SpeechBrain Speed Perturbation Bug: Slow Is Fast!

Hey guys, let's dive into a peculiar bug we've spotted in SpeechBrain's speed perturbation augmentation. It's a bit of a head-scratcher, but once you get it, you'll see why it's important to address. So, let's get started!

The Curious Case of Inverted Speeds

So, here's the deal: When you're using SpeechBrain's speed perturbation feature, you might think that setting a speed change to, say, 90% would slow down your audio. But surprise! It actually speeds it up. Yes, you read that right. Slow becomes fast. The issue lies in how the code calculates the new sampling frequency. Instead of dividing the original frequency by the speed factor, it multiplies.

Let's break it down. Imagine you want to slow down your audio to 90% of its original speed. The code does something like this:

new_freq = orig_freq * speed // 100

So, if your original frequency (orig_freq) is 16000 Hz, and you set speed to 90, the new frequency (new_freq) becomes:

new_freq = 16000 * 90 // 100 = 14400

But wait, that's not how it's supposed to work! To slow down the audio, you should be dividing the original frequency by the speed factor. The correct calculation should be something like:

new_freq = orig_freq / (speed / 100)

This means that with speed = 90, you'd actually want:

new_freq = 16000 / (90 / 100) = 17777.78

See the difference? Multiplying makes the audio faster, while dividing makes it slower, which is what we want!

Why This Matters

Now, you might be thinking, "Okay, so it's a little backwards. What's the big deal?" Well, there are a couple of reasons why this is worth fixing.

1. Unexpected Behavior

First and foremost, it's just not intuitive. When you tell a program to slow something down to 90%, you expect it to slow down, not speed up. This can lead to confusion and frustration, especially for new users of SpeechBrain. Imagine spending hours debugging your code, only to realize that the speed perturbation is doing the opposite of what you intended. Not fun, right?

2. Non-Symmetric Speed Changes

The issue becomes even more critical when you're using non-symmetric speed change lists. By default, SpeechBrain often uses symmetric speed changes (e.g., 90, 100, 110). In this case, the model still sees both faster and slower versions of the audio during training, which can somewhat mitigate the problem. However, if you're specifically trying to slow down your audio for a particular reason, this bug will completely mess up your plans.

For example, let's say you're working on a speech recognition system for elderly people, who tend to speak more slowly. You might want to augment your training data by slowing down some of the audio samples. If you use SpeechBrain's speed perturbation with a value of 90, you'll actually be speeding up the audio, which is the opposite of what you want! This can lead to your model performing poorly on elderly speakers.

3. Reproducibility

Finally, it's important to fix this bug for the sake of reproducibility. If someone else tries to use your code with the same speed perturbation settings, they'll get different results than you intended. This can make it difficult to compare results and collaborate on projects. Fixing the bug ensures that everyone is on the same page and that the speed perturbation behaves as expected.

Diving into the Code

To really understand what's going on, let's take a closer look at the relevant code snippet from SpeechBrain:

def __init__(self, orig_freq, speeds=[90, 100, 110], device="cpu"):
 ...
 ...
 ...
 for speed in self.speeds:
 config = {
 "orig_freq": self.orig_freq,
 "new_freq": self.orig_freq * speed // 100,
 }
 ...

As you can see, the new_freq is calculated by multiplying orig_freq by speed // 100. This is where the inversion happens. To fix this, we need to change the calculation to divide orig_freq by speed / 100.

The Fix: A Simple Solution

Luckily, the fix for this bug is quite simple. We just need to modify the line of code that calculates the new frequency. Here's the corrected code:

def __init__(self, orig_freq, speeds=[90, 100, 110], device="cpu"):
 ...
 ...
 ...
 for speed in self.speeds:
 config = {
 "orig_freq": self.orig_freq,
 "new_freq": int(self.orig_freq / (speed / 100)),
 }
 ...

Notice that we've changed the calculation to self.orig_freq / (speed / 100). We've also added int() to ensure that the new frequency is an integer value. This simple change will ensure that the speed perturbation behaves as expected, slowing down the audio when you pass a value less than 100 and speeding it up when you pass a value greater than 100.

How to Implement the Fix

Now that we know what the bug is and how to fix it, let's talk about how to actually implement the fix in your SpeechBrain code. There are a couple of ways to do this.

1. Modify the SpeechBrain Source Code

The most straightforward way to fix the bug is to directly modify the SpeechBrain source code. This involves finding the relevant file (likely in the speechbrain/augment/speed.py directory) and making the changes we discussed above. However, this approach has a few drawbacks.

First, it means that you'll be modifying the SpeechBrain library directly, which can make it harder to update to future versions. Second, it can be difficult to track your changes and share them with others. Finally, if you're working in a team, it's generally not a good idea to modify shared libraries directly.

2. Create a Custom Speed Perturbation Class

A better approach is to create a custom speed perturbation class that inherits from SpeechBrain's original class. This allows you to override the buggy behavior without modifying the original code. Here's an example of how you can do this:

from speechbrain.augment import speed

class FixedSpeedPerturbation(speed.SpeedPerturb):
 def __init__(self, *args, **kwargs):
 super().__init__(*args, **kwargs)

 def _do_speed_perturb(self, speeds):
 new_freqs = [int(self.orig_freq / (speed / 100)) for speed in speeds]
 return new_freqs

In this example, we're creating a new class called FixedSpeedPerturbation that inherits from speed.SpeedPerturb. We then override the _do_speed_perturb method to use the corrected frequency calculation. Now, you can use this class instead of the original SpeedPerturb class in your code.

3. Monkey Patching (Use with Caution)

Another approach, though generally discouraged for production code, is monkey patching. This involves dynamically modifying the original class at runtime. Here's how you can do it:

from speechbrain.augment import speed

def fixed_speed_perturb(self, speeds):
 new_freqs = [int(self.orig_freq / (speed / 100)) for speed in speeds]
 return new_freqs

speed.SpeedPerturb._do_speed_perturb = fixed_speed_perturb

In this example, we're defining a new function called fixed_speed_perturb that uses the corrected frequency calculation. We then assign this function to the _do_speed_perturb method of the SpeedPerturb class. This will effectively replace the original method with our corrected version. Use this approach with caution, as it can lead to unexpected behavior if not done carefully.

Wrapping Up

So, there you have it! A deep dive into the curious case of inverted speeds in SpeechBrain's speed perturbation augmentation. We've explored the bug, understood why it matters, and discussed several ways to fix it. Remember, a small change in code can make a big difference in your results. By addressing this issue, you can ensure that your speed perturbation behaves as expected, leading to more accurate and reliable speech processing systems.

Now go forth and perturb those speeds with confidence! And remember, slow should be slow, and fast should be fast. Happy coding, everyone!