Machine Learning Solution

Machine Learning HW4 Speaker Identification


Outline
● Task Description
● Dataset
● Data Segmentation ● Model Architecture
● Baselines
● Report
● Guidelines
Task Introduction
● Self-attention
○ Proposed in GOOGLE's work, Attention is all you need. It combines the strengths of RNN (consider the whole sequence) and CNN (processing parallelly).
● Goal: Learn how to use Transformer.
Ref. Prof. Hung-Yi Lee
[2021Spring ML] Transformer (1), (2)
Speaker Identification
Task: Multiclass Classification
Predict speaker class from given speech.

Dataset - VoxCeleb2
● Training: 56666 processed audio features with labels.
● Testing: 4000 processed audio features (public & private) without labels. ● Label: 600 classes in total, each class represents a speaker.
VoxCeleb2: Link
Data Preprocessing
Ref. Prof. Hung-Yi Lee
[2020Spring DLHLP] Speech
Recognition
Data Format
● Data Directory
○ metadata.json
○ testdata.json
○ mapping.json ○ uttr-{random string}.pt
● The information in metadata
○ "n_mels": The dimention of mel-spectrogram. ○ "speakers": A dictionary.
■ Key: speaker ids
■ Value: "feature_path" and "mel_len"
Data Segmentation During Training
Different length:
Data Segmentation During Training
Different length:
Segment during training
Segment = 2

Sample Code
● Link
● Baseline Methods
○ Simple: Run sample code & know how to use Transformer.
○ Medium: Know how to adjust parameters of Transformer.
○ Strong: Construct Conformer, which is a variety of Transformer.
○ Boss: Implement Self-Attention Pooling & Additive Margin Softmax to further boost the performance.
Requirements - Simple
● Build a self-attention network to classify speakers with sample code.
● Simple public baseline: 0.60824
● Estimate training time: 30~40 mins on Colab.
Requirements - Medium
● Modify the parameters of the transformer modules in the sample code.
● Medium public baseline: 0.70375

Requirements - Strong
● Construct Conformer, which is a variety of Transformer. ● Strong public baseline: 0.77750


Requirements - Boss
● Implement Self-Attention Pooling & Additive Margin Softmax to further boost the performance.
● Public boss baseline: 0.86500
● Estimate training time: about 2~2.5 hours on Kaggle.
Hints
● Self-Attention Pooling
Hints ● Additive Margin Softmax

Grading
● Evalute Metric: @1 Accuracy
● Simple Baseline (Public / Private) +0.5 pt / 0.5 pt
● Medium Baseline (Public / Private) +0.5 pt / 0.5 pt
● Strong Baseline (Public / Private) +0.5 pt / 0.5 pt
● Boss Baseline (Public / Private) +0.5 pt / 0.5 pt
● Code Submission +2 pts
● Report +4 pts
Submission Format
● "Id, Category" split by ',' in the first row.
● Followed by 8000 lines of "filename, speaker name" split by ','.

Code Submission
● Submit your code to NTU COOL (2 pts).
○ We can only see your last submission.
○ Do NOT submit the model or dataset.
○ If your codes are not reasonable, your final grade will be x 0.9 ○ You should compress your code into a single zip file:
■ ex. b08902126_hw4.zip
<Student ID>_hw4.zip
Report (4 pts)
1. Make a brief introduction about a variant of Transformer. (2 pts)
2. Briefly explain why adding convolutional layers to Transformer can boost performance. (2 pts)
Links
● Kaggle: link
● Colab: link
● Data: link (請參照 sample code 之下載方式)
● Dataset: link
Regulation
● You should NOT plagiarize, if you use any other resource, you should cite it in the reference. (＊)
● You should NOT modify your prediction files manually.
● Do NOT share codes or prediction files with any living creatures.
● Do NOT use any approaches to submit your results more than 5 times a day.
● Do NOT search or use additional data or pre-trained models.
● Your final grade x 0.9 if you violate any of the above rules.
● Prof. Lee & TAs preserve the rights to change the rules & grades.
(＊) Academic Ethics Guidelines for
Researchers by the Ministry of Science and
Technology (MOST)
If any questions, you can ask us via...
● NTU COOL (Recommended)
○ https://cool.ntu.edu.tw/courses/11666
● Email
○ The title should begin with “[hw4]”
Appendix
● Colab 縮排問題
○ 工具 -> 設定：