

Fireredasr AED L
Overview :
FireRedASR-AED-L is an open-source, industrial-grade automatic speech recognition model designed to meet the needs for high efficiency and performance in speech recognition. This model utilizes an attention-based encoder-decoder architecture and supports multiple languages including Mandarin, Chinese dialects, and English. It achieved new record levels in public Mandarin speech recognition benchmarks and has shown exceptional performance in singing lyric recognition. Key advantages of the model include high performance, low latency, and broad applicability across various speech interaction scenarios. Its open-source feature allows developers the freedom to use and modify the code, further advancing the development of speech recognition technology.
Target Users :
This product is suitable for developers, enterprises, and research institutions that require efficient speech recognition, especially in scenarios that support multiple languages and dialects, such as smart customer service, voice assistants, and educational applications. Its open-source nature makes it an ideal choice for academic research and commercial applications.
Use Cases
In smart customer service systems, quickly and accurately recognize user voice commands to provide instant responses.
Used in educational applications to help students practice Mandarin pronunciation and listening comprehension.
In music production, accurately recognize and transcribe singing lyrics to assist in creation and editing.
Features
Supports speech recognition in Mandarin, Chinese dialects, and English
Achieved top levels in public Mandarin speech recognition benchmarks
Exceptional singing lyric recognition capabilities
Open-source code, facilitating customization and optimization by developers
Offers a variety of model variants to meet differing performance and efficiency needs
How to Use
1. Download the model files from Hugging Face and place them in the 'pretrained_models' folder.
2. Create a Python environment and install the necessary dependencies.
3. Convert audio files to 16kHz 16-bit PCM format.
4. Use the command line tool or Python API to invoke the model for speech recognition.
5. Adjust model parameters, such as beam size and decoding length, as needed to optimize recognition performance.
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