Lightweight Phoneme-Based TTS

This repository implements a lightweight phoneme-based Text-to-Speech (TTS) system trained on paired text–audio data. The model follows a NanoSpeech-inspired Conv1D encoder–decoder architecture that reconstructs mel-spectrograms from phoneme inputs, which are converted to waveforms using the Griffin–Lim vocoder.

This project is intended for educational and research purposes, focusing on simplicity, interpretability, and core TTS fundamentals.

---

Model Description

• Architecture: Lightweight Conv1D encoder–decoder (NanoSpeech-inspired)
• Input: ARPAbet phoneme sequences
• Output: Predicted mel-spectrograms
• Vocoder: Griffin–Lim (spectrogram → waveform)
• Framework: PyTorch
• Checkpoint: tts_model.pth

The model learns basic phoneme-to-acoustic mappings and produces intelligible speech while remaining computationally lightweight.

---

Dataset

• Source: Desivocal
• Metadata: metadata.csv containing text and corresponding audio filenames
• Audio: .wav files aligned with metadata entries

---

Phonemization

Text is converted to ARPAbet phonemes using the g2p-en library.

Example

"I invite you to embark on a profound journey" AY1 IH2 N V AY1 T Y UW1 T UW1 EH0 M B AA1 R K AA1 N AH0 P R OW0 F AW1 N D JH ER1 N IY0

---

Training Procedure

• Optimizer: Adam
• Loss Function: L1 reconstruction loss
• Epochs: 20 (configurable)
• Training Notebook: train_tts.ipynb

The model is trained to minimize reconstruction error between predicted and ground-truth mel-spectrograms.

---

Evaluation

Evaluation was performed on 3 held-out samples using L1 and L2 similarity metrics.

---

Audio Samples

Ground-truth and synthesized audio samples are available in:

results/audio_samples/ ├── gt_73.wav ├── pred_73.wav ├── gt_415.wav ├── pred_415.wav ├── gt_392.wav ├── pred_392.wav └── ground_truth.wav

---

Observations

• The model captures basic phoneme-to-mel mappings
• Synthesized speech is intelligible
• Natural prosody and expressiveness are limited
• Higher L1/L2 errors reflect:
  • small model size
  • limited training data
  • absence of a pretrained neural vocoder

---

Future Improvements

• Replace Griffin–Lim with a pretrained neural vocoder (e.g., HiFi-GAN, WaveGlow)
• Train with more data and a larger model
• Add style and prosody modeling for more natural speech
• Convert the model into a fully deployable TTS pipeline

---

Intended Use

This model is intended for:

• Learning phoneme-based TTS systems
• Research and experimentation
• Educational demonstrations of lightweight speech synthesis

It is not intended for production or commercial deployment.

---

License

This project is released under the MIT License.