3D-Speaker-MT

3D-Speaker-MT is a multi-task learning-based speaker recognition model capable of performing tasks such as speaker verification and identification simultaneously, achieving high-precision voice identity recognition across multiple scenarios.

Manually download the model and upload it to raspberrypi5, or pull the model repository using the following command.

Tip

If git lfs is not installed, please refer to git lfs installation guide for installation instructions.

git clone https://huggingface.co/AXERA-TECH/3D-Speaker-MT.axera

File Description:

m5stack@raspberrypi:~/rsp/3D-Speaker-MT.axera $ ls -lh
total 64K
-rwxrwxr-x 1 m5stack m5stack 7.7K Sep 29 14:39 ax_meeting_transc_demo.py
drwxrwxr-x 4 m5stack m5stack 4.0K Sep 29 14:39 ax_model
-rwxrwxr-x 1 m5stack m5stack    0 Sep 29 14:39 config.json
-rwxrwxr-x 1 m5stack m5stack  33K Sep 29 14:39 model.py
-rwxrwxr-x 1 m5stack m5stack 3.4K Sep 29 14:39 README.md
-rwxrwxr-x 1 m5stack m5stack   74 Sep 29 14:39 requirements.txt
drwxrwxr-x 5 m5stack m5stack 4.0K Sep 29 14:39 utils
drwxrwxr-x 2 m5stack m5stack 4.0K Sep 29 14:39 wav

Create virtual environment

python -m venv speaker

Activate virtual environment

source speaker/bin/activate

Install dependencies

pip install https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc1/axengine-0.1.3-py3-none-any.whl
pip install -r requirements.txt

python3 ax_meeting_transc_demo.py --output_dir output_dir --wav_file wav/vad_example.wav

Run result:

(esrgan) m5stack@raspberrypi:~/rsp/Real-ESRGAN $ python3 main.py --input test_256.jpeg --output test_256_20e.jpeg --model ax650/realesrgan-x4-256.axmodel
[INFO] Available providers:  ['AXCLRTExecutionProvider']
[INFO] Using provider: AXCLRTExecutionProvider
[INFO] SOC Name: AX650N
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Compiler version: 3.4 3dfd5692
input.1 [1, 256, 256, 3] uint8
1895 [1, 1024, 1024, 3] float32
Original Image Shape: (243, 243, 3)
Preprocessed Image Shape: (1, 256, 256, 3)
Inference Time: 454.03 ms
Output Shape: (1, 1024, 1024, 3)
Final Output Image Shape: (1024, 1024, 3)

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