What is Orpheus TTS
Orpheus TTS is an open-sourcetext-to-speech(TTS) system based on the Llama-3b architecture. Orpheus TTS supports generating natural, emotionally rich, and near-human-level speech, with zero-shotvoice cloningcapabilities, cloning a specific voice without pretraining. Orpheus TTS has a latency as low as about 200 milliseconds, making it suitable for real-time applications. Orpheus TTS provides multiple pretrained and fine-tuned models, allowing users to perform customized training based on a small amount of data to meet speech synthesis needs in different scenarios.
Main features of Orpheus TTS
- Near-human-level speech: Provides natural intonation, emotion, and rhythm.
- Zero-shot voice cloning: Clone a voice without pretraining.
- Guided emotion and intonation: Control voice and emotional characteristics based on simple tags.
- Low latency: Streaming latency for real-time applications is about 200 milliseconds, and input streaming can reduce latency to about 100 milliseconds.
- Supports multiple voice styles: Provides multiple preset voice styles (such as "tara", "leah", etc.), allowing users to choose different voice roles for synthesis as needed.
Technical principles of Orpheus TTS
- Based on the Llama architecture: Llama-3b serves as the base model architecture, combining powerful language understanding and generation capabilities to better handle the complexity of natural language in speech synthesis.
- Large-scale data training: The model is pretrained on more than 100,000 hours of English speech data and billions of text tokens, learning the prosody, intonation, and emotional expression of language based on large amounts of speech and text data.
- Non-streaming tokenizer and SNAC decoder: Uses a non-streaming (CNN-based) tokenizer and SNAC decoder, enabling uninterrupted streaming speech synthesis based on an improved decoder and avoiding the audio "pops" that may occur in traditional methods.
- Real-time streaming inference: Implemented based on efficient vLLM (very large language model), quickly generating speech on GPU and supporting real-time output and input streaming to meet the low-latency requirements of real-time applications.
- Emotion and intonation guidance: The model introduces emotion tags and text-speech pairs into the training data, learning speech characteristics under different emotional states and supporting user tag control of speech emotion and intonation.
Orpheus TTS project addresses
- Project official website：https://canopylabs.ai/model-releases
- GitHub repository：https://github.com/canopyai/Orpheus-TTS
- HuggingFace model library：https://huggingface.co/collections/canopylabs/orpheus-tts
Application scenarios of Orpheus TTS
- Audiobooks and podcasts: Convert text into natural speech and generate audio content.
- Virtual assistants: Provide natural voice interaction and support real-time conversation.
- Games: Generate personalized voices for game characters and enhance immersion.
- Education: Assist teaching and help students with listening training.
- Accessibility assistance: Help visually impaired people obtain information through speech.