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Seed-ASR - ByteDance's AI speech recognition model

Seed-ASR is an ASR (Automatic Speech Recognition) model developed by ByteDance based on a Large Language Model (LLM). It was trained on over 20 million hours of speech data and nearly 900,000 hours of paired ASR data, supporting Mandarin and 13...

What is Seed-ASR?

Seed-ASR is an ASR (Automatic Speech Recognition) model developed by ByteDance, based on a large language model (LLM). Trained on over 20 million hours of speech data and nearly 900,000 hours of paired ASR data, it supports transcription of Mandarin and 13 Chinese dialects, and can recognize English and seven other foreign languages. Seed-ASR employs self-supervised learning, supervised fine-tuning, context-aware training, and reinforcement learning techniques to improve recognition accuracy and contextual understanding. It can accurately transcribe speech in video, live streaming, and conference settings, even in multi-person conversations or with background noise, achieving an error rate 10%-40% lower than existing large-scale ASR models. Seed-ASR's context-aware capabilities make it more effective in applications such as intelligent assistants and voice search.

Main functions of Seed-ASR

  • High-precision speech recognitionIt can accurately identify and transcribe speech signals from multiple languages, dialects, and accents.
  • Multilingual supportIt supports Mandarin, English, and many other languages, and has the ability to expand to more than 40 languages.
  • Context awarenessBy leveraging contextual information such as historical dialogues and video editing history, the accuracy of keyword recognition and transcription can be improved.
  • Large-scale trainingTraining is based on a large amount of speech data to enhance the model's generalization ability.
  • Phased training strategyThe model performance is gradually improved through stages such as self-supervised learning, supervised fine-tuning, context fine-tuning, and reinforcement learning.
  • Long speech processingIt effectively handles long speech inputs, maintaining the integrity of information and the accuracy of transcription.

The technical principle of Seed-ASR

  • Large Language Model (LLM) FoundationSeed-ASR is built on a large language model and is based on powerful text understanding and generation capabilities.
  • Audio-Conditional Language Model (AcLLM) FrameworkThe framework inputs continuous speech representations and contextual information into a pre-trained LLM, enabling the model to understand speech content and generate corresponding text.
  • Self-Supervised Learning (SSL)Audio encoders can capture rich speech features when trained on large-scale, unlabeled speech data.
  • Supervisory fine-tuning (SFT)After the SSL phase, a large number of speech-text pairs are used for training to establish a speech-to-text mapping.
  • Context-aware trainingBy incorporating contextual information (such as historical dialogues, video editing history, etc.) into the training, the model's ability to recognize specific contexts can be improved.
  • Reinforcement Learning (RL)We further optimize the model's text generation behavior by using a reward function based on ASR performance metrics, especially for accurate transcription of semantically important parts.

Seed-ASR project address

How to use Seed-ASR

  • Environmental preparationEnsure that the hardware and software requirements for Seed-ASR to run are met, such as sufficient computing power, memory, and storage space.
  • Get the modelAuthorized users can obtain the Seed-ASR model and all its required dependency libraries from ByteDance or related channels.
  • Data preparationCollect and prepare the speech data that the model wants to process. This includes audio files or real-time speech streams.
  • Data preprocessingPreprocessing of speech data as needed, such as noise reduction, segmentation, and normalization, can improve recognition accuracy.
  • Model ConfigurationConfigure Seed-ASR model parameters according to the application scenario, including language selection, context information input, etc.
  • Model DeploymentDeploy the Seed-ASR model to a server or cloud platform to ensure that it can receive and process voice data.

Application scenarios of Seed-ASR

  • Smart assistants and voice interactionProvides voice command recognition and interaction functions in smartphones, smart home devices, etc.
  • Automatic subtitle generationIt automatically generates subtitles for video content, live streams, meetings, etc., improving the accessibility of the content.
  • Meeting minutes and transcriptionAutomatically record speech and transcribe it into text during business meetings, lectures, seminars, and other occasions.
  • Customer ServiceAutomatically understand customer speech in call centers or online customer service to provide faster responses and problem resolution.
  • Voice searchProvide voice input in search engines or applications to help users quickly find the information they need through voice.
  • Language learning and educationIt assists language learners in practicing pronunciation and listening skills, providing real-time feedback and improvement suggestions.
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