python代码实现将PDF文件转为文本及其对应的音频

代码地址：

https://github.com/TiffinTech/python-pdf-audo

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import pyttsx3,PyPDF2

#insert name of your pdf
pdfreader = PyPDF2.PdfReader(open('book.pdf', 'rb'))
speaker = pyttsx3.init()

for page_num in range(len(pdfreader.pages)):
    text = pdfreader.pages[page_num].extract_text()
    clean_text = text.strip().replace('\n', ' ')
    print(clean_text)
#name mp3 file whatever you would like
speaker.save_to_file(clean_text, 'story.mp3')
speaker.runAndWait()

speaker.stop()

首先说下PDF文字提取的功能，大概还是可以凑合的，给出Demo：

提取的文字为：

Safe and efficient off-policy reinforcement learning R´emi Munos munos@google.com Google DeepMindThomas Stepleton stepleton@google.com Google DeepMind Anna Harutyunyan anna.harutyunyan@vub.ac.be Vrije Universiteit BrusselMarc G. Bellemare bellemare@google.com Google DeepMind Abstract In this work, we take a fresh look at some old and new algorithms for off-policy, return-based reinforcement learning. Expressing
these in a common form, we de- rive a novel algorithm, Retrace(λ), with three desired properties: (1) it haslow variance; (2) itsafelyuses samples collected from any behaviour policy, whatever its degree of
“off-policyness”; and (3) it isefficientas it makes the best use of sam- ples collected from near on-policy behaviour policies. We analyze the contractive nature of the related operator under both off-policy
policy evaluation and control settings and derive online sample-based algorithms. We believe this is thefirst return-based off-policy control algorithm converging a.s. toQ∗without the GLIE assumption (Greedy
in the Limit with Infinite Exploration). As a corollary, we prove the convergence of Watkins’ Q(λ), which was an open problem since 1989. We illustrate the benefits of Retrace(λ) on a standard suite of Atari 2600 games. One fundamental trade-off in reinforcement learning lies in the definition of the update target: should one estimate Monte Carlo returns or bootstrap from an existing Q-function? Return-based meth- ods (wherereturnrefers to the sum of discounted rewards� tγtrt) offer some advantages over value bootstrap methods: they are better behaved when combined with function approximation, and quickly propagate the fruits of exploration (Sutton, 1996). On the other hand, value bootstrap meth- ods are more readily applied to off-policy data, a common use case. In this paper we show that learning from returns need not be at cross-purposes with off-policy learning. We start from the recent work of Harutyunyan et al. (2016), who show that naive off-policy policy evaluation, without correcting for the “off-policyness” of a
trajectory, still converges to the desired Qπvalue function provided the behaviorµand targetπpolicies are not too far apart (the maxi- mum allowed distance depends on theλparameter). TheirQπ(λ)algorithm learns from trajectories generated byµsimply by summing discounted off-policy corrected rewards at each time step. Un- fortunately, the assumption thatµandπare close is restrictive, as well as difficult to uphold in the control case, where the target policy is greedy with respect to the current Q-function. In that sense this algorithm is notsafe: it does not handle the case of arbitrary “off-policyness”. Alternatively, the Tree-backup (TB(λ)) algorithm (Precup et al., 2000) tolerates arbitrary tar- get/behavior discrepancies by scaling information (here calledtraces) from future temporal dif- ferences by the product of target policy probabilities. TB(λ) is notefficientin the “near on-policy” case (similarµandπ), though, as traces may be cut prematurely, blocking learning from full returns. In this work, we express several
off-policy, return-based algorithms in a common form. From this we derive an improved algorithm, Retrace(λ), which is bothsafeandefficient, enjoying convergence guarantees for off-policy policy evaluation and – more importantly – for the control setting. 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain.

上面这些这就是文字提取的效果，而对于音频转换这部分就效果实在是糟糕的很，转换的音频是无法贴合原文的，因此这里认为上面代码中这个PDF文字提取功能还是可以勉强用的，为以后项目需要做一定的技术积累，而这个音频转换就无法考虑使用了。

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对应的视频：

https://www.youtube.com/watch?v=LXsdt6RMNfY