### Exploring RNN-Transducer for Chinese speech recognition

2019-9-30 · RNN Transducer (RNN-T) 18 19 has been recently proposed as an extension of the CTC model. Speciﬁcally by adding an LSTM based prediction network RNN-T removes the conditional independence assumption in the CTC model. Moreover RNN-T does not need the entire utterance level representation before decoding which makes streaming end-

### cnblogs

2020-9-16 · TRANSFORMER TRANSDUCER A STREAMABLE SPEECH RECOGNITION MODELWITH TRANSFORMER ENCODERS AND RNN-T LOSS RNN-T transformerRNN

### RNN-Transducer Loss supportaddons

Ask questions RNN-Transducer Loss support I found that there are no RNN-Transducer loss in the new version. I have to use third party pkg like wrap-transducer So official support in both tf1.x and tf2 is therefore expected.

### How to implement an RNN (1/2)Minimal example

2021-5-22 · How to implement a minimal recurrent neural network (RNN) from scratch with Python and NumPy. The RNN is simple enough to visualize the loss surface and explore why vanishing and exploding gradients can occur during optimization. For stability the RNN will be trained with backpropagation through time using the RProp optimization algorithm.

### Improving RNN Transducer Modeling for Small-Footprint

2021-5-13 · The recurrent neural network transducer (RNN-T) model has been proved effective for keyword spotting (KWS) recently. However compared with cross-entropy (CE) or connectionist temporal classification (CTC) based models the additional prediction network in the RNN-T model increases the model size and computational cost. Besides since the keyword training data usually only contain the

### IMPROVING RNN TRANSDUCER MODELING FOR END-TO

2019-10-13 · The loss function of RNN-T is the negative log posterior of out-put label sequence y given input acoustic feature x L= lnP(yjx) (7) which is calculated based on the forward-backward algorithm de-scribed in 17 . The derivatives of loss Lwith respect to P(kjtu) is L P(kjtu) = (tu) P(yjx) 8 < (tu 1) if k= y u 1 (t 1u) if k= 0 otherwise (8)

### cnblogs

2020-9-16 · TRANSFORMER TRANSDUCER A STREAMABLE SPEECH RECOGNITION MODELWITH TRANSFORMER ENCODERS AND RNN-T LOSS RNN-T transformerRNN

### Neural Transducer for Speech Recognition

2021-3-11 · RNN-Transducer RNN-Transducer has three parts. (1) Transcription Net (Encoder) is similar to an acoustic model in a traditional ASR systems. (2) Prediction Net (Decoder) can be regard as a language model. (3) Joint Net can combine the encoder outputs and

### torchaudio.prototype.rnnt_loss — Torchaudio master

2021-7-17 · The RNN Transducer loss (`Graves 2012

`__) extends the CTC loss by defining a distribution over output sequences of all lengths and by jointly modelling both input-output and output-output dependencies. ### End-to-End Deep Neural Network for Automatic Speech

2015-6-22 · CTC with RNN transducer method where a language model is added in conjunction with the CTC model. Using the embeddings or the probability distributions learned by the CNN we would then use a CTC loss layer to ﬁnally output the phone sequence. First we would like to describe the paradigm for decoding utilizing CTC loss in a RNN for decoding

### torchaudio.prototype.rnnt_loss — Torchaudio master

2021-7-17 · def rnnt_loss (logits Tensor targets Tensor logit_lengths Tensor target_lengths Tensor blank int =-1 clamp float =-1 fused_log_softmax bool = True reuse_logits_for_grads bool = True reduction str = "mean" ) """Compute the RNN Transducer loss from Sequence Transduction with Recurrent Neural Networks footcite `graves2012sequence` . The RNN Transducer loss extends the

### Exploring RNN-Transducer for Chinese speech recognition

2019-9-30 · RNN Transducer (RNN-T) has been recentlyproposed as an extension of the CTC model. Speciﬁcally byadding an LSTM based prediction network RNN-T removesthe conditional independence assumption in the CTC model.Moreover RNN-T does not need the entire utterance levelrepresentation before decoding which makes streaming end-to-end ASR possible. In Google has implemented the

### /RNN-Transducer

RNN Transducer MXNET GPU version of RNN Transducer loss is now available File description eval.py transducer decode model.py rnn transducer refer to Graves2012 DataLoader.py data process train.py rnnt training script can be initialized from CTC

### RNN-Transducer based Chinese Sign Language Recognition

2021-4-28 · The RNN-Transducer loss is defined with the negative log-likelihood of P (y x) (8) L RNN-T =-ln P (y x). To efficiently compute the probability P (y x) the forward–backward algorithm is applied. Due to the combination of video representation and language representation in a latent space the joint alignment strategy of RNN-Transducer

### EXPLORING PRE-TRAINING WITH ALIGNMENTS FOR RNN

2020-4-11 · loss CE loss < RNN -T model > Joint Network RNN -T loss Softmax 0"1 234 Fig. 1. RNN-Transducer model structure. 2. RNN TRANSDUCER MODEL The RNN-T model was proposed in 20 as an extension to the CTC model. A typical RNN-T model has three components as shown in the Figure 1 namely encoder prediction network and joint network.

### /RNN-Transducer

RNN Transducer MXNET GPU version of RNN Transducer loss is now available File description eval.py transducer decode model.py rnn transducer refer to Graves2012 DataLoader.py data process train.py rnnt training script can be initialized from CTC

### Baidu Research

2017-10-31 · The RNN-Transducer can be thought of as an encoder-decoder model which assumes the alignment between input and output tokens is local and monotonic. This makes the RNN-Transducer loss a better fit for speech recognition (especially when online) than attention-based Seq2Seq models by removing extra hacks applied to attentional models to

### cnblogs

2020-9-16 · TRANSFORMER TRANSDUCER A STREAMABLE SPEECH RECOGNITION MODELWITH TRANSFORMER ENCODERS AND RNN-T LOSS RNN-T transformerRNN

### IMPROVING RNN TRANSDUCER MODELING FOR END-TO

2019-10-13 · Fig. 1. Diagram of RNN-Transducer. 2. RNN-T Figure 1 shows the diagram of the RNN-T model which consists of encoder prediction and joint networks. The encoder network is analogous to the acoustic model which converts the acoustic feature x tinto a high-level representation henc where tis time index. henc t= f enc(x) (1)

### Transformer Transducer A Streamable Speech Recognition

2020-2-7 · This is similar to the Recurrent Neural Network Transducer (RNN-T) model which uses RNNs for information encoding instead of Transformer encoders. The model is trained with a monotonic RNN-T loss well-suited to frame-synchronous streaming decoding. We present results on the LibriSpeech dataset showing that limiting the left context for self

### /RNN-Transducer

RNN Transducer MXNET GPU version of RNN Transducer loss is now available File description eval.py transducer decode model.py rnn transducer refer to Graves2012 DataLoader.py data process train.py rnnt training script can be initialized from CTC

### /RNN-Transducer

### torchaudio.prototype.rnnt_loss — Torchaudio master

2021-7-13 · rnnt_loss. Compute the RNN Transducer Loss. The RNN Transducer loss ( Graves 2012) extends the CTC loss by defining a distribution over output sequences of all lengths and by jointly modelling both input-output and output-output dependencies. logits ( Tensor)Tensor of dimension (batch time target class) containing output from joiner.

### torchaudio.prototype.rnnt_loss — Torchaudio master

2021-7-13 · rnnt_loss. Compute the RNN Transducer Loss. The RNN Transducer loss ( Graves 2012) extends the CTC loss by defining a distribution over output sequences of all lengths and by jointly modelling both input-output and output-output dependencies. logits ( Tensor)Tensor of dimension (batch time target class) containing output from joiner.

### Minimum Bayes Risk Training of RNN-Transducer for End

2020-10-22 · Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition Chao Weng Chengzhu Yu Jia Cui Chunlei Zhang Dong Yu Tencent AI Lab Bellevue USA cweng tencent Abstract In this work we propose minimum Bayes risk (MBR) training of RNN-Transducer (RNN-T) for end-to-end speech recognition.

### RNN-Transducer LossGitHub

2020-10-23 · RNN-Transducer Loss. This package provides a implementation of Transducer Loss in TensorFlow==2.0. Using the pakage. First install the module using pip command.

### Exploring Pre-training with Alignments for RNN Transducer

2020-5-1 · Exploring Pre-training with Alignments for RNN Transducer based End-to-End Speech Recognition. 05/01/2020 ∙ by Hu Hu et al. ∙ 0 ∙ share . Recently the recurrent neural network transducer (RNN-T) architecture has become an emerging trend in end-to-end automatic speech recognition research due to its advantages of being capable for online streaming speech recognition.

### EXPLORING PRE-TRAINING WITH ALIGNMENTS FOR RNN

2020-4-11 · loss Softmax 0"1 234 Fig. 1. RNN-Transducer model structure. 2. RNN TRANSDUCER MODEL The RNN-T model was proposed in 20 as an extension to the CTC model. A typical RNN-T model has three components as shown in the Figure 1 namely encoder prediction network and joint network. Compared with CTC RNN-T does not have the conditional indepen-

### torchaudio.prototype.rnnt_loss — Torchaudio master

2021-7-17 · def rnnt_loss (logits Tensor targets Tensor logit_lengths Tensor target_lengths Tensor blank int =-1 clamp float =-1 fused_log_softmax bool = True reuse_logits_for_grads bool = True reduction str = "mean" ) """Compute the RNN Transducer loss from Sequence Transduction with Recurrent Neural Networks footcite `graves2012sequence` . The RNN Transducer loss extends the

### End-to-End Deep Neural Network for Automatic Speech

2015-6-22 · CTC with RNN transducer method where a language model is added in conjunction with the CTC model. Using the embeddings or the probability distributions learned by the CNN we would then use a CTC loss layer to ﬁnally output the phone sequence. First we would like to describe the paradigm for decoding utilizing CTC loss in a RNN for decoding

### Recurrent Neural Networks (RNN) with Keras TensorFlow Core

2021-3-25 · Unlike RNN layers which processes whole batches of input sequences the RNN cell only processes a single timestep. The cell is the inside of the for loop of a RNN layer. Wrapping a cell inside a keras.layers.RNN layer gives you a layer capable of processing batches of sequences e.g. RNN(LSTMCell(10)).

### Efﬁcient minimum word error rate training of RNN

2020-8-14 · based models 1 recurrent neural network transducer (RNN-T) 2 and attention-based seq2seq models 3 . Among these mod-els RNN-T is the most suitable streaming end-to-end recognizer which has shown competitive performance compared to conven-tional systems 4 5 . RNN-T models are typically trained with RNN-T loss which

### Exploring RNN-Transducer for Chinese speech recognition

2019-9-30 · RNN Transducer (RNN-T) 18 19 has been recently proposed as an extension of the CTC model. Speciﬁcally by adding an LSTM based prediction network RNN-T removes the conditional independence assumption in the CTC model. Moreover RNN-T does not need the entire utterance level representation before decoding which makes streaming end-

### cnblogs

### transformer

2020-9-16 · RNN-T transformer encoder transformerfeed-forward RNN-T . transformer encoder block blocklayer norm multi-head attention feed-forward networkresnet connection . blocklayer norm

### Exploring Pre-training with Alignments for RNN Transducer

2020-5-1 · Exploring Pre-training with Alignments for RNN Transducer based End-to-End Speech Recognition. 05/01/2020 ∙ by Hu Hu et al. ∙ 0 ∙ share . Recently the recurrent neural network transducer (RNN-T) architecture has become an emerging trend in end-to-end automatic speech recognition research due to its advantages of being capable for online streaming speech recognition.

### Symmetry Free Full-Text An Overview of End-to-End

The RNN-transducer which is a model structure has many similarities with CTC which is a loss function their goals are to solve the forced segmentation alignment problem in speech recognition they both introduce a "blank" label they both calculate the probability of all possible paths and aggregate them to get the label sequence.

### IMPROVING RNN TRANSDUCER MODELING FOR END-TO

2019-10-13 · Fig. 1. Diagram of RNN-Transducer. 2. RNN-T Figure 1 shows the diagram of the RNN-T model which consists of encoder prediction and joint networks. The encoder network is analogous to the acoustic model which converts the acoustic feature x tinto a high-level representation henc where tis time index. henc t= f enc(x) (1)

### EXPLORING PRE-TRAINING WITH ALIGNMENTS FOR RNN

2020-4-11 · loss Softmax 0"1 234 Fig. 1. RNN-Transducer model structure. 2. RNN TRANSDUCER MODEL The RNN-T model was proposed in 20 as an extension to the CTC model. A typical RNN-T model has three components as shown in the Figure 1 namely encoder prediction network and joint network. Compared with CTC RNN-T does not have the conditional indepen-

### Multitask Learning and Joint Optimization for Transformer

2020-11-2 · In this paper we propose multitask learning and joint optimization for the transformer-RNN-transducer ASR systems to overcome the limitations of conventional methods. Joint optimization with CTC loss on transcription network and LM loss on prediction