You can deploy this model and start earning money today!

csukuangfj/
sherpa-onnx-streaming-paraformer-trilingual-zh-cantonese-en

Name: sherpa-onnx-streaming-paraformer-trilingual-zh-cantonese-en
Availability: InStock

onnx

Views

No views yet

Introduction

Files in this directory are from https://modelscope.cn/models/dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online/summary

First, we use the following Python code (using FunASR v0.8.8)

python

from funasr_onnx.paraformer_online_bin import Paraformer

model_dir = "dengcunqin/speech_paraformer-large_asr_nat-zh-cantonese-en-16k-vocab8501-online"
chunk_size = [5, 10, 5]

model = Paraformer(model_dir, batch_size=1, quantize=True, chunk_size=chunk_size, intra_op_num_threads=4) #, revision='v1.0.0') # only support batch_size = 1

to get the following files:

model.onnx
model_quant.onnx
decoder.onnx
decoder_quant.onnx

Then we rename the following three files:

model.onnx -> encoder.onnx
model_quant.onnx -> encoder.int8.onnx
decoder_quant.onnx -> decoder.int8.onnx

Then we run

./generate-tokens.py

to get tokens.txt.

Finally, we run

./add-model-metadata.py

to add metadata to encoder.onnx and encoder.int8.onnx.

Now you can use the generated files in sherpa-onnx.

csukuangfj/sherpa-onnx-streaming-paraformer-trilingual-zh-cantonese-en

Introduction

csukuangfj/
sherpa-onnx-streaming-paraformer-trilingual-zh-cantonese-en