src/dataset.py

import torch
import torchaudio
import os
from torch.utils.data import Dataset
from .text_encoder import TextEncoder
from .config import HexaConfig

class HexaDataset(Dataset):
    """
    Real Dataset Loader for Hexa TTS.
    Expects a directory structure:
    /data_root
        /wavs/
        metadata.csv (formatted: filename|text)
    """
    def __init__(self, root_dir, config: HexaConfig, train=True):
        self.root_dir = root_dir
        self.config = config
        self.encoder = TextEncoder()
        
        self.wav_dir = os.path.join(root_dir, "wavs")
        self.metadata_path = os.path.join(root_dir, "metadata.csv")
        
        self.files = []
        if os.path.exists(self.metadata_path):
            with open(self.metadata_path, 'r', encoding='utf-8') as f:
                for line in f:
                    parts = line.strip().split('|')
                    if len(parts) >= 2:
                        self.files.append((parts[0], parts[1]))
        else:
            print(f"Warning: Metadata not found at {self.metadata_path}")
            
        # Mel Spectrogram Transform
        self.mel_transform = torchaudio.transforms.MelSpectrogram(
            sample_rate=config.sample_rate,
            n_fft=config.n_fft,
            win_length=config.win_length,
            hop_length=config.hop_length,
            n_mels=config.n_mel_channels
        )

    def __len__(self):
        return len(self.files)

    def __getitem__(self, idx):
        filename, text = self.files[idx]
        wav_path = os.path.join(self.wav_dir, filename + ".wav")
        
        # 1. Load Audio
        waveform, sr = torchaudio.load(wav_path)
        
        # Resample if needed
        if sr != self.config.sample_rate:
            resampler = torchaudio.transforms.Resample(sr, self.config.sample_rate)
            waveform = resampler(waveform)
            
        # 2. Compute Mel
        mel = self.mel_transform(waveform) # [channels, frames]
        mel = mel.squeeze(0).transpose(0, 1) # [frames, channels]
        
        # 3. Tokenize Text
        # Assuming English for starter dataset (LJSpeech)
        text_ids = self.encoder.preprocess(text, lang_code='en').squeeze(0)
        
        # 4. Dummy Speaker/Lang/Emotion for single-speaker dataset
        speaker = torch.tensor(0) 
        lang = torch.tensor(0)   
        emotion = torch.tensor(0)
        
        return text_ids, speaker, lang, emotion, mel

def collate_fn(batch):
    """
    Pads batch to longest sequence.
    """
    # Sort by text length for packing (optional but good practice)
    batch.sort(key=lambda x: x[0].shape[0], reverse=True)
    
    text_ids, speakers, langs, emotions, mels = zip(*batch)
    
    # Pad Text
    text_padded = torch.nn.utils.rnn.pad_sequence(text_ids, batch_first=True, padding_value=0)
    
    # Pad Mels
    mel_padded = torch.nn.utils.rnn.pad_sequence(mels, batch_first=True, padding_value=0.0)
    
    speakers = torch.stack(speakers)
    langs = torch.stack(langs)
    emotions = torch.stack(emotions)
    
    return text_padded, speakers, langs, emotions, mel_padded