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Better speech synthesis through scaling
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Better speech synthesis through scaling
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In recent years, the field of image generation has been revolutionized by the application of autoregressive transformers and DDPMs. These approaches model the process of image generation as a step-wise probabilistic processes and leverage large amounts of compute and data to learn the image distribution. This methodology of improving performance need not be confined to images. This paper describes a way to apply advances in the image generative domain to speech synthesis. The result is TorToise -- an expressive, multi-voice text-to-speech system. All model code and trained weights have been open-sourced at https://github.com/neonbjb/tortoise-tts.
Forward citations
Cited by 12 Pith papers
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FlexiSLM: A Dynamic and Controllable Frame Rate Spoken Language Model
FlexiSLM is the first spoken language model supporting dynamic and controllable frame rates on speech input and output, outperforming fixed-rate 7B models at high quality and enabling faster inference at lower rates l...
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Sarashina2.2-TTS: Tackling Kanji Polyphony in Japanese Speech Generation via Data Scaling and Targeted Data Synthesis
Sarashina2.2-TTS achieves SOTA kanji reading accuracy via data scaling and Joyo-kanji-targeted synthesis, introduces the Joyo Kanji Yomi Benchmark and Kana-CER metric, and shows stable cross-lingual performance.
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An Evaluation Framework for Text-to-Speech Voice Reconstruction
The paper introduces a subjective-objective evaluation framework using Best Worst Scaling and a novel dual-reference distributional measure to better assess intelligibility versus speaker identity trade-offs in TTS vo...
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X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning
X-Voice achieves zero-shot cross-lingual voice cloning across 30 languages by using IPA as a unified phonetic representation and a two-stage training process that first generates its own audio prompts then fine-tunes ...
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Step-Audio 2 Technical Report
Step-Audio 2 integrates a latent audio encoder, reasoning-centric reinforcement learning, and discrete audio token generation into language modeling to deliver state-of-the-art performance on audio understanding and c...
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DeePen: Penetration Testing for Audio Deepfake Detection
DeePen demonstrates that both production and academic audio deepfake detectors can be reliably deceived by simple signal processing attacks such as time-stretching or echo addition, with some attacks resistible via re...
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Seed-TTS: A Family of High-Quality Versatile Speech Generation Models
Seed-TTS models produce speech matching human naturalness and speaker similarity, with added controllability via self-distillation and reinforcement learning.
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MLAAD: The Multi-Language Audio Anti-Spoofing Dataset
MLAAD provides a large-scale multi-language synthetic audio dataset for training and evaluating audio anti-spoofing models, showing better training performance than InTheWild and FakeOrReal and alternating superiority...
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FlashTTS: Fast Streaming TTS with MTP Acceleration and X-pred Mean Flow Distillation
FlashTTS delivers a streaming TTS system using multi-track input processing and X-pred mean flow matching to reach 325 ms latency in two function evaluations while retaining zero-shot voice cloning.
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X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning
X-Voice achieves zero-shot cross-lingual voice cloning across 30 languages via IPA-based training on 420K hours of data and a two-stage paradigm that synthesizes its own audio prompts for text-masked fine-tuning.
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Enhancing Conversational TTS with Cascaded Prompting and ICL-Based Online Reinforcement Learning
A cascaded audio-prompting and ICL-based online RL method improves naturalness and expressivity in conversational TTS with reduced data needs.
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AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan
AT-ADD introduces standardized tracks and datasets for evaluating audio deepfake detectors on speech under real-world conditions and on diverse unknown audio types to promote generalization beyond speech-centric methods.
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