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README.md

@@ -1,19 +1,164 @@
 ---
+language:
+- ny
+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: 6478689352.88
+    num_bytes: 0
     num_examples: 132549
-  download_size: 4939483028
-  dataset_size: 6478689352.88
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+  download_size: 0
+  dataset_size: 0
+tags:
+- speech
+- chichewa
+- akan
+- ghana
+- african-languages
+- low-resource
+- parallel-corpus
+- trigrams
+- n-grams
+pretty_name: Chichewa Trigrams Speech-Text Parallel Dataset
 ---
+
+# Chichewa Trigrams Speech-Text Parallel Dataset
+
+## Dataset Description
+
+This dataset contains 132549 parallel speech-text pairs for Chichewa, a language spoken primarily in Malawi. The dataset consists of audio recordings of trigram segments (3-word sequences) paired with their corresponding text transcriptions, making it suitable for automatic speech recognition (ASR) and text-to-speech (TTS) tasks.
+
+### Dataset Summary
+
+- **Language**: Chichewa - `ny`
+- **Task**: Speech Recognition, Text-to-Speech
+- **Size**: 132549 trigram audio segments > 1KB (small/corrupted files filtered out)
+- **Format**: WAV audio files with corresponding trigram text labels
+- **Segment Type**: Primarily trigrams (3-word sequences), with some bigrams and single words as fallbacks
+- **Modalities**: Audio + Text
+
+### Supported Tasks
+
+- **Automatic Speech Recognition (ASR)**: Train models to convert Chichewa speech to text
+- **Text-to-Speech (TTS)**: Use parallel data for TTS model development
+- **Keyword Spotting**: Identify specific Chichewa word sequences in audio
+- **N-gram Language Modeling**: Study Chichewa trigram patterns
+- **Phonetic Analysis**: Study Chichewa pronunciation patterns in context
+
+## Dataset Structure
+
+### Data Fields
+
+- `audio`: Audio file in WAV format containing a trigram segment
+- `text`: Corresponding text transcription (typically 3 words, sometimes 2 or 1 for shorter segments)
+
+### Data Splits
+
+The dataset contains a single training split with 132549 filtered trigram audio segments.
+
+## 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 Alignment**: Original audio files were processed using forced alignment to obtain word-level timestamps
+2. **Trigram Segmentation**: Audio was segmented into overlapping trigrams (3-word sequences)
+3. **Fallback Segmentation**: For shorter texts, bigrams or single words were created as needed
+4. **Quality Filtering**: 
+   - Segments longer than 30 seconds were excluded
+   - Segments shorter than 0.1 seconds were excluded
+   - Files smaller than 1KB were filtered out to ensure audio quality
+5. **Text Processing**: Text was lowercased and cleaned of end punctuation
+6. **Unique Naming**: Each segment received a unique sequential filename (trigram_XXXXXX.wav)
+
+### Alignment Technology
+
+Audio processing and word-level alignment performed using the [MMS-300M-1130 Forced Aligner](https://huggingface.co/MahmoudAshraf/mms-300m-1130-forced-aligner) tool, which provides accurate timestamp information for creating precise trigram segments.
+
+### Annotations
+
+Text annotations represent the spoken content in each trigram audio segment, with text processing applied for consistency:
+- Lowercased for uniformity
+- End punctuation removed
+- Spaces normalized
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+This dataset contributes to the preservation and digital representation of Chichewa, supporting:
+- Language technology development for underrepresented languages
+- Educational resources for Chichewa language learning
+- Cultural preservation through digital archives
+- N-gram based language modeling research
+
+### 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 segments
+- Trigram distribution may not be representative of natural Chichewa language patterns
+- Some segments may contain overlapping content due to the sliding window approach
+
+### Other Known Limitations
+
+- Segment-level rather than full sentence context
+- Potential audio quality variations benyeen segments
+- Regional dialect representation may be uneven
+- Variable segment lengths (primarily 3 words, but includes 2-word and 1-word segments)
+
+## Additional Information
+
+### Dataset Statistics
+
+- **Primary Content**: Trigram segments (3-word sequences)
+- **Fallback Content**: Bigram segments (2-word sequences) and single words
+- **Segment Duration**: 0.1 to 30 seconds
+- **Minimum File Size**: 1KB after processing
+
+### Licensing Information
+
+This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
+
+### Citation Information
+
+If you use this dataset in your research, please cite:
+
+```
+@dataset{chichewa_trigrams_parallel_2025,
+  title={Chichewa Trigrams Speech-Text Parallel Dataset},
+  year={2025},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/datasets/michsethowusu/chichewa-trigrams-speech-text-parallel}}
+}
+```
+
+### Acknowledgments
+
+- Audio processing and alignment performed using [MMS-300M-1130 Forced Aligner](https://huggingface.co/MahmoudAshraf/mms-300m-1130-forced-aligner)
+- Forced alignment and trigram segmentation using CTC forced alignment techniques
+- Thanks to all contributors who provided audio samples while maintaining privacy protection
+
+### Contact
+
+For questions or concerns about this dataset, please open an issue in the dataset repository.