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README.md

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+# Plapre Simple - Phoneme-based TTS Model
+
+A simplified phoneme-based text-to-speech model built on Qwen3-0.6B-Base, trained to generate audio tokens from phoneme sequences.
+
+## Model Overview
+
+This model is trained to perform phoneme-to-audio-token generation using a causal language modeling approach. It takes phoneme sequences as input and generates audio tokens that can be decoded by a neural audio codec (e.g., NeuCodec).
+
+## Tokenization
+
+The model uses a custom phoneme-based tokenizer with the following vocabulary structure:
+
+### Vocabulary Composition (66,192 tokens total)
+
+1. **Standard tokens (4)**: `<pad>`, `<unk>`, `<bos>`, `<eos>`
+2. **Phonemes (109)**: IPA phoneme characters from `phoneme_list.json`
+3. **Audio tokens (65,536)**: `<audio_0>` to `<audio_65535>` (representing neural codec codes)
+4. **Special structure tokens (8)**:
+   - `<phoneme_start>`, `<phoneme_end>`
+   - `<audio_start>`, `<audio_end>`
+   - `<ref_audio_start>`, `<ref_audio_end>`
+   - `<ref_text_start>`, `<ref_text_end>`
+5. **Placeholder tokens (128)**: `<placeholder_0>` to `<placeholder_127>` (reserved for future use)
+
+## Training Sequence Format
+
+The model is trained on sequences with the following structure:
+
+```
+<phoneme_start> + [phoneme tokens] + <phoneme_end> + <audio_start> + [audio tokens] + <audio_end>
+```
+
+### Example Sequence
+
+For the text "You know, when":
+
+1. **Text**: "You know, when"
+2. **Phonemes**: `juː nˈoʊ, wˌɛn`
+3. **Training sequence**:
+   ```
+   <phoneme_start>
+   j u ː   n ˈ o ʊ ,   w ˌ ɛ n
+   <phoneme_end>
+   <audio_start>
+   <audio_2151> <audio_43235> <audio_56802> ... (audio tokens)
+   <audio_end>
+   ```
+
+### Training Objective
+
+- The model uses causal language modeling (next-token prediction)
+- **Phoneme tokens are masked** in the loss (labels set to -100)
+- **Only audio tokens are trained** to be predicted from the phoneme context
+- This teaches the model to generate audio tokens conditioned on phoneme input
+
+## Phoneme Encoding
+
+Text is converted to phonemes using espeak-ng with the following settings:
+- Language: `en-us`
+- Preserve punctuation: `True`
+- With stress markers: `True`
+
+Phonemes are then tokenized character-by-character (each IPA symbol is a separate token).
+
+## Audio Token Encoding
+
+Audio codes from the neural codec (range 0-65535) are mapped to vocabulary tokens:
+- Audio code `n` → token `<audio_n>` → token ID `(audio_token_start_id + n)`
+
+## Model Details
+
+- **Base model**: Qwen3-0.6B-Base
+- **Vocabulary size**: 66,192 tokens
+- **Training dataset**: neuphonic/emilia-yodas-english-neucodec
+- **Batch size**: 16 (effective)
+- **Precision**: bfloat16
+- **Attention**: Flash Attention 2
+
+## Usage
+
+To use this model, you'll need:
+1. The custom `PhonemeTokenizer` class (see `train_simple.py`)
+2. espeak-ng for phonemization
+3. A neural audio codec decoder for converting audio tokens to waveforms
+
+```python
+from transformers import AutoModelForCausalLM
+from train_simple import PhonemeTokenizer
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("syvai/plapre-simple")
+tokenizer = PhonemeTokenizer.from_pretrained("syvai/plapre-simple")
+
+# Your inference code here
+```
+
+## Files in Repository
+
+- `config.json` - Model configuration
+- `model.safetensors` / `pytorch_model.bin` - Model weights
+- `tokenizer_config.json` - Tokenizer configuration and vocabulary
+- `phoneme_list.json` - List of phonemes used in vocabulary
+- `README.md` - This file
+
+## Training Details
+
+Trained using the Hugging Face Transformers `Trainer` with:
+- Learning rate: 0.0002
+- Warmup steps: 1000
+- Gradient accumulation: 4
+- Per-device batch size: 4
+- Optimizer: AdamW
+
+## License
+
+Inherits license from Qwen3-0.6B-Base.