 413463
-  download_size: 5237405839
-  dataset_size: 5239359293.898
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+  download_size: 0
+  dataset_size: 0
+tags:
+- speech
+- twi
+- akan
+- ghana
+- african-languages
+- low-resource
+- parallel-corpus
+pretty_name: Twi Words Speech-Text Parallel Dataset
 ---
-# Dataset Card for "twi-words-speech-text-parallel-v2"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+# Twi Words Speech-Text Parallel Dataset
+
+## Dataset Description
+
+This dataset contains 413463 parallel speech-text pairs for Twi (Akan), a language spoken primarily in Ghana. The dataset consists of audio recordings paired with their corresponding text transcriptions, making it suitable for automatic speech recognition (ASR) and text-to-speech (TTS) tasks.
+
+### Dataset Summary
+
+- **Language**: Twi (Akan) - `tw`
+- **Task**: Speech Recognition, Text-to-Speech
+- **Size**: 413463 audio files > 1KB (small/corrupted files filtered out)
+- **Format**: WAV audio files with corresponding text labels
+- **Modalities**: Audio + Text
+
+### Supported Tasks
+
+- **Automatic Speech Recognition (ASR)**: Train models to convert Twi speech to text
+- **Text-to-Speech (TTS)**: Use parallel data for TTS model development
+- **Keyword Spotting**: Identify specific Twi words in audio
+- **Phonetic Analysis**: Study Twi pronunciation patterns
+
+## Dataset Structure
+
+### Data Fields
+
+- `audio`: Audio file in WAV format
+- `text`: Corresponding text transcription
+
+### Data Splits
+
+The dataset contains a single training split with 413463 filtered audio files.
+
+### File Structure
+
+Each audio segment is stored as a numbered pair:
+- `NNNN.wav`: Audio file (e.g., `0001.wav`)
+- `NNNN.txt`: Corresponding text file (e.g., `0001.txt`)
+
+This structure ensures clean organization and easy pairing of audio-text data.
+
+## Dataset Creation
+
+### Source Data
+
+The audio data has been sourced ethically from consenting contributors. To protect the privacy of the original authors and speakers, specific source information cannot be shared publicly.
+
+### Data Processing
+
+1. Audio files were processed using forced alignment techniques
+2. Word-level segmentation was performed with padding to prevent abrupt cuts
+3. Audio segments were filtered based on:
+   - Minimum duration requirements
+   - Volume/vocal content thresholds
+   - File size validation (> 1KB)
+4. Each valid segment was saved as a numbered audio-text pair
+5. Audio processing used the [MMS-300M-1130 Forced Aligner](https://huggingface.co/MahmoudAshraf/mms-300m-1130-forced-aligner) tool for alignment and quality assurance
+
+### Quality Control
+
+- Empty or silent audio segments were automatically filtered out
+- Very short segments (< 200ms) were excluded
+- Low-volume segments were removed to ensure vocal content
+- Audio padding (100ms) was added to prevent abrupt word cuts
+
+### Annotations
+
+Text annotations are stored in separate `.txt` files corresponding to each audio file, representing the exact spoken content in each audio segment.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset contributes to the preservation and digital representation of Twi, supporting:
+- Language technology development for underrepresented languages
+- Educational resources for Twi language learning
+- Cultural preservation through digital archives
+
+### Discussion of Biases
+
+- The dataset may reflect the pronunciation patterns and dialects of specific regions or speakers
+- Audio quality and recording conditions may vary across samples
+- The vocabulary is limited to the words present in the collected samples
+
+### Other Known Limitations
+
+- Limited vocabulary scope (word-level rather than sentence-level)
+- Potential audio quality variations
+- Regional dialect representation may be uneven
+- Automatic filtering may have removed some valid segments
+
+## Additional Information
+
+### Licensing Information
+
+This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
+
+### Acknowledgments
+
+- Audio processing and alignment performed using [MMS-300M-1130 Forced Aligner](https://huggingface.co/MahmoudAshraf/mms-300m-1130-forced-aligner)
+- The original audio is produced by The Ghana Institute of Linguistics, Literacy and Bible Translation in partnership with Davar Partners
+- Automated quality filtering and padding applied to ensure high-quality audio segments
+
+### Citation Information
+
+If you use this dataset in your research, please cite:
+
+```
+@dataset{twi_words_parallel_2025,
+  title={Twi Words Speech-Text Parallel Dataset},
+  year={2025},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/datasets/michsethowusu/twi-words-speech-text-parallel}}
+}
+```
+
+### Contact
+
+For questions or concerns about this dataset, please open an issue in the dataset repository.
+
+## Usage Example
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("michsethowusu/twi-words-speech-text-parallel")
+
+# Access audio and text pairs
+for example in dataset["train"]:
+    audio = example["audio"]
+    text = example["text"]
+    print(f"Text: {text}")
+    print(f"Audio sample rate: {audio['sampling_rate']}")
+```