library_name: transformers license: apache-2.0 language:

• en base_model:
• HuggingFaceTB/SmolLM2-360M pipeline_tag: text-to-speech

YarnGPT2

Table of Contents

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

Model Summary

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: yoruba_male2, yoruba_female2, yoruba_feamle1
• Igbo: igbo_female2, igbo_male2,igbo_female1,
• Hausa: hausa_feamle1,hausa_female2, hausa_male2,hausa_male1

Prompt YarnGPT2

!git clone https://github.com/saheedniyi02/yarngpt.git

pip install outetts uroman

import os
import re
import json
import torch
import inflect
import random
import uroman as ur
import numpy as np
import torchaudio
import IPython
from transformers import AutoModelForCausalLM, AutoTokenizer
from outetts.wav_tokenizer.decoder import WavTokenizer


!wget https://huggingface.co/novateur/WavTokenizer-medium-speech-75token/resolve/main/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml
!gdown 1-ASeEkrn4HY49yZWHTASgfGFNXdVnLTt


from yarngpt.audiotokenizer import AudioTokenizerV2

tokenizer_path="saheedniyi/YarnGPT2"
wav_tokenizer_config_path="/content/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml"
wav_tokenizer_model_path = "/content/wavtokenizer_large_speech_320_24k.ckpt"


audio_tokenizer=AudioTokenizerV2(
    tokenizer_path,wav_tokenizer_model_path,wav_tokenizer_config_path
    )


model = AutoModelForCausalLM.from_pretrained(tokenizer_path,torch_dtype="auto").to(audio_tokenizer.device)

#change the text
text="The election was won by businessman and politician, Moshood Abiola, but Babangida annulled the results, citing concerns over national security."

# change the language and voice
prompt=audio_tokenizer.create_prompt(text,lang="english",speaker_name="idera")

input_ids=audio_tokenizer.tokenize_prompt(prompt)

output  = model.generate(
            input_ids=input_ids,
            temperature=0.1,
            repetition_penalty=1.1,
            max_length=4000,
            #num_beams=5,# using a beam size helps for the local languages but not english
        )

codes=audio_tokenizer.get_codes(output)
audio=audio_tokenizer.get_audio(codes)
IPython.display.Audio(audio,rate=24000)
torchaudio.save(f"Sample.wav", audio, sample_rate=24000)

Model Description

• 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

Uses

Generate Nigerian-accented English speech for experimental purposes.

Out-of-Scope Use

The model is not suitable for generating speech in languages other than English or other accents.

Bias, Risks, and Limitations

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.

Recommendations

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

Listen to samples generated by YarnGPT:

Training

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,

Preprocessing

Audio files were preprocessed and resampled to 24Khz and tokenized using wavtokenizer.

Training Hyperparameters

• Number of epochs: 5
• batch_size: 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),weight_decay=0.01)
• Learning rate: 1*10^-3

Hardware

• GPUs: 1 A100 (google colab: 50 hours)

Software

• Training Framework: Pytorch

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

Citation [optional]

BibTeX:

@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}
}

APA:

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