Batch-converting audio files to be louder (on Linux)

My mp3 player is very quiet, so I wanted to make all my podcasts as loud as possible.

First I ran this to get the programs I needed:

sudo apt-get install libav-tools normalize-audio

To convert each file I made a script that makes a “loud” directory, and puts the loud version of a file inside there. It uses the normalize-audio command to do it.

Note that this script encodes your (now louder) podcasts into Ogg Vorbis format at 50kb/s, which is quite low quality.


set -e
set -u

DIR=`dirname "$FILE"`

FILENAME=`basename "$FILE"`


mkdir -p "$LOUD_DIR"

avconv -loglevel quiet -i "$FILE" "$WAV_FILE"
normalize-audio -q -a 1 "$WAV_FILE"
avconv -loglevel quiet -i "$WAV_FILE" -c:a libvorbis -b:a 50k "$LOUD_FILE"
rm "$WAV_FILE"

Finally I placed a Makefile in the directory containing podcasts directories, like this:

MP3S := $(wildcard *.mp3)
LOUD_MP3S := $(MP3S:%.mp3=loud/%.mp3)

OGGS := $(wildcard *.ogg)
LOUD_OGGS := $(OGGS:%.ogg=loud/%.ogg)

all: $(LOUD_OGGS) $(LOUD_MP3S)

loud/%.ogg: %.ogg
	loud "$<"

loud/%.mp3: %.mp3
	loud "$<"

Now I can make loud versions of all podcasts by just cding into the directory containing the Makefile, and typing make. By the power of make, it only converts files that have not already been converted.

Tail Call Optimisation in C++ – lightning talk video

You can watch the Tail Call Optimisation in C++ lightning talk video, which I gave at the ACCU 2012 conference in April.

You can also read the (clearer and more correct) writeup I did later: Tail Call Optimisation in C++ or the subsequent article published in Overload 109.

Scheme 6: Lambda video

Series: Feel the cool, Basics, Closures, Recursion, Quotation, Lambda, Macros.

Continuing the series on Scheme, this video explains the lambda function, which allows you to define anonymous functions. It goes on to bend your mind with 2 examples of the enormous power of functions and closures in Scheme.

Slides for Scheme 6: Lambda

Anatomy of an interpreter: the Parser

Posts in this series: Lexer, Parser, Evaluator

Subs has reached version 1.3.4, which means that it can successfully run all the tests from chapter 1 of SICP. This is very exciting.

Last time I explained a bit about the Lexer, which takes in a stream of characters and emits a stream of tokens: individual elements of code such as a bracket, a keyword or a symbol.

Generally, parsers emit some kind of tree structure – they understand the raw tokens as a hierarchical structure which (conceptually, at least) will be executed from the bottom up, with each branch-point in the tree being an operation of some kind.

Our parser takes in a stream of tokens, and emits a stream of parsed trees.

Parsing Scheme is very easy, because (except for a couple of exceptions I haven’t implemented yet) there is essentially one rule: start with an open bracket, see a list of things, and then find a close bracket. Of course, one of the “things” you see may itself be another bracketted list, so after parsing you get a tree structure of nested lists.

The parser in Subs looks like this:

class Parser
    Parser( ILexer& lexer );
    std::auto_ptr<Value> NextValue();
    ILexer& lexer_;

We supply a Lexer in the constructor, which we know will provide us with tokens when we need them via its NextToken() method. The Parser’s NextValue() method returns a pointer to a Value, which is the base class for all the “things” in the Subs interpreter.

There are lots of types of things that inherit from the Value class, but the “parse tree” (the output of the parser) will only consist of a very small subset of them:

  • CombinationValue
  • DecimalValue
  • IntegerValue
  • StringValue
  • SymbolValue

The CombinationValue class forms the tree structure. Its declaration looks like this:

class CombinationValue : public Value, public std::vector<Value*>
    // ...

It is simply a list of other Values.

Note that it “owns” those Values in the sense that it deletes them when it is deleted. I have recently made the jump to make Subs depend on BOOST, so it’s on my TODO list to make containers like this use the BOOST smart containers to manage this job for me.

DecimalValue, IntegerValue and StringValue are relatively self-explanatory: they contain numbers and strings that were found as literals in the source code.

SymbolValue is essentially everything else – if the code that recognises the type of a token can’t recognise it as a bracket, a number or a string, we assume it is a symbol, and tuck it away in a SymbolValue to be understood later.

The core of the Parser looks like this (with some error-checking removed):

std::auto_ptr<Value> next_value( ILexer& lexer, Token token )
    if( token.Name() == "(" )
        auto_ptr<CombinationValue> ret( new CombinationValue );
        while( true )
            token = lexer.NextToken();
            if( token.Name() == ")" )
            // Recursive call
            ret->push_back( next_value( lexer, token ).release() );
        return auto_ptr<Value>( ret.release() );
        return ValueFactory::CreateValue( token );

(Full code here: Parser.cpp) It’s a simple recursive function that creates a CombinationValue whenever it finds a bracket, and otherwise uses a ValueFactory to create an individual value.

Side note: the wisdom of using recursion could certainly be questioned, since it limits the depth of bracketting we can handle to the size of the C++ stack, but the only other way to get the same result would be to keep our own manual stack of unfinished combinations, and it just seems perverse to re-implement language features like that. What might well be more interesting would be to consider whether we can actually evaluate parts of the tree as we go, without parsing it all at once. This might make the whole setup scale rather better, but would most likely be quite complex. The implementation presented here will work fine for almost any imaginable program – remember we would need not just code whose execution is deeply nested, but whose expression in code had thousands of levels of nesting before the parser would fail.

The ValueFactory uses some basic rules such as “starts and ends with a quote” or “consists of only numbers and a decimal point” to recognise what type of Value to create, and if no rules match it defaults to a SymbolValue.

When we have completed a bracketted expression, we return a complete tree of numbers, strings and symbols, and it is ready to be evaluated, which you can think of as simply expanding the tree we already have into the full expression of itself, and then reducing it back down again to an answer.

Next time, the Evaluator and the famous eval-apply loop.

Don’t design for performance until it’s too late

There is a piece of ancient wisdom which states:

Premature optimisation is the root of all evil

This ancient wisdom is, like all ancient wisdom, correct.


It appears to have been reinterpreted as essentially meaning:

Don’t design for performance until it’s too late

which is clearly, and very importantly, very wrong.

Performance is a feature

Before I begin I want us all to agree that performance is a feature.

I work on a real-life "enterprise" application. Its features are entirely driven by the need for immediate cash, not by developers following pipe dreams. And yet, for the last 6-12 months the majority of my time has been spent trying to retrofit performance into this application. Believe me, this is not because we have users who are obsessive about wasting valuable seconds – it’s because our performance sucks so hard it’s deeply embarrassing.

What is your favourite program? How well does it perform? What is your least favourite? Why?

For me, and many other people, their answers to those questions demonstrate the importance of performance. Firefox was launched to improve the performance of Mozilla. People love git because of how fast it is. Lotus Notes is hated so much partly because of its performance. My main complaints about programs I use involve performance (e.g. Thunderbird is too slow for IMAP email.)

A fast response to the user is one of those crucial inches on the journey to software that makes people happy. Making people happy gives you the kind of scary fanboyism that surrounds git. Wouldn’t you like that for your product?

What is optimisation?

When my hero said that premature optimisation was the root of all evil, he was talking in the days when you had to hand-optimise your C in assembly language. Or, more likely in his case, you had to hand-optimise your assembly language into faster assembly language. Optimisation like that very often obfuscates your code.

These days, 99% of the time, your compiler does all of this work for you, so you can have relatively comprehensible code in whatever trendy language you like, and still have the fastest possible local implementation of that in machine code.

Meanwhile, Knuth knew that 99% of your code is simply not performance-critical – it only runs a few times, or it’s just so much faster than some other bit that it doesn’t matter. The lesson we all learn eventually is that the slow bit is never quite what you thought, and you have to measure to find out where to concentrate your effort.

So, if optimisation is obfuscation, and 99% of your code isn’t the bit you need to make faster, it becomes clear that premature optimisation is the root of much evil.

But optimisation is not designing for performance.

Design for performance

Fundamentally, to get good performance, you are going to need to measure the time spent in various parts of your code (I suggest Very Sleepy if you’re on Windows) and make the slow bits faster (or happen less often). However, there are still some principles you can follow that will mean you need to spend less time doing this*.

*Which is a pity, because I really love doing it.

If you don’t design for performance you are almost certainly going to need to restructure large parts of your program later, which is very difficult and time-consuming.

There are two aspects to designing for performance: writing good local code, and creating good global structure.

Write good local code

Before you write an algorithm, think for a few minutes about how to make it work efficiently. e.g. if you’re writing C++, consider whether a deque or a list would be better than a vector for how you’re going to use it.

Think about what is idiomatic for your language and why. Think about what the computer really has to do to produce the results you are asking for. Are there going to be a lot of objects about? Maybe you can avoid copying them too many times. Are you creating and deleting a lot of objects? Can you reuse some instead? (Exercise caution with that one, though – if you start obfuscating you come into conflict with the ancient wisdom.)

Often, if you think through what you are doing, and the most efficient way to do it, you will end up with a faster and more memory-efficient algorithm, that expresses your intention better than if you’d written the first thing that came into your head. There is no downside to that.

Try to minimise the number of times you need to ask the operating system for a chunk of memory: this is surprisingly slow. E.g. in C++, prefer creating by-value data members instead of pointers to objects allocated with their own call to new.

By the way, don’t worry if this sounds intimidating. The way to learn this stuff is to measure what you have done and then work out why it is slow. Next time you’ll jump straight to the fast solution without the detour to the slow one.

Of course, none of this will matter if you don’t have good global structure.

Create good global structure

The hardest and most important work you need to do to have good performance is to have good structure in the ways the different parts of your program interact.

This means thinking about how classes and components communicate with and control each other.

It may be helpful to use a streaming style of communication – can you send little chunks of information to be processed one by one instead of a huge great blob?

Try to make sure your components to use a common infrastructure: if different parts use different string classes you are going to spend most of your time copying from one to the other.

The hardest and deepest mystery in getting good performance (and in programming generally) is choosing the right fundamental data structures. I’ll never forget the lesson I learnt** when a friend of mine had a conversation with me about a toy project I was doing (that was particularly focussed on trying to be fast) and then went away and produced code that was orders of magnitude faster, simply because he had chosen the right data structure.

**The lesson I learnt was that I am not as good as I think I am.

To be honest this section is a little shorter than I’d like because I know I don’t have a lot of answers about how to do this well. I do know, though, that if you don’t think about it now you will have the pain of restructuring your program later, when it’s full of bug fixes that are going to get rebroken by the restructuring.

Of course, if you do think about it now you’re still pretty likely to need to change it later…

Ancient wisdom

Ancient wisdom is usually right, but misinterpreting it and using it as a license to write bad code is a bad idea.

Carry on.