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FireRedASR-AED-L

An open source industrial-grade automatic speech recognition model that supports Mandarin, dialects and English with excellent performance.

#Open source
#multilingual
#speech recognition
#high performance
#Industrial grade
FireRedASR-AED-L

Product Details

FireRedASR-AED-L is an open source industrial-grade automatic speech recognition model designed to meet the needs of high efficiency and high performance speech recognition. The model uses an attention-based encoder-decoder architecture and supports multiple languages ​​such as Mandarin, Chinese dialects and English. It reached new top levels on public Mandarin speech recognition benchmarks and performed well in singing lyrics recognition. The main advantages of this model include high performance, low latency, and broad applicability to a variety of voice interaction scenarios. Its open source feature allows developers to freely use and modify the code, further promoting the development of speech recognition technology.

Main Features

1
Supports speech recognition in Mandarin, Chinese dialects and English
2
Achieve top levels on public Mandarin speech recognition benchmarks
3
Have excellent singing lyrics recognition ability
4
Open source code for developers to customize and optimize
5
Provides multiple model variants to meet different performance and efficiency needs

How to Use

1
1. Download the model files from Hugging Face and place them in the 'pretrained_models' folder.
2
2. Create a Python environment and install dependencies.
3
3. Convert audio files to 16kHz 16-bit PCM format.
4
4. Use command line tools or Python API to call the model for speech recognition.
5
5. Adjust model parameters, such as beam size and decoding length, as needed to optimize the recognition effect.

Target Users

This product is suitable for developers, enterprises and research institutions that require efficient speech recognition, especially for scenarios that need to support multiple languages ​​and dialects, such as intelligent customer service, voice assistants and educational applications. The open source nature makes it an ideal choice for academic research and commercial applications.

Examples

In the intelligent customer service system, user voice commands are quickly and accurately recognized and immediate responses are provided.

For educational applications to help students practice Mandarin pronunciation and listening comprehension.

In music production, accurately identify and transcribe singing lyrics to assist creation and editing.

Quick Access

Visit Website →

Categories

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› Development and Tools
› speech recognition

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