[P] Quick Debugging of Audio Machine Learning Models: Boosting Efficiency and Accuracy

**Fast debugging of audio machine learning models**

I have recently made improvements to a library I created to support the identification of issues in audio data using audio embeddings. This article presents a fast and effective approach to debugging audio machine learning models.

**Exploring audio data through audio embeddings**

The first step in the debugging process is to compute audio embeddings for the raw data. This allows the data to be explored based on audio similarity. The nature of the model being used will affect how similarity is defined. For example, if the model is intended for speaker identification, the data can be ordered according to the distinctive voice properties of different speakers.

**Identifying problematic data slices using clustering**

To identify problematic data slices, clustering can be employed. The samples are clustered based on their audio embeddings, providing explicit suggestions for problematic data slices. Evaluation metrics are then computed for each identified cluster. By comparing the accuracy of the clusters to the overall accuracy, clusters that demonstrate a significant drop in accuracy can be found.

**Reviewing and resolving audio issues visually**

Reviewing the supposed issues visually is an important step in the debugging process. This is particularly crucial when dealing with unstructured data. Although the results of the analysis may not be readily interpretable, listening to the audio and visualizing the data (e.g., by drawing spectrograms) can help identify actual model and data issues.

**Additional resources and examples**

For a more comprehensive explanation of this debugging approach, please refer to my Medium post and example notebook available at the following links:

– [Medium Post](https://medium.com/@daniel-klitzke/fast-audio-machine-learning-model-debugging-using-embedding-space-clustering-1fc1ca232592)
– [Example Notebook](https://github.com/Renumics/sliceguard/blob/main/examples/audio_issues_commonvoice_whisper.ipynb)

I have also created an interactive result visualization on Huggingface, which can be accessed here: [Interactive Result Visualization on Huggingface](https://huggingface.co/spaces/renumics/whisper-commonvoice-speaker-issues)

In summary, the process of debugging audio machine learning models can be greatly expedited by using audio embeddings, clustering techniques, and visual inspection. This approach allows for more accurate identification of problematic data slices and enables the resolution of model and data issues.