saheedniyi

1. Model Summary 2. Model Description 3. Bias, Risks, and Limitations - Recommendations 4. Speech Samples 5. Training 6. Future Improvements 7. Citation 8. Credits & References YarnGPT2 is a text-to-speech (TTS) model designed to synthesize Nigerian-accented Languages (yoruba, igbo, hausa and english) leveraging pure language modelling without external adapters or complex architectures, offering high-quality, natural, and culturally relevant speech synthesis for diverse applications. How to use (Colab) The model can generate audio on its own but its better to use a voice to prompt the model: Voices (arranged in order of perfomance and stability) - English: idera, chinenye, jude, emma,umar,,joke,zainab ,osagie, remi, tayo - Yoruba: yorubamale2, yorubafemale2, yorubafeamle1 - Igbo: igbofemale2, igbomale2,igbofemale1, - Hausa: hausafeamle1,hausafemale2, hausamale2,hausamale1 - Developed by: Saheedniyi - Model type: Text-to-Speech - Language(s) (NLP): English--> Nigerian Accented English - Finetuned from: HuggingFaceTB/SmolLM2-360M - Repository: YarnGPT Github Repository - Paper: IN PROGRESS. - Demo: 1) Prompt YarnGPT2 notebook 2) Simple news reader Generate Nigerian-accented English speech for experimental purposes. The model is not suitable for generating speech in languages other than English or other accents. The model may not capture the full diversity of Nigerian accents and could exhibit biases based on the training dataset. Also a lot of the text the model was trained on were automatically generated which could impact performance. Users (both direct and downstream) should be made aware of the risks, biases, and limitations of the model. Feedback and diverse training data contributions are encouraged. Speech Samples Uhm, so, what was the inspiration behind your latest project? Like, was there a specific moment where you were like, 'Yeah, this is it!' Or, you know, did it just kind of, uh, come together naturally over time (temperature=0.1, repetitionpenalty=1.1), language: english, voice: idera The election was won by businessman and politician, Moshood Abiola, but Babangida annulled the results, citing concerns over national security. (temperature=0.1, repetitionpenalty=1.1), language: english, voice: zainab Habeeb Okikiọla Olalomi Badmus ti ọpọ awọn ololufẹ rẹ mọ si Portable ti sọ fun ile ẹjọ majisireeti ti ipinlẹ Ogun wi pe ṣaka lara oun da, oun ko ni aisan tabi arun kankan lara. (temperature=0.1, repetitionpenalty=1.1), language: yoruba, voice: yorubamale2 Gómìnà náà fẹ̀sùn kàn pé àwọn alága àná gbìyànjú láti fi ipá gba àwọn ìjọba ìbílẹ̀ lọ́nà àìtọ́, tó sì jẹ́ pé ó yẹ kí àwọn ìjọba ìbílẹ̀ náà wà ní títì (temperature=0.1, repetitionpenalty=1.1), language: yoruba, voice: yorubafemale2 Ọ bụ oge ha si Enugwu steeti eme njem aga Anambra ka ndị omekome ahụ wakporo ụgbọala ha. (temperature=0.1, repetitionpenalty=1.1), language: igbo, voice: igbomale2 Isi ụlọorụ Shell dị na Lọndọn na gọọmenti Naịjirịa ekwuputala ugboro ugboro na ọrụ ịsacha ogbe ndị lara n'iyi n'Ogoni bụ nke malitere ihe dịka afọ asatọ gara aga na-aga nke ọma. (temperature=0.1, repetitionpenalty=1.1), language: igbo, voice: igbofemale1 Gwamnatin Najeriya ta sake maka shafin hada-hadar kuɗin kirifto na Binance a kotu, inda take buƙatar ya biya ta diyyar kuɗi dalar Amurka biliyan 81.5 (temperature=0.1, repetitionpenalty=1.1), language: hausa, voice: hausafemale1 Bisa ga dukkan alamu, haƙata cimma ruwa, dangane da koke-koken da tsofaffin ma'aikatan tarayya ke ta yi, a kan dimbin basukan wasu hakkokinsu da suke bi shekara da shekaru. (temperature=0.1, repetitionpenalty=1.1), language: hausa, voice: hausamale2 Data Trained on a dataset of publicly available Nigerian movies, podcasts ( using the subtitle-audio pairs) and open source Nigerian-related audio data on Huggingface, Audio files were preprocessed and resampled to 24Khz and tokenized using wavtokenizer. Training Hyperparameters - Number of epochs: 5 - batchsize: 4 - Scheduler: linear schedule with warmup for 4 epochs, then linear decay to zero for the last epoch - Optimizer: AdamW (betas=(0.9, 0.95),weightdecay=0.01) - Learning rate: 110^-3 Future Improvements? - Scaling up model size and human-annotaed/ reviewed training data - Wrap the model around an API endpoint - Voice cloning. - Potential expansion into speech-to-speech assistant models Credits & References - OuteAI/OuteTTS-0.2-500M - WavTokenizer - CTC Forced Alignment - Voicera

1. Model Summary 2. Model Description 3. Bias, Risks, and Limitations - Recommendations 4. Speech Samples 5. Training 6. Future Improvements 7. Citation 8. Credits & References YarnGPT is a text-to-speech (TTS) model designed to synthesize Nigerian-accented English leveraging pure language modelling without external adapters or complex architectures, offering high-quality, natural, and culturally relevant speech synthesis for diverse applications. How to use (Colab) The model can generate audio on its own but its better to use a voice to prompt the model, there are about 11 voices supported by default (6 males and 5 females ): - zainab - jude - tayo - remi - idera (default and best voice) - regina - chinenye - umar - osagie - joke - emma (the names do not correlate to any tribe or accent) Simple Nigerian Accented-NewsReader python @misc{yarngpt2025, author = {Saheed Azeez}, title = {YarnGPT: Nigerian-Accented English Text-to-Speech Model}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/SaheedAzeez/yarngpt} } python Saheed Azeez. (2025). YarnGPT: Nigerian-Accented English Text-to-Speech Model. Hugging Face. Available at: https://huggingface.co/saheedniyi/YarnGPT ``` Credits & References - OuteAI/OuteTTS-0.2-500M - WavTokenizer - CTC Forced Alignment - Voicera

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