What is OpenAudio S1
OpenAudio S1 isFish Audioa text-to-speech (TTS) model launched by Fish Audio, trained on over 2 million hours of audio data and supporting 13 languages. It adopts a Dual-AR architecture and Reinforcement Learning from Human Feedback (RLHF) technology, generating voices that are highly natural and fluent, almost indistinguishable from human dubbing. The model supports over 50 emotion and tone markers, allowing users to flexibly adjust speech expression through natural language instructions. OpenAudio S1 supports zero-shot and few-shot voice cloning, requiring only 10 to 30 seconds of audio samples to generate high-fidelity cloned voices.
Main features of OpenAudio S1
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Highly natural speech output: Trained on over 2 million hours of audio data, the generated speech is almost indistinguishable from human dubbing, suitable for professional scenarios such as video dubbing, podcasts, and game character voices.
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Rich emotion and tone control: Supports over 50 emotion markers (such as anger, happiness, sadness, etc.) and tone markers (such as hurried, whispered, screaming, etc.). Users can control the emotion and tone of the speech through simple text commands.
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Powerful multilingual support: Supports up to 13 languages, including English, Chinese, Japanese, French, German, etc., demonstrating strong multilingual capabilities.
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Efficient voice cloning: Supports zero-shot and few-shot voice cloning, requiring only 10 to 30 seconds of audio samples to generate high-fidelity cloned voices.
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Flexible deployment options: Provides two versions, the full version S1 with 4 billion parameters and the S1-mini with 500 million parameters, the latter being an open-source model suitable for research and educational purposes.
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Real-time application support: Ultra-low latency (below 100 milliseconds), suitable for real-time applications such as online games and live content.
Technical principles of OpenAudio S1
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Dual-AR architecture: Combines fast and slow Transformer modules to optimize the stability and efficiency of speech generation. The fast module is responsible for quickly generating preliminary speech features, while the slow module fine-tunes these features to ensure the naturalness and fluency of the speech.
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Grouped Finite Scalar Vector Quantization (GFSQ) technology: Improves codebook processing capability, reduces computational cost while ensuring high-fidelity speech output, and improves the model's operational efficiency.
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Reinforcement Learning from Human Feedback (RLHF): Through online RLHF technology, the model can more accurately capture the timbre and tone of speech, and the generated emotional expressions are more natural. Users can achieve delicate emotional control through markers such as (excited), (nervous), or (joyful).
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Large-scale data training: Trained on an audio dataset of over 2 million hours, covering a wide range of languages and emotional expressions, enabling the model to generate highly natural and diverse speech.
- Voice cloning technology: Supports zero-shot and few-shot voice cloning, requiring only 10 to 30 seconds of audio samples to generate high-fidelity cloned voices.
Project address of OpenAudio S1
- Project official website：https://openaudio.com/blogs/s1
Application scenarios of OpenAudio S1
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Content creation: Provides professional-level dubbing for videos, podcasts, and audiobooks, significantly improving production efficiency.
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Virtual assistant: Create personalized voice navigation or customer service systems that support interaction in multiple languages, enhancing the user experience.
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Games and Entertainment: Generate realistic dialogue and narration for game characters, enhancing players' sense of immersion.
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Education and Training: Used to generate multilingual learning content, helping students better understand and learn the pronunciation and intonation of different languages.
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Customer Service and Support: Suitable for customer service robots, providing fast and accurate voice responses, improving the efficiency and quality of customer service.