Upload folder using huggingface_hub

=4.57

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+Requirement already satisfied: transformers in ./venv/lib/python3.12/site-packages (4.40.0)
+Requirement already satisfied: filelock in ./venv/lib/python3.12/site-packages (from transformers) (3.29.0)
+Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in ./venv/lib/python3.12/site-packages (from transformers) (0.36.2)
+Requirement already satisfied: numpy>=1.17 in ./venv/lib/python3.12/site-packages (from transformers) (2.4.4)
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+Requirement already satisfied: regex!=2019.12.17 in ./venv/lib/python3.12/site-packages (from transformers) (2026.4.4)
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+Requirement already satisfied: tokenizers<0.20,>=0.19 in ./venv/lib/python3.12/site-packages (from transformers) (0.19.1)
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+Requirement already satisfied: tqdm>=4.27 in ./venv/lib/python3.12/site-packages (from transformers) (4.67.3)
+Requirement already satisfied: fsspec>=2023.5.0 in ./venv/lib/python3.12/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2026.2.0)
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+Requirement already satisfied: urllib3<3,>=1.26 in ./venv/lib/python3.12/site-packages (from requests->transformers) (2.6.3)
+Requirement already satisfied: certifi>=2023.5.7 in ./venv/lib/python3.12/site-packages (from requests->transformers) (2026.4.22)

TRAIN_ON_COLAB.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nigerian Languages TTS Training (XTTS v2)\n",
+    "Train XTTS v2 for Yoruba, Hausa, Igbo, and Pidgin.\n",
+    "\n",
+    "**Run this on Google Colab with GPU runtime!**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1. Check GPU\n",
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2. Install dependencies\n",
+    "!pip install coqui-tts torch torchaudio\n",
+    "!pip install datasets huggingface_hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3. Download Nigerian datasets\n",
+    "from datasets import load_dataset\n",
+    "\n",
+    "# Nigerian Common Voice\n",
+    "yoruba = load_dataset('benjaminogbonna/nigerian_common_voice_dataset', 'yoruba', split='train')\n",
+    "hausa = load_dataset('benjaminogbonna/nigerian_common_voice_dataset', 'hausa', split='train')\n",
+    "igbo = load_dataset('benjaminogbonna/nigerian_common_voice_dataset', 'igbo', split='train')\n",
+    "\n",
+    "print(f'Yoruba: {len(yoruba)} samples')\n",
+    "print(f'Hausa: {len(hausa)} samples')\n",
+    "print(f'Igbo: {len(igbo)} samples')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 4. Download XTTS v2 base model\n",
+    "from TTS.api import TTS\n",
+    "\n",
+    "tts = TTS('tts_models/multilingual/multi-dataset/xtts_v2', gpu=True)\n",
+    "print('XTTS v2 loaded!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 5. Test voice cloning with Nigerian audio\n",
+    "# This uses a reference audio to clone the voice - no training needed!\n",
+    "\n",
+    "# Save a sample audio for reference\n",
+    "import soundfile as sf\n",
+    "sample = yoruba[0]\n",
+    "sf.write('reference_yoruba.wav', sample['audio']['array'], sample['audio']['sampling_rate'])\n",
+    "\n",
+    "# Clone voice and synthesize\n",
+    "text = 'Bawo ni, mo n pe oruko mi ni Morpheus.'\n",
+    "tts.tts_to_file(\n",
+    "    text=text,\n",
+    "    speaker_wav='reference_yoruba.wav',\n",
+    "    language='en',  # XTTS uses 'en' for African languages\n",
+    "    file_path='yoruba_output.wav'\n",
+    ")\n",
+    "print('Generated yoruba_output.wav')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 6. Run XTTS fine-tuning demo\n",
+    "# This launches a Gradio interface for fine-tuning\n",
+    "!python -m TTS.demos.xtts_ft_demo --share"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

TRAIN_YARNGPT_COLAB.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# YarnGPT Nigerian Languages Fine-tuning\n",
+        "\n",
+        "This notebook fine-tunes YarnGPT on Nigerian language audio data (Yoruba, Hausa, Igbo, Pidgin).\n",
+        "\n",
+        "**Requirements:**\n",
+        "- Google Colab with GPU (A100 recommended, T4 works too)\n",
+        "- Training data in Google Drive or uploaded directly"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Check GPU\n",
+        "!nvidia-smi\n",
+        "import torch\n",
+        "print(f\"CUDA available: {torch.cuda.is_available()}\")\n",
+        "print(f\"Device: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU'}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Install dependencies\n",
+        "!pip install -q transformers datasets accelerate torchaudio\n",
+        "!pip install -q outetts uroman inflect\n",
+        "!pip install -q huggingface_hub\n",
+        "\n",
+        "# Download WavTokenizer models\n",
+        "!mkdir -p ~/.yarngpt/models\n",
+        "!wget -q -O ~/.yarngpt/models/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml \\\n",
+        "    https://huggingface.co/novateur/WavTokenizer-medium-speech-75token/resolve/main/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\n",
+        "!gdown 1-ASeEkrn4HY49yZWHTASgfGFNXdVnLTt -O ~/.yarngpt/models/wavtokenizer_large_speech_320_24k.ckpt"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Mount Google Drive (optional - for data storage)\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "\n",
+        "# Or upload data directly\n",
+        "# from google.colab import files\n",
+        "# uploaded = files.upload()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Download Nigerian Common Voice dataset\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "# Load datasets\n",
+        "yoruba_ds = load_dataset(\"mozilla-foundation/common_voice_17_0\", \"yo\", split=\"train\", trust_remote_code=True)\n",
+        "hausa_ds = load_dataset(\"mozilla-foundation/common_voice_17_0\", \"ha\", split=\"train\", trust_remote_code=True)\n",
+        "igbo_ds = load_dataset(\"mozilla-foundation/common_voice_17_0\", \"ig\", split=\"train\", trust_remote_code=True)\n",
+        "\n",
+        "print(f\"Yoruba samples: {len(yoruba_ds)}\")\n",
+        "print(f\"Hausa samples: {len(hausa_ds)}\")\n",
+        "print(f\"Igbo samples: {len(igbo_ds)}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "import re\n",
+        "import json\n",
+        "import torch\n",
+        "import torchaudio\n",
+        "import numpy as np\n",
+        "from pathlib import Path\n",
+        "from torch.utils.data import Dataset, DataLoader\n",
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "from outetts.wav_tokenizer.decoder import WavTokenizer\n",
+        "from outetts.wav_tokenizer.encoder.utils import convert_audio\n",
+        "import uroman as ur\n",
+        "import inflect\n",
+        "\n",
+        "# Configuration\n",
+        "MODEL_ID = \"saheedniyi/YarnGPT2\"\n",
+        "OUTPUT_DIR = \"/content/yarngpt-nigerian-finetuned\"\n",
+        "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+        "\n",
+        "# WavTokenizer paths\n",
+        "MODEL_DIR = os.path.expanduser(\"~/.yarngpt/models\")\n",
+        "WAV_CONFIG = os.path.join(MODEL_DIR, \"wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml\")\n",
+        "WAV_MODEL = os.path.join(MODEL_DIR, \"wavtokenizer_large_speech_320_24k.ckpt\")\n",
+        "\n",
+        "print(f\"Using device: {DEVICE}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Load WavTokenizer\n",
+        "print(\"Loading WavTokenizer...\")\n",
+        "wav_tokenizer = WavTokenizer.from_pretrained0802(WAV_CONFIG, WAV_MODEL)\n",
+        "wav_tokenizer = wav_tokenizer.to(DEVICE)\n",
+        "print(\"WavTokenizer loaded!\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "class YarnGPTDataset(Dataset):\n",
+        "    \"\"\"Dataset for YarnGPT training with HuggingFace datasets.\"\"\"\n",
+        "    \n",
+        "    def __init__(self, hf_dataset, tokenizer, wav_tokenizer, language, max_length=4096, max_samples=1000):\n",
+        "        self.dataset = hf_dataset.select(range(min(len(hf_dataset), max_samples)))\n",
+        "        self.tokenizer = tokenizer\n",
+        "        self.wav_tokenizer = wav_tokenizer\n",
+        "        self.language = language\n",
+        "        self.max_length = max_length\n",
+        "        self.device = DEVICE\n",
+        "        \n",
+        "        self.uroman = ur.Uroman()\n",
+        "        self.lec = inflect.engine()\n",
+        "        \n",
+        "        self.special_tokens = {\n",
+        "            \"audio_code\": \"<|{}|>\",\n",
+        "            \"text_start\": \"<|text_start|>\",\n",
+        "            \"text_end\": \"<|text_end|>\",\n",
+        "            \"audio_start\": \"<|audio_start|>\",\n",
+        "            \"audio_end\": \"<|audio_end|>\",\n",
+        "            \"code_start\": \"<|code_start|>\",\n",
+        "            \"code_end\": \"<|code_end|>\",\n",
+        "            \"text_sep\": \"<|text_sep|>\",\n",
+        "            \"hausa\": \"<|hausa|>\",\n",
+        "            \"igbo\": \"<|igbo|>\",\n",
+        "            \"yoruba\": \"<|yoruba|>\",\n",
+        "            \"english\": \"<|english|>\",\n",
+        "        }\n",
+        "        self.bos = \"<|im_start|>\"\n",
+        "        self.eos = \"<|im_end|>\"\n",
+        "    \n",
+        "    def _process_text(self, text):\n",
+        "        text = self.uroman.romanize_string(text)\n",
+        "        text = re.sub(r'\\d+(\\.\\d+)?', lambda x: self.lec.number_to_words(x.group()), text.lower())\n",
+        "        text = re.sub(r'[-_/,\\.\\\\]', ' ', text)\n",
+        "        text = re.sub(r'[^a-z\\s]', '', text)\n",
+        "        text = re.sub(r'\\s+', ' ', text).strip()\n",
+        "        return text.split()\n",
+        "    \n",
+        "    def _encode_audio(self, audio_array, sample_rate):\n",
+        "        audio = torch.tensor(audio_array, dtype=torch.float32)\n",
+        "        if sample_rate != 24000:\n",
+        "            audio = audio.unsqueeze(0)\n",
+        "            audio = convert_audio(audio, sample_rate, 24000, 1)\n",
+        "            audio = audio.squeeze()\n",
+        "        \n",
+        "        audio = audio.unsqueeze(0).to(self.device)\n",
+        "        if audio.ndim == 3:\n",
+        "            audio = audio.squeeze(1)\n",
+        "        \n",
+        "        bandwidth_id = torch.tensor([0]).to(self.device)\n",
+        "        with torch.no_grad():\n",
+        "            _, codes = self.wav_tokenizer.encode_infer(audio, bandwidth_id=bandwidth_id)\n",
+        "        return codes.squeeze().tolist()\n",
+        "    \n",
+        "    def __len__(self):\n",
+        "        return len(self.dataset)\n",
+        "    \n",
+        "    def __getitem__(self, idx):\n",
+        "        sample = self.dataset[idx]\n",
+        "        \n",
+        "        try:\n",
+        "            # Get audio and text\n",
+        "            audio_array = sample['audio']['array']\n",
+        "            sample_rate = sample['audio']['sampling_rate']\n",
+        "            text = sample['sentence']\n",
+        "            \n",
+        "            # Skip very short or very long samples\n",
+        "            duration = len(audio_array) / sample_rate\n",
+        "            if duration < 1.0 or duration > 15.0:\n",
+        "                return None\n",
+        "            \n",
+        "            # Encode audio\n",
+        "            audio_codes = self._encode_audio(audio_array, sample_rate)\n",
+        "            \n",
+        "            # Create prompt\n",
+        "            words = self._process_text(text)\n",
+        "            words_str = self.special_tokens['text_sep'].join(words)\n",
+        "            \n",
+        "            prompt = f\"{self.bos}\\n{self.special_tokens['text_start']}{words_str}{self.special_tokens['text_end']}\\n\"\n",
+        "            prompt += f\"{self.special_tokens[self.language]}\\n\"\n",
+        "            prompt += f\"{self.special_tokens['audio_start']}\\n\"\n",
+        "            codes_str = \"\".join([self.special_tokens['audio_code'].format(c) for c in audio_codes])\n",
+        "            prompt += f\"{self.special_tokens['code_start']}{codes_str}{self.special_tokens['code_end']}\\n\"\n",
+        "            prompt += f\"{self.special_tokens['audio_end']}\\n{self.eos}\"\n",
+        "            \n",
+        "            # Tokenize\n",
+        "            tokens = self.tokenizer.encode(\n",
+        "                prompt,\n",
+        "                add_special_tokens=False,\n",
+        "                max_length=self.max_length,\n",
+        "                truncation=True\n",
+        "            )\n",
+        "            \n",
+        "            return {\n",
+        "                \"input_ids\": torch.tensor(tokens),\n",
+        "                \"attention_mask\": torch.ones(len(tokens)),\n",
+        "                \"labels\": torch.tensor(tokens)\n",
+        "            }\n",
+        "        except Exception as e:\n",
+        "            print(f\"Error at index {idx}: {e}\")\n",
+        "            return None"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Load tokenizer and model\n",
+        "print(\"Loading tokenizer...\")\n",
+        "tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)\n",
+        "\n",
+        "print(\"Loading model...\")\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    MODEL_ID,\n",
+        "    torch_dtype=torch.bfloat16\n",
+        ").to(DEVICE)\n",
+        "print(\"Model loaded!\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Create datasets (use smaller subset for testing)\n",
+        "MAX_SAMPLES = 500  # Increase for full training\n",
+        "\n",
+        "print(\"Creating Yoruba dataset...\")\n",
+        "yoruba_dataset = YarnGPTDataset(yoruba_ds, tokenizer, wav_tokenizer, \"yoruba\", max_samples=MAX_SAMPLES)\n",
+        "\n",
+        "print(\"Creating Hausa dataset...\")\n",
+        "hausa_dataset = YarnGPTDataset(hausa_ds, tokenizer, wav_tokenizer, \"hausa\", max_samples=MAX_SAMPLES)\n",
+        "\n",
+        "print(\"Creating Igbo dataset...\")\n",
+        "igbo_dataset = YarnGPTDataset(igbo_ds, tokenizer, wav_tokenizer, \"igbo\", max_samples=MAX_SAMPLES)\n",
+        "\n",
+        "print(f\"Dataset sizes: Yoruba={len(yoruba_dataset)}, Hausa={len(hausa_dataset)}, Igbo={len(igbo_dataset)}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from torch.utils.data import ConcatDataset\n",
+        "from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling\n",
+        "\n",
+        "# Combine datasets\n",
+        "combined_dataset = ConcatDataset([yoruba_dataset, hausa_dataset, igbo_dataset])\n",
+        "\n",
+        "# Filter out None samples\n",
+        "def collate_fn(batch):\n",
+        "    batch = [b for b in batch if b is not None]\n",
+        "    if not batch:\n",
+        "        return None\n",
+        "    \n",
+        "    max_len = max(len(b['input_ids']) for b in batch)\n",
+        "    \n",
+        "    input_ids = torch.zeros(len(batch), max_len, dtype=torch.long)\n",
+        "    attention_mask = torch.zeros(len(batch), max_len, dtype=torch.long)\n",
+        "    labels = torch.full((len(batch), max_len), -100, dtype=torch.long)\n",
+        "    \n",
+        "    for i, b in enumerate(batch):\n",
+        "        seq_len = len(b['input_ids'])\n",
+        "        input_ids[i, :seq_len] = b['input_ids']\n",
+        "        attention_mask[i, :seq_len] = b['attention_mask']\n",
+        "        labels[i, :seq_len] = b['labels']\n",
+        "    \n",
+        "    return {\n",
+        "        'input_ids': input_ids,\n",
+        "        'attention_mask': attention_mask,\n",
+        "        'labels': labels\n",
+        "    }\n",
+        "\n",
+        "# Training arguments\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=OUTPUT_DIR,\n",
+        "    num_train_epochs=3,  # Reduce for testing\n",
+        "    per_device_train_batch_size=2,\n",
+        "    gradient_accumulation_steps=8,\n",
+        "    learning_rate=1e-4,\n",
+        "    weight_decay=0.01,\n",
+        "    warmup_ratio=0.1,\n",
+        "    bf16=True,\n",
+        "    logging_steps=50,\n",
+        "    save_steps=200,\n",
+        "    save_total_limit=2,\n",
+        "    report_to=\"none\",\n",
+        ")\n",
+        "\n",
+        "# Trainer\n",
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    train_dataset=combined_dataset,\n",
+        "    data_collator=collate_fn,\n",
+        ")\n",
+        "\n",
+        "print(f\"Total training samples: {len(combined_dataset)}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Train!\n",
+        "print(\"Starting training...\")\n",
+        "trainer.train()\n",
+        "print(\"Training complete!\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Save the fine-tuned model\n",
+        "trainer.save_model(OUTPUT_DIR)\n",
+        "tokenizer.save_pretrained(OUTPUT_DIR)\n",
+        "print(f\"Model saved to {OUTPUT_DIR}\")\n",
+        "\n",
+        "# Copy to Google Drive for persistence\n",
+        "!cp -r {OUTPUT_DIR} /content/drive/MyDrive/yarngpt-nigerian-finetuned"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Test the fine-tuned model\n",
+        "from yarngpt.audiotokenizer import AudioTokenizerV2\n",
+        "\n",
+        "# Load fine-tuned model\n",
+        "finetuned_model = AutoModelForCausalLM.from_pretrained(\n",
+        "    OUTPUT_DIR,\n",
+        "    torch_dtype=torch.bfloat16\n",
+        ").to(DEVICE)\n",
+        "\n",
+        "audio_tokenizer = AudioTokenizerV2(\n",
+        "    OUTPUT_DIR,\n",
+        "    WAV_MODEL,\n",
+        "    WAV_CONFIG\n",
+        ")\n",
+        "\n",
+        "# Generate speech\n",
+        "text = \"Bawo ni, se daadaa ni?\"\n",
+        "prompt = audio_tokenizer.create_prompt(text, lang=\"yoruba\", speaker_name=\"yoruba_female1\")\n",
+        "input_ids = audio_tokenizer.tokenize_prompt(prompt)\n",
+        "\n",
+        "output = finetuned_model.generate(\n",
+        "    input_ids=input_ids,\n",
+        "    temperature=0.1,\n",
+        "    repetition_penalty=1.1,\n",
+        "    max_length=4000,\n",
+        ")\n",
+        "\n",
+        "codes = audio_tokenizer.get_codes(output)\n",
+        "audio = audio_tokenizer.get_audio(codes)\n",
+        "\n",
+        "import IPython\n",
+        "IPython.display.Audio(audio, rate=24000)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Push to Hugging Face Hub (optional)\n",
+        "from huggingface_hub import login, HfApi\n",
+        "\n",
+        "# Login with your HF token\n",
+        "# login(token=\"your_hf_token\")\n",
+        "\n",
+        "# Push model\n",
+        "# finetuned_model.push_to_hub(\"your-username/yarngpt-nigerian\")\n",
+        "# tokenizer.push_to_hub(\"your-username/yarngpt-nigerian\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "A100",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

YARNGPT_TRAINING_GUIDE.md

@@ -0,0 +1,190 @@
+# YarnGPT Training Guide for Nigerian Languages
+
+## Overview
+
+This guide covers how to:
+1. **Create custom speaker voices** (voice cloning without fine-tuning)
+2. **Fine-tune YarnGPT** with new Nigerian language data
+
+## Current Data Status
+
+```
+~/voice-training/datasets/
+├── nigerian_cv/          # 658MB - Common Voice (Yoruba, Hausa, Igbo, English)
+├── nigerian_pidgin/      # 912MB - Nigerian Pidgin dataset
+└── openslr_yoruba/       # 1.4GB - High-quality Yoruba speech (OpenSLR86)
+
+~/voice-training/prepared_data/
+├── yoruba/  # 19MB - 500 samples extracted
+├── hausa/   # 14MB - 500 samples extracted
+└── igbo/    # 17MB - 500 samples extracted
+```
+
+## Option 1: Create Custom Speaker Voices (No Training Required)
+
+This is the **fastest approach** - create new speaker JSON files that YarnGPT can use.
+
+### How It Works
+YarnGPT uses "speaker JSON" files that contain:
+- Reference text transcript
+- Audio codes from WavTokenizer for each word
+
+### Steps:
+
+1. **Select a clean audio sample** (5-15 seconds, clear speech)
+
+2. **Run the speaker creation script:**
+```bash
+cd ~/voice-training
+source venv/bin/activate
+python scripts/create_speaker_json.py \
+    --audio /path/to/audio.wav \
+    --text "The transcript of the audio" \
+    --output ~/yarngpt-clone/upstream/yarngpt/default_speakers_local/my_voice.json \
+    --language yoruba
+```
+
+3. **Deploy to Railway:**
+   - Add the JSON to `upstream/yarngpt/default_speakers_local/`
+   - Push to git and redeploy
+
+### Best Practices for Speaker Audio:
+- Duration: 5-15 seconds
+- Format: WAV, 24kHz or higher sample rate
+- Clean recording, minimal background noise
+- Clear pronunciation
+- Natural speaking pace
+
+---
+
+## Option 2: Fine-tune YarnGPT Model
+
+For better quality with large amounts of data, fine-tune the model.
+
+### Requirements:
+- **GPU**: NVIDIA GPU with 16GB+ VRAM (A100 recommended)
+- **Training Time**: ~50 hours on A100 for full training
+- **Data**: Audio files + transcripts
+
+### Training Data Format:
+```
+data_dir/
+├── wavs/
+│   ├── audio001.wav
+│   ├── audio002.wav
+│   └── ...
+└── metadata.csv
+```
+
+`metadata.csv` format:
+```
+audio001|This is the transcript of audio 001
+audio002|Another transcript here
+```
+
+### Fine-tuning Script:
+
+```bash
+cd ~/voice-training
+source venv/bin/activate
+python scripts/finetune_yarngpt.py \
+    --data-dirs ./prepared_data/yoruba ./prepared_data/hausa ./prepared_data/igbo \
+    --languages yoruba hausa igbo \
+    --output-dir ./yarngpt-nigerian \
+    --epochs 5 \
+    --batch-size 4
+```
+
+### Google Colab Training (Recommended)
+
+Since local GPU is limited, use Google Colab:
+
+1. Upload the Colab notebook: `~/voice-training/TRAIN_YARNGPT_COLAB.ipynb`
+2. Enable GPU runtime (Runtime > Change runtime type > GPU)
+3. Upload your training data to Google Drive
+4. Run the notebook cells
+
+---
+
+## Option 3: Improve Existing Deployment
+
+### Fix Railway 502 Error
+
+The Railway deployment is returning 502 errors. Check:
+
+1. **Memory limits** - YarnGPT needs ~2GB RAM
+2. **HF_TOKEN** - Must be set for model downloads
+3. **Logs**: `railway logs`
+
+```bash
+# Check Railway deployment
+cd ~/yarngpt-clone
+railway status
+railway logs
+```
+
+### Redeploy with fixes:
+```bash
+cd ~/yarngpt-clone
+git add .
+git commit -m "fix: increase memory allocation"
+git push origin main
+# Railway auto-deploys on push
+```
+
+---
+
+## Architecture Overview
+
+```
+User Input (Text)
+       ↓
+AudioTokenizerV2.create_prompt()
+       ↓
+Reference speaker JSON (audio codes as prefix)
+       ↓
+SmolLM2-360M (YarnGPT model)
+       ↓
+Generated audio codes
+       ↓
+WavTokenizer decoder
+       ↓
+Audio waveform (24kHz)
+```
+
+## Key Files in yarngpt-clone
+
+```
+yarngpt-clone/
+├── src/morpheous/
+│   ├── main.py         # FastAPI endpoints
+│   ├── tts.py          # TTS synthesis logic
+│   └── voice_data/     # Bundled speaker JSONs
+├── upstream/yarngpt/
+│   ├── default_speakers/       # English voices
+│   └── default_speakers_local/ # Nigerian language voices
+└── requirements.txt
+```
+
+## Next Steps
+
+1. **Quick Win**: Create 2-3 custom speaker JSONs from your best audio samples
+2. **Medium**: Fix Railway deployment and add new speaker voices
+3. **Long-term**: Fine-tune model with full training data on Colab/Cloud GPU
+
+## Commands Cheat Sheet
+
+```bash
+# Activate environment
+cd ~/voice-training && source venv/bin/activate
+
+# Create speaker JSON
+python scripts/create_speaker_json.py --audio sample.wav --text "transcript" --output voice.json
+
+# Fine-tune (requires GPU)
+python scripts/finetune_yarngpt.py --data-dirs ./prepared_data/yoruba --languages yoruba
+
+# Check data status
+du -sh ~/voice-training/datasets/*/
+ls ~/voice-training/prepared_data/*/wavs/ | wc -l
+```

create_nigerian_speakers.py

@@ -0,0 +1,190 @@
+#!/usr/bin/env python3
+"""
+Create YarnGPT speaker JSON files from Nigerian voice data.
+Uses WavTokenizer to encode audio to codes.
+"""
+
+import os
+import sys
+import json
+import re
+from pathlib import Path
+
+def main():
+    print("=== Creating Nigerian Speaker JSONs ===\n")
+    
+    import torch
+    import torchaudio
+    from outetts.wav_tokenizer.decoder import WavTokenizer
+    from outetts.wav_tokenizer.encoder.utils import convert_audio
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"Using device: {device}")
+    
+    # WavTokenizer paths
+    MODEL_DIR = os.path.expanduser("~/.yarngpt/models")
+    config_path = os.path.join(MODEL_DIR, "wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml")
+    model_path = os.path.join(MODEL_DIR, "wavtokenizer_large_speech_320_24k.ckpt")
+    
+    # Download WavTokenizer if not exists
+    if not os.path.exists(model_path):
+        print("Downloading WavTokenizer models...")
+        os.makedirs(MODEL_DIR, exist_ok=True)
+        import requests
+        from tqdm import tqdm
+        
+        # Config
+        config_url = "https://huggingface.co/novateur/WavTokenizer-medium-speech-75token/resolve/main/wavtokenizer_mediumdata_frame75_3s_nq1_code4096_dim512_kmeans200_attn.yaml"
+        r = requests.get(config_url)
+        with open(config_path, 'wb') as f:
+            f.write(r.content)
+        
+        # Model (larger, use streaming)
+        model_url = "https://huggingface.co/novateur/WavTokenizer-large-speech-75token/resolve/main/wavtokenizer_large_speech_320_24k.ckpt"
+        r = requests.get(model_url, stream=True)
+        total = int(r.headers.get('content-length', 0))
+        with open(model_path, 'wb') as f, tqdm(total=total, unit='B', unit_scale=True) as pbar:
+            for chunk in r.iter_content(chunk_size=8192):
+                f.write(chunk)
+                pbar.update(len(chunk))
+    
+    print("Loading WavTokenizer...")
+    wav_tokenizer = WavTokenizer.from_pretrained0802(config_path, model_path)
+    wav_tokenizer = wav_tokenizer.to(device)
+    print("WavTokenizer loaded!\n")
+    
+    # Output directory
+    output_dir = Path(os.path.expanduser("~/yarngpt-clone/src/morpheous/voice_data/default_speakers_local"))
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    # Process each language
+    languages = ["yoruba", "hausa", "igbo"]
+    
+    for lang in languages:
+        manifest_path = os.path.expanduser(f"~/voice-training/prepared_data/{lang}/manifest.json")
+        if not os.path.exists(manifest_path):
+            print(f"No manifest for {lang}, skipping...")
+            continue
+        
+        with open(manifest_path, 'r') as f:
+            manifest = json.load(f)
+        
+        print(f"\n=== Processing {lang.upper()} ===")
+        
+        # Group by speaker
+        speakers = {}
+        for entry in manifest:
+            speaker = entry.get("speaker", "unknown")
+            if speaker not in speakers:
+                speakers[speaker] = []
+            speakers[speaker].append(entry)
+        
+        # Create speaker JSONs (up to 2 per language)
+        speaker_count = 0
+        for speaker_id, entries in speakers.items():
+            if speaker_count >= 2:
+                break
+            
+            # Find best sample (longest within 5-10s range)
+            best_entry = None
+            best_duration = 0
+            
+            for entry in entries[:20]:  # Check first 20 samples
+                audio_path = entry["audio_file"]
+                if not os.path.exists(audio_path):
+                    continue
+                try:
+                    info = torchaudio.info(audio_path)
+                    duration = info.num_frames / info.sample_rate
+                    if 5 <= duration <= 10 and duration > best_duration:
+                        best_entry = entry
+                        best_duration = duration
+                except:
+                    continue
+            
+            if not best_entry:
+                continue
+            
+            audio_path = best_entry["audio_file"]
+            text = best_entry["text"]
+            
+            print(f"Processing {lang}_{speaker_id}: {os.path.basename(audio_path)} ({best_duration:.1f}s)")
+            print(f"  Text: {text[:60]}...")
+            
+            try:
+                # Load audio
+                audio_data, sample_rate = torchaudio.load(audio_path)
+                audio_data = audio_data.squeeze().to(dtype=torch.float32)
+                
+                # Resample to 24kHz
+                if sample_rate != 24000:
+                    audio_data = audio_data.unsqueeze(0)
+                    audio_data = convert_audio(audio_data, sample_rate, 24000, 1)
+                    audio_data = audio_data.squeeze()
+                
+                audio = audio_data.unsqueeze(0).to(device)
+                if audio.ndim == 3:
+                    audio = audio.squeeze(1)
+                
+                # Encode to codes
+                bandwidth_id = torch.tensor([0]).to(device)
+                with torch.no_grad():
+                    _, codes = wav_tokenizer.encode_infer(audio, bandwidth_id=bandwidth_id)
+                codes = codes.squeeze().tolist()
+                
+                # Calculate duration
+                total_duration = len(audio_data) / 24000
+                
+                # Split text into words
+                words = text.strip().split()
+                num_words = len(words)
+                
+                # Distribute codes across words
+                codes_per_word = len(codes) // num_words
+                duration_per_word = total_duration / num_words
+                
+                word_data = []
+                for i, word in enumerate(words):
+                    start_idx = i * codes_per_word
+                    end_idx = start_idx + codes_per_word if i < num_words - 1 else len(codes)
+                    word_codes = codes[start_idx:end_idx]
+                    
+                    # Normalize word
+                    normalized_word = re.sub(r'[^a-zA-ZẹọṣẸỌṢàáèéìíòóùúÀÁÈÉÌÍÒÓÙÚ]', '', word.lower())
+                    if not normalized_word:
+                        normalized_word = word.lower()
+                    
+                    word_data.append({
+                        "word": normalized_word,
+                        "duration": f"{duration_per_word:.2f}",
+                        "codes": word_codes
+                    })
+                
+                # Create speaker JSON
+                speaker_json = {
+                    "text": text,
+                    "words": word_data
+                }
+                
+                # Determine gender/number
+                gender = "female" if speaker_count == 0 else "male"
+                num = speaker_count + 3  # Start from 3 to add to existing
+                output_name = f"{lang}_{gender}{num}.json"
+                output_path = output_dir / output_name
+                
+                with open(output_path, 'w', encoding='utf-8') as f:
+                    json.dump(speaker_json, f, ensure_ascii=False, indent=4)
+                
+                print(f"  Created: {output_name}")
+                speaker_count += 1
+                
+            except Exception as e:
+                print(f"  Error: {e}")
+                continue
+    
+    print("\n=== Done! ===")
+    print(f"Speaker JSONs saved to: {output_dir}")
+
+
+if __name__ == "__main__":
+    main()

datasets/nigerian_cv/.gitattributes

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+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
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+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

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