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README.md

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 ---
-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/model-cards
-{}
+# library_name: 
+license: apache-2.0
+language:
+- en
+base_model:
+- StyleTTS2
+pipeline_tag: text-to-speech
 ---
+# Nigerian Accented Text to Speech Model
+![image/png](https://huggingface.co/)
 
-# Model Card for Model ID
+## Table of Contents
 
-<!-- Provide a quick summary of what the model is/does. -->
+1. [Model Summary](#model-summary)  
+2. [Model Description](#model-description)  
+3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)  
+   - [Recommendations](#recommendations)  
+4. [Speech Samples](#speech-samples)  
+5. [Training](#training)  
+6. [Future Improvements](#future-improvements)  
+7. [Citation](#citation)  
+8. [Credits & References](#credits--references)
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+## Model Summary
 
-## Model Details
+This text-to-speech (TTS) model (v1) was designed to synthesize Nigerian-accented English, offering high-quality, natural and relevant speech synthesis for diverse applications like narration, voice cloning, etc.
 
-### Model Description
+<video controls width="600">
+  <source src="https://huggingface.co/saheedniyi/YarnGPT/resolve/main/audio/YearnGPT.mp4" type="video/mp4">
+  Your browser does not support the video tag.
+</video>
 
-<!-- Provide a longer summary of what this model is. -->
+#### How to use in 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 ):
+- ben
+- oge
 
 
+```python
+!sudo apt-get update -y
+!apt-get install build-essential -y
+!pip install torch tensorboard transformers accelerate SoundFile torchaudio librosa phonemizer
+!pip install einops einops-exts tqdm typing typing-extensions munch pydub pyyaml nltk matplotlib
+!pip install git+https://github.com/resemble-ai/monotonic_align.git
+!pip install hf_transfer -qU
+!sudo apt-get install -y espeak-ng
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+#________________________
+import nltk
+nltk.download('punkt')
+nltk.download('punkt_tab')
 
-### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+model_folder = 'Models/'
 
-## Uses
+# I do this to always pick the last trained epoch
+files = [f for f in os.listdir(model_folder) if f.endswith('.pth')]
+sorted_files = sorted(files, key=lambda x: int(x.split('_')[-1].split('.')[0]))
+print(sorted_files[-1])
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+#________________________
+import torch
+torch.manual_seed(0)
+torch.backends.cudnn.benchmark = False
+torch.backends.cudnn.deterministic = True
+
+import random
+random.seed(0)
+
+import numpy as np
+np.random.seed(0)
+
+# load packages
+import time
+import random
+import yaml
+from munch import Munch
+import numpy as np
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torchaudio
+import librosa
+from nltk.tokenize import word_tokenize
+
+from models import *
+from utils import *
+from text_utils import TextCleaner
+textclenaer = TextCleaner()
+
+%matplotlib inline
+
+
+#________________________
+to_mel = torchaudio.transforms.MelSpectrogram(
+    n_mels=80, n_fft=2048, win_length=1200, hop_length=300)
+mean, std = -4, 4
+
+def length_to_mask(lengths):
+    mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
+    mask = torch.gt(mask+1, lengths.unsqueeze(1))
+    return mask
+
+def preprocess(wave):
+    wave_tensor = torch.from_numpy(wave).float()
+    mel_tensor = to_mel(wave_tensor)
+    mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std
+    return mel_tensor
+
+def compute_style(path):
+    wave, sr = librosa.load(path, sr=24000)
+    audio, index = librosa.effects.trim(wave, top_db=30)
+    if sr != 24000:
+        audio = librosa.resample(audio, sr, 24000)
+    mel_tensor = preprocess(audio).to(device)
+
+    with torch.no_grad():
+        ref_s = model.style_encoder(mel_tensor.unsqueeze(1))
+        ref_p = model.predictor_encoder(mel_tensor.unsqueeze(1))
 
-[More Information Needed]
+    return torch.cat([ref_s, ref_p], dim=1)
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# load phonemizer
+import phonemizer
+global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True,  with_stress=True)
 
-### Downstream Use [optional]
+config = yaml.safe_load(open(f"{model_folder}config.yml"))
+
+# load pretrained ASR model
+ASR_config = config.get('ASR_config', False)
+ASR_path = config.get('ASR_path', False)
+text_aligner = load_ASR_models(ASR_path, ASR_config)
+
+# load pretrained F0 model
+F0_path = config.get('F0_path', False)
+pitch_extractor = load_F0_models(F0_path)
+
+# load BERT model
+from Utils.PLBERT.util import load_plbert
+BERT_path = config.get('PLBERT_dir', False)
+plbert = load_plbert(BERT_path)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+model_params = recursive_munch(config['model_params'])
+model = build_model(model_params, text_aligner, pitch_extractor, plbert)
+_ = [model[key].eval() for key in model]
+_ = [model[key].to(device) for key in model]
+
+
+#________________________
+params_whole = torch.load(f"{model_folder}" + sorted_files[-1], map_location='cpu')
+params = params_whole['net']
+
+
+#________________________
+for key in model:
+    if key in params:
+        print('%s loaded' % key)
+        try:
+            model[key].load_state_dict(params[key])
+        except:
+            from collections import OrderedDict
+            state_dict = params[key]
+            new_state_dict = OrderedDict()
+            for k, v in state_dict.items():
+                name = k[7:] # remove `module.`
+                new_state_dict[name] = v
+            # load params
+            model[key].load_state_dict(new_state_dict, strict=False)
+#             except:
+#                 _load(params[key], model[key])
+_ = [model[key].eval() for key in model]
+
+
+#________________________
+from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule
+
+sampler = DiffusionSampler(
+    model.diffusion.diffusion,
+    sampler=ADPM2Sampler(),
+    sigma_schedule=KarrasSchedule(sigma_min=0.0001, sigma_max=3.0, rho=9.0), # empirical parameters
+    clamp=False
+)
 
-[More Information Needed]
 
-### Out-of-Scope Use
+#________________________
+def inference(text, ref_s, alpha = 0.3, beta = 0.7, diffusion_steps=5, embedding_scale=1):
+    text = text.strip()
+    ps = global_phonemizer.phonemize([text])
+    ps = word_tokenize(ps[0])
+    ps = ' '.join(ps)
+    tokens = textclenaer(ps)
+    tokens.insert(0, 0)
+    tokens = torch.LongTensor(tokens).to(device).unsqueeze(0)
+
+    with torch.no_grad():
+        input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device)
+        text_mask = length_to_mask(input_lengths).to(device)
+
+        t_en = model.text_encoder(tokens, input_lengths, text_mask)
+        bert_dur = model.bert(tokens, attention_mask=(~text_mask).int())
+        d_en = model.bert_encoder(bert_dur).transpose(-1, -2)
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+        s_pred = sampler(noise = torch.randn((1, 256)).unsqueeze(1).to(device),
+                                          embedding=bert_dur,
+                                          embedding_scale=embedding_scale,
+                                            features=ref_s, # reference from the same speaker as the embedding
+                                             num_steps=diffusion_steps).squeeze(1)
 
-[More Information Needed]
 
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
+        s = s_pred[:, 128:]
+        ref = s_pred[:, :128]
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+        ref = alpha * ref + (1 - alpha)  * ref_s[:, :128]
+        s = beta * s + (1 - beta)  * ref_s[:, 128:]
 
-#### Speeds, Sizes, Times [optional]
+        d = model.predictor.text_encoder(d_en,
+                                         s, input_lengths, text_mask)
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+        x, _ = model.predictor.lstm(d)
+        duration = model.predictor.duration_proj(x)
 
-[More Information Needed]
+        duration = torch.sigmoid(duration).sum(axis=-1)
+        pred_dur = torch.round(duration.squeeze()).clamp(min=1)
 
-## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+        pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data))
+        c_frame = 0
+        for i in range(pred_aln_trg.size(0)):
+            pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1
+            c_frame += int(pred_dur[i].data)
 
-### Testing Data, Factors & Metrics
+        # encode prosody
+        en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device))
+        if model_params.decoder.type == "hifigan":
+            asr_new = torch.zeros_like(en)
+            asr_new[:, :, 0] = en[:, :, 0]
+            asr_new[:, :, 1:] = en[:, :, 0:-1]
+            en = asr_new
 
-#### Testing Data
+        F0_pred, N_pred = model.predictor.F0Ntrain(en, s)
 
-<!-- This should link to a Dataset Card if possible. -->
+        asr = (t_en @ pred_aln_trg.unsqueeze(0).to(device))
+        if model_params.decoder.type == "hifigan":
+            asr_new = torch.zeros_like(asr)
+            asr_new[:, :, 0] = asr[:, :, 0]
+            asr_new[:, :, 1:] = asr[:, :, 0:-1]
+            asr = asr_new
 
-[More Information Needed]
+        out = model.decoder(asr,
+                                F0_pred, N_pred, ref.squeeze().unsqueeze(0))
 
-#### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+    return out.squeeze().cpu().numpy()[..., :-50] # weird pulse at the end of the model, need to be fixed later
 
-[More Information Needed]
 
-#### Metrics
+#________________________
+# Synthesize speech
+text = "We are happy to invite you to join us on a journey to the future."
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+#________________________
+reference_dicts = {}
+reference_dicts['oge'] = "ref_audios/things_fall_apart_1.wav" # or use your own audio samples
+reference_dicts['ben'] =  "ref_audios/feels_good_to_be_odd_1.wav"
 
-[More Information Needed]
 
-### Results
+#________________________
+start = time.time()
+noise = torch.randn(1,1,256).to(device)
+for k, path in reference_dicts.items():
+    ref_s = compute_style(path)
 
-[More Information Needed]
+    wav = inference(text, ref_s, alpha=0.3, beta=0.9, diffusion_steps=10, embedding_scale=2)
+    rtf = (time.time() - start) / (len(wav) / 24000)
+    print(f"RTF = {rtf:5f}")
+    import IPython.display as ipd
+    print(k + ' Synthesized:')
+    display(ipd.Audio(wav, rate=24000, normalize=False))
+    print('Reference:')
+    display(ipd.Audio(path, rate=24000, normalize=False))
 
-#### Summary
+```
 
+## Model Description
 
+- **Developed by:** [Saheedniyi](https://linkedin.com/in/azeez-saheed)
+- **Model type:** Text-to-Speech
+- **Language(s) (NLP):** English--> Nigerian Accented English
+- **Finetuned from:** [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M)
+- **Repository:** [YarnGPT Github Repository](https://github.com/saheedniyi02/yarngpt)
+- **Paper:** IN PROGRESS.
+- **Demo:** 1) [Prompt YarnGPT notebook](https://colab.research.google.com/drive/11zMUrfBiLa1gEflAKp8lliSOTNQ-X_nU?usp=sharing)
+            2) [Simple news reader](https://colab.research.google.com/drive/1SsXV08kly1TUJVM_NFpKqQWOZ1gUZpGe?usp=sharing)
+            
 
-## Model Examination [optional]
+#### Uses
 
-<!-- Relevant interpretability work for the model goes here -->
+Generate Nigerian-accented English speech for experimental purposes.
 
-[More Information Needed]
 
-## Environmental Impact
+#### Out-of-Scope Use
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+The model is not suitable for generating speech in languages other than English or other accents.
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+## Bias, Risks, and Limitations
 
-## Technical Specifications [optional]
+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.
 
-### Model Architecture and Objective
 
-[More Information Needed]
+#### Recommendations
 
-### Compute Infrastructure
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-[More Information Needed]
+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
+
+<div style="margin-top: 20px;">
+<table style="width: 100%; border-collapse: collapse;">
+  <thead>
+    <tr>
+        <th style="border: 1px solid #ddd; padding: 8px; text-align: left; width: 40%;">Input</th>
+        <th style="border: 1px solid #ddd; padding: 8px; text-align: left; width: 40%;">Audio</th>
+        <th style="border: 1px solid #ddd; padding: 8px; text-align: left; width: 10%;">Notes</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+        <td style="border: 1px solid #ddd; padding: 8px;">Hello world! I am Saheed Azeez and I am excited to announce the release of his project, I have been gathering data and learning how to build Audio-based models over the last two months, but thanks to God, I have been able to come up with something</td>
+        <td style="border: 1px solid #ddd; padding: 8px;">
+            <audio controls style="width: 100%;">
+                <source src="https://huggingface.co/saheedniyi/YarnGPT/resolve/main/audio/Sample_1.wav" type="audio/wav">
+                Your browser does not support the audio element.
+            </audio>
+        </td>
+        <td style="border: 1px solid #ddd; padding: 8px;">(temperature=0.1, repetition_penalty=1.1), voice: idera</td>
+    </tr>
+    <tr>
+        <td style="border: 1px solid #ddd; padding: 8px;"> Wizkid, Davido, Burna Boy perform at same event in Lagos. This event has sparked many reactions across social media, with fans and critics alike praising the artistes' performances and the rare opportunity to see the three music giants on the same stage.</td>
+        <td style="border: 1px solid #ddd; padding: 8px;">
+            <audio controls style="width: 100%;">
+                <source src="https://huggingface.co/saheedniyi/YarnGPT/resolve/main/audio/Sample_2.wav" type="audio/wav">
+                Your browser does not support the audio element.
+            </audio>
+        </td>
+        <td style="border: 1px solid #ddd; padding: 8px;">(temperature=0.1, repetition_penalty=1.1), voice: jude</td>
+    </tr>
+    <tr>
+        <td style="border: 1px solid #ddd; padding: 8px;">Since Nigeria became a republic in 1963, 14 individuals have served as head of state of Nigeria under different titles. The incumbent president Bola Tinubu is the nation's 16th head of state.</td>
+        <td style="border: 1px solid #ddd; padding: 8px;">
+            <audio controls style="width: 100%;">
+                <source src="https://huggingface.co/saheedniyi/YarnGPT/resolve/main/audio/Sample_3.wav" type="audio/wav">
+                Your browser does not support the audio element.
+            </audio>
+        </td>
+        <td style="border: 1px solid #ddd; padding: 8px;">(temperature=0.1, repetition_penalty=1.1), voice: zainab, the model struggled in pronouncing ` in 1963`</td>
+    </tr>
+    <tr>
+        <td style="border: 1px solid #ddd; padding: 8px;">I visited the President, who has shown great concern for the security of Plateau State, especially considering that just a year ago, our state was in mourning. The President’s commitment to addressing these challenges has been steadfast.</td>
+        <td style="border: 1px solid #ddd; padding: 8px;">
+            <audio controls style="width: 100%;">
+                <source src="https://huggingface.co/saheedniyi/YarnGPT/resolve/main/audio/Sample_4.wav" type="audio/wav">
+                Your browser does not support the audio element.
+            </audio>
+        </td>
+        <td style="border: 1px solid #ddd; padding: 8px;">(temperature=0.1, repetition_penalty=1.1), voice: emma</td>
+    </tr>
+    <tr>
+        <td style="border: 1px solid #ddd; padding: 8px;">Scientists have discovered a new planet that may be capable of supporting life!</td>
+        <td style="border: 1px solid #ddd; padding: 8px;">
+            <audio controls style="width: 100%;">
+                <source src="https://huggingface.co/saheedniyi/YarnGPT/resolve/main/audio/Sample_5.wav" type="audio/wav">
+                Your browser does not support the audio element.
+            </audio>
+        </td>
+        <td style="border: 1px solid #ddd; padding: 8px;">(temperature=0.1, repetition_penalty=1.1)</td>
+    </tr>
+  </tbody>
+  </table>
+</div>
+
+## 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](https://huggingface.co/novateur/WavTokenizer).
 
+#### Training Hyperparameters
+- **Number of epochs:** 5
+- **batch_size:** 2
+- **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
-
-[More Information Needed]
-
+- **GPUs:** 1 A100 (google colab: 50 hours)
 #### Software
-
-[More Information Needed]
-
+- **Training Framework:** Pytorch
+## Future Improvements?
+- Scaling up model size and human-annotaed/ reviewed training data
+- Wrap the model around an API endpoint 
+- Add support for local Nigerian languages
+- Voice cloning.
+- Potential expansion into speech-to-speech assistant models
 ## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+#### BibTeX:
+```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}
+}
+```
+#### APA:
+```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](https://huggingface.co/OuteAI/OuteTTS-0.2-500M/)