许可证: cc-by-4.0
语言:

• 俄语
  基础模型:
• nvidia/stt_ru_fastconformer_hybrid_large_pc
  任务标签: 自动语音识别

NVIDIA FastConformer-Hybrid Large (俄语) 模型 已转换为ONNX格式，适用于 onnx-asr。

安装onnx-asr

pip install onnx-asr[cpu,hub]  

加载带CTC解码器的FastConformer俄语模型并识别wav文件

import onnx_asr  
model = onnx_asr.load_model("nemo-fastconformer-ru-ctc")  
print(model.recognize("test.wav"))  

加载带RNN-T解码器的FastConformer俄语模型并识别wav文件

import onnx_asr  
model = onnx_asr.load_model("nemo-fastconformer-ru-rnnt")  
print(model.recognize("test.wav"))  

模型导出代码

import nemo.collections.asr as nemo_asr  
from pathlib import Path  

model_name = "stt_ru_fastconformer_hybrid_large_pc"  
onnx_dir = Path("nemo-onnx")  
onnx_dir.mkdir(exist_ok=True)  

model = nemo_asr.models.ASRModel.from_pretrained("nvidia/" + model_name)  

# 导出带CTC解码器的混合模型  
# model.set_export_config({"decoder_type": "ctc"})  

model.export(str(Path(onnx_dir, "model.onnx")))  

with Path(onnx_dir, "vocab.txt").open("wt") as f:  
    for i, token in enumerate([*model.tokenizer.vocab, "<blk>"]):  
        f.write(f"{token} {i}\n")