GitHub - parlance/ctcdecode: PyTorch CTC Decoder bindings(C++，不支持Win)pyctcdecode https://github.com/nanoporetech/fast-ctc-decode ASR Inference with CTC Decoder(对torch和torchaudio版本有要求)ASR Inference with CUDA
decoder部分,在脑图中的位置 下面开启decoder的部分,也是我目前感觉完全看不明白的部分: >/opt/conda/lib/python3.8/site-packages/nemo/collections/asr/models/ctc_models.py(166)__init__()165importipdb;ipdb.set_trace()-->166self.decoder=EncDecCTCModel.from_config_dict(self._cfg.decoder)167 还是和...
from ctcdecode import OnlineCTCBeamDecoder decoder = OnlineCTCBeamDecoder( labels, model_path=None, alpha=0, beta=0, cutoff_top_n=40, cutoff_prob=1.0, beam_width=100, num_processes=4, blank_id=0, log_probs_input=False ) state1 = ctcdecode.DecoderState(decoder) probs_seq = torch.Float...
Inputs to CTCBeamDecoder Inputs to the decode method Outputs from the decode method More examples Resources ctcdecode is an implementation of CTC (Connectionist Temporal Classification) beam search decoding for PyTorch. C++ code borrowed liberally from Paddle Paddles'DeepSpeech. It includes swappable sco...
PyTorch CTC Decoder bindings machine-learningdecoderpytorchbeam-searchctcctc-loss UpdatedApr 4, 2024 C++ The official repository of the Eesen project tensorflowspeech-recognitionspeech-to-textkaldiasrctcctc-loss UpdatedMay 23, 2019 C++ Connectionist Temporal Classification (CTC) decoding algorithms: best ...
...四、模型的训练 4.1 损失函数 损失函数采用CTC损失函数,直接调用ctc_ops.ctc_loss 进行计算,其输入为训练文本标签、神经网络的输出logits和 序列长度。...五、解码 5.1 ctc_ops.ctc_beam_search_decoder 解码 最简单的方式我们使用tensorflow自带的ctc_ops.ctc_beam_search_decoder()...
例如我们选择keras yolo3进行文字检测,选择pytorch进行文字识别,去掉文字方向检测(假定输入的图片绝大多数是方向正确的),那么即可对chineseocr的源代码进行大幅精简。在model.py代码的基础上进行修改,去繁存简,对识别能力进行封装,方便提供给其它应用程序使用。修改后的核心代码如下: ...
于注意力的编解码器(attentionbasedencoderdecoder,1相关工作 [5-6][7-8] AED)和换能器(transducers)。这些深度学习模型1.1Conformer编码器 易于搭建、调优,在某些应用场景方面的识别率都超过[15] 由Gulati等提出的Conformer对比文献[9]将卷积 [5] 了基于传统语音识别方法的模型,还可以将多个模型和自我注意相结合...
Although these models used the most common atten- tion types in speech recognition, the calculations have low bounds to monotonicity, which may generate ran- dom connections between encoders and decoders. We believe current attentions may still be simpler in struc- ture than they should be. ...
出现Nan值的情况。 01 Loss计算中出现Nan值 在搜索以后，找到StackOverflow上找到大致的一个解决办法（...