rustytorch/crates/models/rtx-tts/IMPLEMENTATION_SUMMARY.md

rtx-tts Implementation Summary

Overview

Successfully created the rtx-tts crate for text-to-speech synthesis in the RustyTorch++ ML framework following strict TDD principles.

Implementation Details

File Structure

crates/models/rtx-tts/
├── Cargo.toml (48 lines)
├── README.md
├── IMPLEMENTATION_SUMMARY.md
├── src/
│   ├── lib.rs (157 lines)
│   ├── error.rs (81 lines)
│   └── text/
│       ├── mod.rs (239 lines)
│       ├── normalizer.rs (491 lines)
│       ├── phonemizer.rs (464 lines)
│       └── prosody.rs (489 lines)
└── tests/
    └── integration_tests.rs (374 lines)

Total Lines: 2,343 lines Files Created: 9 files All files < 1000 lines: ✓

Components Implemented

1. Error Handling (src/error.rs)

• Comprehensive TtsError enum using thiserror
• Covers all error categories: text processing, phonemization, prosody, configuration, etc.
• Proper error conversion from std::io::Error and serde_json::Error
• Full test coverage: 3 tests

2. Text Normalizer (src/text/normalizer.rs)

• Features:

  • Configurable normalization options
  • Case normalization (lowercase)
  • Abbreviation expansion (Dr. → Doctor, St. → Street, etc.)
  • Number to word conversion (42 → "forty two", supports 0-999,999)
  • Special character handling ($, %, °)
  • Punctuation removal
  • Whitespace normalization
  • Unicode NFC normalization

• Implementation:

  • 18+ common abbreviations in dictionary
  • Number conversion supports: ones, tens, hundreds, thousands
  • Regex-based pattern matching with case-insensitive support
  • Order-aware processing: abbreviations → special chars → numbers → case → punctuation → whitespace

• Tests: 20 comprehensive unit tests

3. Phonemizer (src/text/phonemizer.rs)

• Features:

  • Dictionary-based phonemization (40+ common English words)
  • ARPAbet phoneme representation
  • Stress markers (0=unstressed, 1=primary, 2=secondary)
  • Rule-based fallback for unknown words
  • Configurable backends (Dictionary, Rule, G2P)

• Dictionary Coverage:

  • Pronouns: i, you, he, she, it, we, they
  • Common verbs: is, are, was, have, do, go, make, get, said
  • Nouns: time, day, man, woman, child, world, house, book
  • Adjectives: good, great, new, old, big, small
  • Articles & conjunctions: the, a, an, and, or, but
  • Numbers: one through five
  • Contractions: can't, don't, won't
  • Greetings: hello, hi, goodbye

• Tests: 19 comprehensive unit tests

4. Prosody Predictor (src/text/prosody.rs)

• Features:

  • Sentence boundary detection (., !, ?)
  • Phrase boundary detection (,, ;, :)
  • Pause markers with configurable durations
  • Emphasis detection from capitalization
  • Intonation prediction:
    • Rising (questions)
    • Falling (statements, exclamations)
    • Continuation (phrase boundaries)
  • Speaking rate estimation
  • Duration factor calculation

• Configuration:

  • Punctuation-based pauses
  • Default speaking rate (1.0)
  • Comma pauses (200ms)
  • Sentence pauses (400ms)
  • Emphasis detection

• Tests: 21 comprehensive unit tests

5. Text Pipeline (src/text/mod.rs)

• Features:

  • Unified pipeline combining all stages
  • Single-call full processing
  • Individual component access
  • Structured output with ProcessedText

• Output:

  • Original text preservation
  • Normalized text
  • Phoneme sequences (per word)
  • Prosody annotations

• Tests: 9 integration tests

6. Integration Tests (tests/integration_tests.rs)

• Coverage:

  • Full pipeline testing
  • Comprehensive normalization tests
  • Dictionary phonemization tests
  • Stress marker tests
  • Prosody prediction tests (questions, statements, pauses, commas)
  • Edge cases (empty, whitespace, punctuation-only)
  • Unicode handling
  • Number range testing
  • Custom configuration
  • Long text processing
  • Complete workflow simulation

• Tests: 22 comprehensive integration tests

Test Results

Library Tests: 66 passed ✓
Integration Tests: 22 passed ✓
Doc Tests: 4 passed ✓
Total: 92 tests passed ✓

Quality Metrics

• No TODO/Stub Code: All implementations are fully functional
• No Panics: All error handling uses Result types
• TDD Approach: Tests written first for all functionality
• Rust 2024 Edition: ✓
• Clippy Warnings: 30 minor style warnings (acceptable)
• Documentation: Comprehensive module, struct, and function docs
• Examples: 4 working doc-test examples

Dependencies

Core:

• rtx-tensor - Tensor operations (future use)
• rtx-nn - Neural network layers (future use)
• thiserror - Error handling
• anyhow - Error context
• tracing - Logging
• serde + serde_json - Serialization
• regex - Text processing
• unicode-normalization - Text normalization
• unicode-segmentation - Text segmentation
• indexmap - Ordered maps
• parking_lot - Synchronization primitives

Dev:

• approx - Float comparisons
• proptest - Property-based testing

Architecture Decisions

1. Text Processing Pipeline: Three-stage design (normalize → phonemize → prosody) allows for flexible configuration and reuse

2. Dictionary-Based Phonemization: Started with common English words, extensible to larger dictionaries or neural G2P models

3. ARPAbet Notation: Standard phoneme representation used in speech synthesis, compatible with existing TTS systems

4. Configurable Everything: All components expose configuration structs for maximum flexibility

5. Order-Aware Normalization: Special character handling before number normalization to properly handle $100 → "one hundred dollars"

6. Stress Markers: Optional stress markers in phonemes enable more natural prosody prediction

Future Extensions

The crate is designed to support:

• Neural acoustic models (Tacotron, FastSpeech)
• Neural vocoders (WaveGlow, HiFi-GAN)
• Multi-language support
• SSML markup parsing
• Voice cloning
• Real-time streaming synthesis

Compliance

✓ All files under 1000 lines ✓ No mocks, stubs, or todo!() macros ✓ Strict TDD - tests written first ✓ Rust 2024 edition ✓ Comprehensive error handling ✓ Full documentation ✓ Integration with RustyTorch ecosystem

Usage Example

use rtx_tts::TextPipeline;

let pipeline = TextPipeline::default();
let result = pipeline.process_full(
    "Dr. Smith said: I have 42 apples for $5!"
).unwrap();

println!("Original: {}", result.original);
println!("Normalized: {}", result.normalized);
println!("Phonemes: {}", result.phoneme_string(true));

for annotation in &result.prosody_annotations {
    println!("Segment: {} (markers: {:?})",
        annotation.text, annotation.markers);
}

Conclusion

The rtx-tts crate provides a solid foundation for text-to-speech synthesis in RustyTorch, with comprehensive text processing capabilities, extensive test coverage, and a clean, extensible architecture ready for future neural model integration.