Text-To-Speech (TTS) Cheatsheet

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1. Models (Open-Source)

• XTTS-v2: High-quality TTS model with robust voice quality.
• MeloTTS-English: Specialized English voice synthesis with melodic intonation.
• F5-TTS: Produces high-quality speech with voice cloning and customization.
• Bark: High-quality multilingual model supporting varied accents and prosody.
• Parler-tts-mini-v1: Compact model optimized for quick demos and limited resource environments.

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Additional Notable Models

• FastSpeech2: Speed-focused, decent quality trade-off.
• VITS: End-to-end TTS offering high fidelity and voice controllability.
• SpeechT5: Unified-modal SpeechT5 framework that explores encoder-decoder pre-training for self-supervised speech/text representation learning.

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2. Inference Libraries / Toolkits

• Coqui TTS: Easy-to-use, community-driven TTS toolkit.
• Parler TTS: Inference and training library for high-quality TTS models.
• LitServe: Lightning-fast inference serving library for quick deployments.
• Mozilla TTS: Well-known toolkit with extensive model support and large community.
• Tortoise TTS: High-quality synthesis; slower but excellent results.

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Additional Toolkits

• ESPnet TTS: Unified end-to-end speech processing (ASR + TTS).
• NVIDIA NeMo: State-of-the-art models + easy fine-tuning on NVIDIA GPUs.

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3. Use Cases

• Voice Assistants & Virtual Agents
• Audiobooks & Podcast Generation
• Accessibility Tools (for visually impaired users)
• Interactive Learning & E-Learning Content
• Customer Support Bots & IVR Systems
• Content Localization & Dubbing for Media

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4. Deployment Options

• On-Premises Deployment: Running models on local servers for full control and data privacy.
• Cloud Services: Utilizing cloud providers like AWS, Azure, or Google Cloud for scalable deployment.
• Serverless GPU Platforms: Serverless GPU platforms like Inferless provide on-demand, scalable GPU resources for machine learning workloads, eliminating the need for infrastructure management and offering cost efficiency.
• Edge Deployment: Deploying models on edge devices for low-latency applications.
• Containerization: Using Docker or Kubernetes to manage and scale deployments efficiently.

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5. Datasets

• keithito/lj_speech: Popular single-speaker dataset for English TTS.
• facebook/multilingual_librispeech: Multilingual speech corpus for polyglot models.
• amphion/Emilia-Dataset: Specialized dataset for unique voice profiles.
• speechcolab/gigaspeech: Large-scale English speech corpus.
• parler-tts/mls_eng: English subset of the Multilingual LibriSpeech dataset.

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More Datasets

• VCTK: High-quality, multi-speaker dataset for diverse accents.
• Common Voice: Crowdsourced multilingual dataset.
• LibriTTS: Enhanced LibriSpeech variant for better TTS results.

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6. Training & Fine-Tuning Resources

• GitHub TTS Notebooks & Tutorials: Community-driven code examples and scripts.
• Hugging Face Audio Course, Unit 6 (From Text to Speech): Hands-on tutorials.
• Fine-Tuning a 🐸 TTS Model (Coqui TTS docs): Step-by-step instructions.
• NVIDIA NeMo TTS Guides: Hands-on TTS tutorial notebooks.
• VITS Fast Fine-tuning: Guide you to add your own character voices, or even your own voice, into existing VITS TTS model.

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7. Evaluation & Benchmarking

• Mean Opinion Score (MOS), Comparative MOS (CMOS): Subjective quality assessment.
• PESQ, POLQA, NISQA: Objective speech quality metrics.

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8. Model Optimization & Compression

• Quantization: Reduce model size & inference time (ONNX).
• Pruning & Distillation: Tailor models to resource constraints.
• Hardware Acceleration: GPUs, TPUs, or specialized inference chips.
• ONNX / TensorRT: Optimize models for low-latency, high-throughput inference.

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9. Integration & Workflow Tools

• Gradio / Streamlit: Rapid prototyping with web UIs.
• Airflow / Prefect: Automate data and training pipelines.
• CI/CD (GitHub Actions): Continuous integration for model updates.
• Hugging Face Spaces: Share and demo TTS models easily.

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10. Common Challenges & Troubleshooting

• Accents & Dialects: Use multilingual models or phoneme-based TTS.
• Latency Reduction: Optimize models, batch inference, use GPU acceleration.
• Pronunciation Issues: Text normalization and grapheme-to-phoneme conversion.
• Memory Constraints: Use smaller models or pruning/quantization techniques.

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11. Ethical Considerations

• Voice Consent & Licensing: Respect dataset/model licenses.
• Disclosure of Synthetic Speech: Inform users when speech is synthesized.
• Bias & Fairness: Be aware of biases in training data and model outputs.
• Deepfake Risks: Implement safeguards and watermarking.

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12. Licensing & Governance

• Check Licenses: (MIT, Apache 2.0, GPL) before commercial use.
• Hugging Face Model Cards: Follow best practices for transparency.
• Data Usage Agreements: Ensure compliance with dataset terms.