PyTorch-Kaldi：融合深度学习与高效语音识别的工具包

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PyTorch-Kaldi 语音识别工具包是一个新兴且颇具潜力的跨平台工具，旨在将两个业界知名的开源软件——Kaldi与PyTorch——的优势相结合。Kaldi作为语音识别领域的基石框架，一直以来都是开发高性能语音识别系统的首选，其效率被广泛认可。而PyTorch，则凭借其Python编程语言的易用性和灵活性，在深度学习社区中迅速崛起，深受开发者喜爱。 PyTorch-Kaldi项目的核心目标是打破这两者之间的界限，继承Kaldi的高效性，并融入PyTorch的灵活性。它不仅提供了Kaldi和PyTorch之间无缝的接口，使得开发者可以方便地在两者之间切换，而且内置了一系列实用功能，特别适合于构建现代的语音识别系统。这个工具包的一大亮点是其设计灵活性，允许用户轻松地定制自己的声学模型，满足个性化需求。此外，PyTorch-Kaldi还支持预实现的神经网络，用户可以通过直观的方式对其进行调整和优化，无需从头开始编写复杂的代码。使用PyTorch-Kaldi，开发者能够享受以下优势： 1. **兼容性与灵活性**：通过整合PyTorch的动态计算图和Kaldi的高效执行引擎，用户可以在保持模型灵活性的同时，享受高效的训练和推理过程。 2. **自定义能力**：允许用户设计和实现自己的声学模型，这对于研究新颖的模型结构和算法至关重要。 3. **易于集成**：无论是对于已经熟悉Kaldi的团队，还是希望利用PyTorch快速原型设计的开发者，PyTorch-Kaldi都提供了便捷的工具链。 4. **预训练模型库**：通过现成的神经网络模型，减少了模型开发的初期投入，加快了研究进程。 5. **学习曲线平滑**：PyTorch-Kaldi的设计考虑到了初学者的需求，通过直观的API和文档，降低了学习和使用的门槛。 PyTorch-Kaldi是一个值得深入探索的工具包，它正在推动语音识别技术的发展，尤其在结合深度学习的场景下，极大地提高了研究者和工程师的工作效率。

THE PYTORCH-KALDI SPEECH RECOGNITION TOOLKIT

Mirco Ravanelli

, Titouan Parcollet

, Yoshua Bengio

1∗

Mila, Universit

e de Montr

eal ,

∗

CIFAR Fellow

LIA, Universit

e d’Avignon

ABSTRACT

The availability of open-source software is playing a remarkable role

in the popularization of speech recognition and deep learning. Kaldi,

for instance, is nowadays an established framework used to develop

state-of-the-art speech recognizers. PyTorch is used to build neural

networks with the Python language and has recently spawn tremen-

dous interest within the machine learning community thanks to its

simplicity and ﬂexibility.

The PyTorch-Kaldi project aims to bridge the gap between these

popular toolkits, trying to inherit the efﬁciency of Kaldi and the

ﬂexibility of PyTorch. PyTorch-Kaldi is not only a simple inter-

face between these software, but it embeds several useful features

for developing modern speech recognizers. For instance, the code is

speciﬁcally designed to naturally plug-in user-deﬁned acoustic mod-

els. As an alternative, users can exploit several pre-implemented

neural networks that can be customized using intuitive conﬁguration

ﬁles. PyTorch-Kaldi supports multiple feature and label streams as

well as combinations of neural networks, enabling the use of com-

plex neural architectures. The toolkit is publicly-released along with

a rich documentation and is designed to properly work locally or on

HPC clusters.

Experiments, that are conducted on several datasets and tasks,

show that PyTorch-Kaldi can effectively be used to develop modern

state-of-the-art speech recognizers.

Index Terms: speech recognition, deep learning, Kaldi, PyTorch.

1. INTRODUCTION

Over the last years, we witnessed a progressive improvement and

maturation of Automatic Speech Recognition (ASR) technologies

[1, 2], that have reached unprecedented performance levels and are

nowadays used by millions of users worldwide.

A key role in this technological breakthrough is being played by

deep learning [3], that contributed to overcoming previous speech

recognizers based on Gaussian Mixture Models (GMMs). Beyond

deep learning, other factors have played a role in the progress of

the ﬁeld. A number of speech-related projects such as AMI [4],

DICIT [5], DIRHA [6] and speech recognition challenges such

as CHiME [7], Babel, and Aspire, have remarkably fostered the

progress in ASR. The public distribution of large datasets such

as Librispeech [8] has also played an important role to establish

common evaluation frameworks and tasks.

Among the others factors, the development of open-source soft-

ware such as HTK [9], Julius [10], CMU-Sphinx, RWTH-ASR [11],

LIA-ASR [12] and, more recently, the Kaldi toolkit [13] have further

helped popularize ASR, making both research and development of

novel ASR applications signiﬁcantly easier.

Kaldi currently represents the most popular ASR toolkit. It re-

lies on ﬁnite-state transducers (FSTs) [14] and provides a set of C++

libraries for efﬁciently implementing state-of-the-art speech recogni-

tion systems. Moreover, the toolkit includes a large set of recipes that

cover all the most popular speech corpora. In parallel to the devel-

opment of this ASR-speciﬁc software, several general-purpose deep

learning frameworks, such as Theano [15], TensorFlow [16], and

CNTK [17], have gained popularity in the machine learning com-

munity. These toolkits offer a huge ﬂexibility in the neural network

design and can be used for a variety of deep learning applications.

PyTorch [18] is an emerging python package that implements ef-

ﬁcient GPU-based tensor computations and facilitates the design of

neural architectures, thanks to proper routines for automatic gradi-

ent computation. An interesting feature of PyTorch lies in its modern

and ﬂexible design, that naturally supports dynamic neural networks.

In fact, the computational graph is dynamically constructed on-the-

ﬂy at running time rather than being statically compiled.

The PyTorch-Kaldi project aims to bridge the gap between Kaldi

and PyTorch

. Our toolkit implements acoustic models in PyTorch,

while feature extraction, label/alignment computation, and decod-

ing are performed with the Kaldi toolkit, making it a perfect ﬁt to

develop state-of-the-art DNN-HMM speech recognizers. PyTorch-

Kaldi natively supports several DNNs, CNNs, and RNNs models.

Combinations between deep learning models, acoustic features, and

labels are also supported, enabling the use of complex neural archi-

tectures. For instance, users can employ a cascade between CNNs,

LSTMs, and DNNs, or run in parallel several models that share some

hidden layers. Users can also explore different acoustic features,

context duration, neuron activations (e.g., ReLU, leaky ReLU), nor-

malizations (e.g., batch [19] and layer normalization [20]), cost func-

tions, regularization strategies (e.g, L2, dropout [21]), optimization

algorithms (e.g., SGD, Adam [22], RMSPROP), and many other

hyper-parameters of an ASR system through simple edits of a con-

ﬁguration ﬁle.

The toolkit is designed to make the integration of user-deﬁned

acoustic models as simple as possible. In practice, users can em-

bed their deep learning model and conduct ASR experiments even

without being fully familiar with the complex speech recognition

pipeline. The toolkit can perform computations on both local ma-

chines and HPC cluster, and supports multi-gpu training, recovery

strategy, and automatic data chunking.

The experiments, conducted on several datasets and tasks, have

shown that PyTorch-Kaldi makes it possible to easily develop com-

petitive state-of-the-art speech recognition systems.

2. THE PYTORCH-KALDI PROJECT

An overview of the architecture adopted in PyTorch-Kaldi is re-

ported in Fig. 1. The main script run exp.py is written in python

The code is available on GitHub (github.com/mravanelli/

PyTorch-kaldi/).

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