Both repositories are maintained by the same team and contain the same model files. DataoceanAI1 is the newly created enterprise organization account, while DataoceanAI is kept to avoid breaking existing user download scripts and links.
Please do not regard either repository as an unofficial copy or unauthorized redistribution.
Dolphin-CN-Dialect is a multi-dialect ASR model developed by Dataocean AI and Tsinghua University, with a strong focus on Chinese dialect recognition and real-world deployment scenarios. Compared with the previous Dolphin series, Dolphin-CN-Dialect introduces significant improvements in tokenizer design, dialect-balanced training, streaming capability, hotword biasing, and deployment efficiency.
The model supports Mandarin Chinese and 22 Chinese dialects, while also maintaining multilingual ASR capability inherited from Dolphin. Dolphin-CN-Dialect supports both streaming and non-streaming inference, enabling practical deployment in latency-sensitive applications such as real-time transcription and industrial speech recognition systems.
Approach
Dolphin-CN-Dialect is built upon the Dolphin architecture and follows a joint CTC-Attention framework with:
Encoder: E-Branchformer
Decoder: Transformer Decoder
Training Objective: Joint CTC + Attention loss
Compared to Dolphin, Dolphin-CN-Dialect introduces several important improvements:
Temperature-based data sampling for balancing standard Mandarin and low-resource dialects
Redesigned tokenizer with:
character-level modeling for Chinese
BPE-based subword modeling for English
extensible dialect tokens
Streaming ASR support
Hotword-biased decoding, including:
encoder-level contextual biasing
prompt-based decoder biasing
Experimental results show that Dolphin-CN-Dialect achieves:
38% improvement in dialect recognition accuracy
16.3% relative CER reduction over Dolphin
Competitive performance with recent large-scale ASR systems while maintaining a smaller model size
