| # YarnGPT 🎙️ |
|  |
| A text-to-speech model generating natural Nigerian-accented English speech. Built on pure language modeling without external adapters. |
|
|
| Web Url: https://yarngpt.co/ |
|
|
| ## Quick Start |
|
|
| ```python |
| |
| !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) |
| |
| ``` |
|
|
| ## Features |
|
|
| - 🗣️ 12 preset voices (6 male, 6 female) |
| - 🎯 Trained on 2000+ hours of Nigerian audio |
| - 🔊 24kHz high-quality audio output |
| - 🚀 Simple API for quick integration |
| - 📝 Support for long-form text |
|
|
| ## Available Voices |
| - Female: zainab, idera, regina, chinenye, joke, remi |
| - Male: jude, tayo, umar, osagie, onye, emma |
|
|
| ## Examples |
|
|
| Check out our [demo notebook](link-to-notebook) or listen to [sample outputs](https://huggingface.co/saheedniyi/YarnGPT/tree/main/audio). |
|
|
| ## Model Details |
|
|
| - Base: [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) |
| - Training: 5 epochs on A100 GPU |
| - Data: Nigerian movies, podcasts, and open-source audio |
| - Architecture: Pure language modeling approach |
|
|
| ## Limitations |
|
|
| - English to Nigerian-accented English only |
| - May not capture all Nigerian accent variations |
| - Training data includes auto-generated content |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{yarngpt2025, |
| author = {Saheed Azeez}, |
| title = {YarnGPT: Nigerian-Accented English Text-to-Speech Model}, |
| year = {2025}, |
| publisher = {Hugging Face} |
| } |
| ``` |
|
|
| ## License |
| MIT |
|
|
| ## Acknowledgments |
| Built with [WavTokenizer](https://github.com/jishengpeng/WavTokenizer) and inspired by [OuteTTS](https://huggingface.co/OuteAI/OuteTTS-0.2-500M/). |
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|
