CPU TTS benchmark with UTMOS MOS scoring: Kokoro, Supertonic, Inflect-Nano, and Kyutai's new Pocket TTS [P]
A new benchmark compares CPU-based text-to-speech models using UTMOS MOS scoring, including Kyutai's Pocket TTS, Kokoro, Supertonic, and Inflect-Nano.
- Kyutai's Pocket TTS was benchmarked against Kokoro, Supertonic, and Inflect-Nano using UTMOS MOS scoring on CPU.
- Kokoro 82M is available in PyTorch and ONNX Runtime versions, while Supertonic 3 was tested at 2 and 5 flow-matching steps.
- The benchmark highlights architectural differences, particularly in Kyutai's Pocket TTS, which sets it apart from other models.
- UTMOS MOS scoring provides an objective measure of TTS model quality for CPU-based inference.
A developer has published a CPU-based text-to-speech (TTS) benchmark comparing several lightweight models using UTMOS Mean Opinion Score (MOS) scoring. The benchmark includes Kokoro 82M, Supertonic 3, Inflect-Nano, and Kyutai's newly introduced Pocket TTS. Each model was evaluated for its performance on CPU-only inference, providing a head-to-head comparison of their output quality.
Kokoro 82M, inspired by StyleTTS2, is available in both PyTorch and ONNX Runtime versions. Supertonic 3 was tested at two different flow-matching steps (2 and 5) using a Vector Estimator backbone. Kyutai's Pocket TTS stands out due to its unique architecture, which differs from the other models included in the benchmark. The benchmark aims to help developers evaluate small TTS models for deployment on resource-constrained hardware.
UTMOS MOS scoring was used to objectively measure the quality of the generated speech, offering a standardized way to compare the models. The results provide insights into the trade-offs between model size, computational requirements, and output quality for CPU-based TTS systems.
Source: CPU TTS benchmark with UTMOS MOS scoring: Kokoro, Supertonic, Inflect-Nano, and Kyutai's new Pocket TTS [P]. Read the full piece at the source.
Provides a practical comparison of lightweight TTS models for CPU deployment, aiding in model selection for edge applications.
Offers insights into the performance of emerging TTS technologies for non-experts interested in AI-driven speech synthesis.
- UTMOS MOS
- A standardized metric for evaluating the quality of text-to-speech outputs, where higher scores indicate better perceived speech quality.
- Flow-matching steps
- A technique used in generative models to iteratively refine outputs, with more steps generally improving quality but increasing computational cost.
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