Efficient and Accurate Streaming Automatic Speech Recognition with Stateful Conformer and Cache-based Inference

Konsep Inti

An efficient and accurate streaming speech recognition model based on the FastConformer architecture, with a cache-based inference mechanism and a hybrid CTC/RNNT architecture to boost accuracy and speed up convergence.

Abstrak

The paper proposes an efficient and accurate streaming speech recognition model based on the FastConformer architecture. The key aspects of the proposed approach are:

Constraining the look-ahead and past contexts in the encoder to maintain consistent behavior during training and streaming inference.
Introducing an activation caching mechanism to enable the non-autoregressive encoder to operate autoregressively during inference, eliminating the need for buffering and reducing computation.
Utilizing a hybrid CTC/RNNT architecture with a shared encoder, which not only saves computation but also improves the accuracy and speeds up the convergence of the CTC decoder.

The proposed model is evaluated on the LibriSpeech dataset and a large multi-domain dataset. The results show that the cache-aware streaming model outperforms the buffered streaming approach in terms of accuracy, latency, and inference time. The experiments also demonstrate that training a model with multiple latencies can achieve better accuracy than single latency models, while enabling support for multiple latencies with a single model.

Statistik

The offline FastConformer-CTC model achieves a WER of 5.7% on the LibriSpeech test-other set.
The buffered streaming FastConformer-CTC model has a WER of 8.0% with an average latency of 1500ms.
The cache-aware streaming FastConformer-CTC model with chunk-aware look-ahead has a WER of 7.1% with an average latency of 1360ms.
The cache-aware streaming FastConformer-T model with chunk-aware look-ahead has a WER of 6.3% with an average latency of 1360ms.

Kutipan

"We propose an efficient and accurate streaming speech recognition model based on the FastConformer architecture."
"We introduced a hybrid CTC/RNNT architecture which utilizes a shared encoder with both a CTC and RNNT decoder to boost the accuracy and save computation."
"Our experiments also showed the hybrid architecture would not only speedup the convergence of the CTC decoder but also improves the accuracy of streaming models compared to single decoder models."

Wawasan Utama Disaring Dari

Stateful Conformer with Cache-based Inference for Streaming Automatic Speech Recognition

by Vahid Norooz... pada arxiv.org 05-06-2024

https://arxiv.org/pdf/2312.17279.pdf

Pertanyaan yang Lebih Dalam

How can the proposed cache-aware streaming approach be extended to other types of neural network architectures beyond FastConformer

The proposed cache-aware streaming approach can be extended to other types of neural network architectures by adapting the caching mechanism to suit the specific requirements of each architecture. For instance, in architectures with recurrent layers, such as LSTMs or GRUs, the cache can store the hidden states of the recurrent layers to facilitate autoregressive inference. Similarly, in convolutional neural network (CNN) architectures, the cache can store intermediate feature maps to reduce redundant computations. The key is to identify the components of the architecture that require context information and design the caching mechanism accordingly. By customizing the cache for different architectures, the benefits of reduced computation and improved efficiency can be extended to a variety of neural network models.

What are the potential challenges and limitations of the cache-based inference mechanism, and how can they be addressed

While the cache-based inference mechanism offers significant advantages in terms of reducing computation and improving efficiency, there are potential challenges and limitations that need to be addressed. One challenge is the management of cache size, especially in models with a large number of layers or parameters. As the cache grows, it can consume significant memory resources, potentially leading to memory constraints. This challenge can be addressed by implementing efficient cache management strategies, such as prioritizing important activations or implementing dynamic cache resizing based on memory availability.
Another limitation is the potential for stale information in the cache, especially in dynamic environments where the context may change rapidly. Stale information can lead to inaccuracies in predictions and degrade model performance. To mitigate this limitation, periodic cache refreshing or updating mechanisms can be implemented to ensure that the information stored in the cache remains relevant and up-to-date. Additionally, incorporating mechanisms for adaptive caching, where the model dynamically adjusts the cache content based on the input data, can help improve the accuracy of the inference process.
Furthermore, the cache-based approach may introduce additional complexity in model training and inference pipelines, requiring careful implementation and optimization to ensure seamless integration with existing systems. Robust error handling mechanisms and thorough testing procedures are essential to identify and address any issues that may arise due to the caching mechanism. By proactively addressing these challenges and limitations, the cache-based inference mechanism can be effectively leveraged to enhance the performance of streaming speech recognition models.

How can the hybrid CTC/RNNT architecture be further improved to achieve even better accuracy and efficiency for streaming speech recognition

To further improve the hybrid CTC/RNNT architecture for streaming speech recognition, several strategies can be implemented:

Dynamic Loss Balancing: Instead of using a fixed hyperparameter α to balance the CTC and RNNT losses, dynamic loss balancing techniques can be employed. Adaptive algorithms that adjust the weight between the two losses based on model performance during training can help optimize the overall loss function and improve convergence speed.

Enhanced Decoder Interaction: Explore ways to enhance the interaction between the CTC and RNNT decoders in the hybrid architecture. Techniques such as joint training with shared encoder representations, feedback mechanisms between the decoders, or incorporating cross-decoder attention mechanisms can improve the synergy between the decoders and lead to better accuracy.

Regularization and Fine-tuning: Implement regularization techniques such as dropout, weight decay, or label smoothing to prevent overfitting and improve generalization. Fine-tuning the hybrid architecture on domain-specific data or utilizing transfer learning from pre-trained models can further enhance accuracy and efficiency.

Optimized Hyperparameters: Conduct thorough hyperparameter tuning experiments to identify the optimal settings for the hybrid architecture. Parameters such as learning rate schedules, batch sizes, and optimizer configurations can significantly impact model performance and convergence speed.

Ensemble Methods: Explore ensemble learning techniques by combining multiple hybrid models with diverse architectures or training strategies. Ensemble methods can help mitigate individual model weaknesses and improve overall accuracy by leveraging the diversity of the ensemble members.

By incorporating these advanced strategies and optimizations, the hybrid CTC/RNNT architecture can be further refined to achieve superior accuracy, efficiency, and robustness in streaming speech recognition tasks.