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Introducing Parler-TTS: an Inference and Training Library for High-Quality, Controllable Text-to-Speech (TTS) Models
To advance open-source TTS research, we are making all datasets, training code, and our first iteration checkpoint, Parler-TTS Mini v0.1, available.
Key Takeaways:
- Ethical Framework: Parler-TTS prioritizes ethical considerations by avoiding invasive voice cloning methods, using permissive data, and enabling voice control through simple text prompts.
- Open-Source Innovation: By releasing all related materials under a permissive license, Parler-TTS fosters an environment of collaboration and open innovation in the TTS research community.
- Minimal Data, Maximum Quality: Despite being trained on relatively small datasets, Parler-TTS Mini v0.1 is capable of producing high-fidelity speech across various speaking styles, demonstrating the efficiency and potential of the model.
- Architectural Advancements: Incorporating elements from the MusicGen architecture and introducing novel modifications, Parler-TTS offers a flexible and powerful framework for generating natural-sounding, diverse speech.
- Community Engagement: The open-source nature of Parler-TTS encourages the AI and research community to participate in the ongoing development and refinement of TTS technologies, paving the way for more ethical and innovative applications in the field.
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