# 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.