michsethowusu/twi-speech-text-parallel-synthetic-1m-part002

Twi Speech-Text Parallel Dataset - Part 2 of 5

🎉 The Largest Speech Dataset for Twi Language

This dataset contains part 2 of the largest speech dataset for the Twi language, featuring 1 million speech-to-text pairs split across 5 parts (approximately 200,000 samples each). This represents a groundbreaking resource for Twi (Akan), a language spoken primarily in Ghana.

🚀 Breaking the Low-Resource Language Barrier

This publication demonstrates that African languages don't have to remain low-resource. Through creative synthetic data generation techniques, we've produced the largest collection of AI training data for speech-to-text models in Twi, proving that innovative approaches can build the datasets African languages need.

📊 Complete Dataset Series (1M Total Samples)

Part	Repository	Samples	Status
Part 1	michsethowusu/twi-speech-text-parallel-synthetic-1m-part001	~200,000	✅ Available
Part 2	michsethowusu/twi-speech-text-parallel-synthetic-1m-part002	~200,000	🔥 THIS PART
Part 3	michsethowusu/twi-speech-text-parallel-synthetic-1m-part003	~200,000	✅ Available
Part 4	michsethowusu/twi-speech-text-parallel-synthetic-1m-part004	~200,000	✅ Available
Part 5	michsethowusu/twi-speech-text-parallel-synthetic-1m-part005	~200,000	✅ Available

Dataset Summary

• Language: Twi/Akan - aka
• Total Dataset Size: 1,000,000 speech-text pairs
• This Part: {len(data):,} audio files (filtered, >1KB)
• Task: Speech Recognition, Text-to-Speech
• Format: WAV audio files with corresponding text transcriptions
• Generation Method: Synthetic data generation
• 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
• Speech-to-Speech Translation: Cross-lingual speech applications
• Keyword Spotting: Identify specific Twi words in audio
• Phonetic Analysis: Study Twi pronunciation patterns
• Language Model Training: Large-scale Twi language understanding

📁 Dataset Structure

Data Fields

• audio: Audio file in WAV format (synthetically generated)
• text: Corresponding text transcription in Twi

Data Splits

This part contains a single training split with {len(data):,} filtered audio-text pairs (small/corrupted files removed).

Loading the Complete Dataset

from datasets import load_dataset, concatenate_datasets

# Load all parts of the dataset
parts = []
for i in range(1, 6):
    part_name = f"michsethowusu/twi-speech-text-parallel-synthetic-1m-part{i:03d}"
    part = load_dataset(part_name, split="train")
    parts.append(part)

# Combine all parts into one dataset
complete_dataset = concatenate_datasets(parts)
print(f"Complete dataset size: {{len(complete_dataset):,}} samples")

Loading Just This Part

from datasets import load_dataset

# Load only this part
dataset = load_dataset("michsethowusu/twi-speech-text-parallel-synthetic-1m-part002", split="train")
print(f"Part 2 dataset size: {{len(dataset):,}} samples")

🛠️ Dataset Creation

Methodology

This dataset was created using synthetic data generation techniques, specifically designed to overcome the challenge of limited speech resources for African languages. The approach demonstrates how AI can be used to bootstrap language resources for underrepresented languages.

Data Processing Pipeline

1. Text Generation: Synthetic Twi sentences generated
2. Speech Synthesis: Text-to-speech conversion using advanced models
3. Quality Filtering: Files smaller than 1KB removed to ensure quality
4. Alignment Verification: Audio-text alignment validated
5. Format Standardization: Consistent WAV format and text encoding

Technical Details

• Audio Format: WAV files, various sample rates
• Text Encoding: UTF-8
• Language Code: aka (ISO 639-3)
• Filtering: Minimum file size 1KB to remove corrupted/empty files

🌍 Impact and Applications

Breaking Language Barriers

This dataset represents a paradigm shift in how we approach low-resource African languages:

• Scalability: Proves synthetic generation can create large datasets
• Accessibility: Makes Twi ASR/TTS development feasible
• Innovation: Demonstrates creative solutions for language preservation
• Reproducibility: Methodology can be applied to other African languages

Use Cases

• Educational Technology: Twi language learning applications
• Accessibility: Voice interfaces for Twi speakers
• Cultural Preservation: Digital archiving of Twi speech patterns
• Research: Phonetic and linguistic studies of Twi
• Commercial Applications: Voice assistants for Ghanaian markets

⚠️ Considerations for Using the Data

Social Impact

Positive Impact:

• Advances language technology for underrepresented communities
• Supports digital inclusion for Twi speakers
• Contributes to cultural and linguistic preservation
• Enables development of Twi-language AI applications

Limitations and Biases

• Synthetic Nature: Generated data may not capture all nuances of natural speech
• Dialect Coverage: May not represent all regional Twi dialects equally
• Speaker Diversity: Limited to synthesis model characteristics
• Domain Coverage: Vocabulary limited to training data scope
• Audio Quality: Varies across synthetic generation process

Ethical Considerations

• Data created with respect for Twi language and culture
• Intended to support, not replace, natural language preservation efforts
• Users should complement with natural speech data when possible

📚 Technical Specifications

Audio Specifications

• Format: WAV
• Channels: Mono
• Sample Rate: 16kHz
• Bit Depth: 16-bit
• Duration: Variable per sample

Quality Assurance

• Minimum file size: 1KB (corrupted files filtered)
• Text-audio alignment verified
• UTF-8 encoding validation
• Duplicate removal across parts

📄 License and Usage

Licensing Information

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

You are free to:

• Share: Copy and redistribute the material
• Adapt: Remix, transform, and build upon the material
• Commercial use: Use for commercial purposes

Under the following terms:

• Attribution: Give appropriate credit and indicate if changes were made

🙏 Acknowledgments

• Original Audio Production: The Ghana Institute of Linguistics, Literacy and Bible Translation in partnership with Davar Partners
• Audio Processing: MMS-300M-1130 Forced Aligner
• Synthetic Generation: Advanced text-to-speech synthesis pipeline
• Community: Twi language speakers and researchers who inspire this work

📖 Citation

If you use this dataset in your research, please cite:

@dataset{{twi_speech_parallel_1m_2025,
  title={{Twi Speech-Text Parallel Dataset: The Largest Speech Dataset for Twi Language}},
  author={{Owusu, Mich-Seth}},
  year={{2025}},
  publisher={{Hugging Face}},
  note={{1 Million synthetic speech-text pairs across 5 parts}},
  url={{https://huggingface.co/datasets/michsethowusu/twi-speech-text-parallel-synthetic-1m-part002}}
}}

For the complete dataset series:

@dataset{{twi_speech_complete_series_2025,
  title={{Complete Twi Speech-Text Parallel Dataset Series (1M samples)}},
  author={{Owusu, Michael Seth}},
  year={{2025}},
  publisher={{Hugging Face}},
  note={{Parts 001-005, 200k samples each}},
  url={{https://huggingface.co/michsethowusu}}
}}

📞 Contact and Support

• Repository Issues: Open an issue in this dataset repository
• General Questions: Contact through Hugging Face profile
• Collaboration: Open to partnerships for African language AI development

🔗 Related Resources

• Complete Dataset Series
• Twi Language Resources

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