FireRedASR

FireRedASR

Say hello to FireRedASR! It is a top notch open source speech recognition model that works really well with Mandarin speech. The FireRed team created this model, and it has set new standards in speech recognition technology.

Key Features

FireRedASR has two main parts:

FireRedASR-LLM
It aims to give the best accuracy in speech recognition.
It mixes large language models to be very precise.
It is great for tasks that need high recognition accuracy.

FireRedASR-AED
It balances good performance with saving computer resources.
It is based on the Attention based Encoder Decoder structure.
It is perfect for tasks that do not have many resources because it is small.

Benefits

FireRedASR offers several good things:

It has high accuracy. FireRedASR LLM has a low Character Error Rate CER of 3.05 percent, which is better than older models.
It is efficient. FireRedASR AED works well with a smaller size, making it useful for many tasks.
It is open source. The model and its code are open to everyone, which helps the community improve speech recognition technology.

Use Cases

FireRedASR works well in real life situations:

It works well with Mandarin ASR in short videos, live shows, and voice input. It lowers CER by 23.7 percent to 40 percent compared to industry leaders.
It is great at lyric recognition. It lowers CER by 50.2 percent to 66.7 percent.
It works well with Chinese dialects and English. It does better than older open source models on KeSpeech and LibriSpeech tests.

Technical Innovations

FireRedASR brings new ideas:

It has an Encoder Adapter LLM Framework. FireRedASR LLM uses this to get high accuracy with large language models.
It has an Attention based Encoder Decoder. FireRedASR AED uses this to balance performance and efficiency.

Cost/Price

The product is open source and free on GitHub and Hugging Face. This makes it easy for developers and researchers to use without any cost.

Funding

The article does not talk about funding details for FireRedASR.

Reviews/Testimonials

FireRedASR gets good feedback for its high accuracy and efficiency in speech recognition, especially in Mandarin and other languages. Users like that it is open source, which lets the community help and improve it continuously.