Text-to-Speech
Transformers
Safetensors
arktts
feature-extraction
audio
tts
voice-cloning
zero-shot
multilingual
custom_code
Instructions to use Edge0/Audio8-TTS-Realistic-Air with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Edge0/Audio8-TTS-Realistic-Air with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="Edge0/Audio8-TTS-Realistic-Air", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Edge0/Audio8-TTS-Realistic-Air", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Link Audio8 technical report and correct paper associations
Browse files
README.md
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This release supports **23 languages**, adding **12 languages compared with Preview**. It uses the same Transformers inference interface as Preview. This repository includes the complete model checkpoint, its 44.1 kHz neural audio codec, tokenizer, processor, and custom Transformers code.
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## Highlights
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- **Improved realism:** more natural prosody and expressive delivery, building on Preview.
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| CosyVoice3-1.5B | 1.5 | 1.67 | 6.89 | — |
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| VoxCPM2 | 2.3 | 1.41 | 5.35 | — |
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> **Sources:** Qwen Seed-TTS results are from the
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> **Parameter conventions:** Audio8's 0.6B excludes its separate codec; Qwen and CosyVoice use release labels; Higgs Audio v3 uses its nominal **4B** release label. The remaining sizes are rounded checkpoint counts used in our report (VoxCPM2 excludes its separate AudioVAE). These counts use different conventions across systems.
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This release supports **23 languages**, adding **12 languages compared with Preview**. It uses the same Transformers inference interface as Preview. This repository includes the complete model checkpoint, its 44.1 kHz neural audio codec, tokenizer, processor, and custom Transformers code.
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**Technical report:** [Training a Compact, Strong, and Realistic Text-to-Speech Model](https://arxiv.org/abs/2610.14776)
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## Highlights
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- **Improved realism:** more natural prosody and expressive delivery, building on Preview.
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| CosyVoice3-1.5B | 1.5 | 1.67 | 6.89 | — |
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| VoxCPM2 | 2.3 | 1.41 | 5.35 | — |
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> **Sources:** Qwen Seed-TTS results are from the Qwen3-TTS report, linked from the [official Qwen3-TTS repository](https://github.com/QwenLM/Qwen3-TTS); Qwen's CV3/MiniMax results, all Higgs Audio v3 results, and MOSS-TTS-v1.5's CV3/MiniMax results are from [Boson AI's evaluation](https://huggingface.co/bosonai/higgs-audio-v3-tts-4b). Fish S2 Pro's MiniMax result is from the Fish Audio S2 report, linked from the [official Fish Speech repository](https://github.com/fishaudio/fish-speech); its Seed-TTS/CV3 results and Higgs Audio v2's results are our reevaluations. MOSS-TTS-v1.5's Seed-TTS result and the CosyVoice3/VoxCPM2 results are compiled in the [VoxCPM repository](https://github.com/OpenBMB/VoxCPM). Cross-project SIM values are omitted because speaker encoders and evaluation conventions differ.
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> **Parameter conventions:** Audio8's 0.6B excludes its separate codec; Qwen and CosyVoice use release labels; Higgs Audio v3 uses its nominal **4B** release label. The remaining sizes are rounded checkpoint counts used in our report (VoxCPM2 excludes its separate AudioVAE). These counts use different conventions across systems.
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