This method outlines a scalable and efficient approach to generating fully offline, real-time text-to-speech (TTS) voices, particularly in low-resource languages or dialects, using no real recordings of the target speaker. It combines donor TTS, voice conversion, and lightweight TTS model training to synthesize high-quality, personalized voices that can run on edge devices like the Raspberry Pi 4.
Many languages and dialects lack open, high-quality, and privacy-respecting TTS solutions. Traditional approaches often require:
- Large corpora of transcribed recordings,
- Significant compute for model training,
- Ongoing cloud connectivity at runtime.
This method addresses these limitations by:
- Removing the need for real speaker recordings (optional target voice),
- Generating training data synthetically,
- Producing compact, embeddable models optimized for fully offline use.
The technique is a multi-stage pipeline consisting of:
A high-quality, language-specific TTS system (proprietary or open-source) generates an arbitrary amount of clean audio/text pairs. This serves as the base dataset for further processing.
Each synthesized utterance is converted into a new voice, using voice cloning or speaker adaptation techniques. This step transfers the prosody, style, and speaker identity of a target voice onto the donor speech.
- If no specific target speaker is desired, a generic or randomly sampled speaker identity can be used.
- For ethical use, all real voices used must be public domain or have documented consent.
The cloned dataset is used to train a lightweight, end-to-end TTS model based on ViTS (Voice Transformer TTS), a non-autoregressive transformer architecture optimized for speed and low memory footprint.
- Models are quantized and optimized for real-time inference on CPUs.
- Final models are typically under 100MB and require no internet access.
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Zero Real Data Required Enables voice creation even where no recordings exist for a speaker or language.
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Fully Offline Execution Models are deployable on edge devices without cloud access.
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Customizable Voices Voices can be stylized or personalized without retraining donor models.
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Privacy-Preserving Final voices are not direct replicas of real individuals unless explicitly intended and authorized.
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Rapid Language Expansion Enables TTS deployment in underrepresented or low-resource linguistic communities.
This method respects individual voice rights and emphasizes responsible AI deployment:
- Real human voices are only used with explicit consent or when in the public domain.
- The voice conversion model acts as a filter, intentionally reducing fidelity to the original voice where anonymization is required.
- Final TTS models are typically non-identifiable, ensuring that output speech is synthetic and not a replica of any known speaker.
- Open-source voice assistants (e.g., OpenVoiceOS)
- Home automations and domotics (e.g., Home Assistant)
- Real-time AAC (Augmentative and Alternative Communication) tools
- Embedded IoT devices and robotics
- Video game NPCs and interactive characters
- Educational and accessibility platforms
This approach has been successfully applied to European Portuguese, resulting in the creation of several unique, high-quality voices for offline deployment. Expansion to other languages and voice types is ongoing.
For more information or to request a demonstration, please contact us. Research inquiries welcome.