Skip to content

[fix] Fix errors in lecture 12 code #12

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

[fix] Fix errors in lecture 12 code #12

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 3 additions & 0 deletions .gitignore
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -127,3 +127,6 @@ dmypy.json

# Pyre type checker
.pyre/

# json files
*.json
9 changes: 6 additions & 3 deletions 12- Music genre classification: Preparing the dataset/code/extract_data.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
import math
import librosa

DATASET_PATH = "path/to/marsyas/dataset"
DATASET_PATH = "path/to/gtzan/dataset" # can be downloaded from 'https://huggingface.co/datasets/marsyas/gtzan/resolve/main/data/genres.tar.gz'
JSON_PATH = "data_10.json"
SAMPLE_RATE = 22050
TRACK_DURATION = 30 # measured in seconds
Expand Down Expand Up @@ -48,7 +48,10 @@ def save_mfcc(dataset_path, json_path, num_mfcc=13, n_fft=2048, hop_length=512,

# load audio file
file_path = os.path.join(dirpath, f)
signal, sample_rate = librosa.load(file_path, sr=SAMPLE_RATE)
try:
signal, sample_rate = librosa.load(file_path, sr=SAMPLE_RATE)
except Exception:
continue

# process all segments of audio file
for d in range(num_segments):
Expand All @@ -58,7 +61,7 @@ def save_mfcc(dataset_path, json_path, num_mfcc=13, n_fft=2048, hop_length=512,
finish = start + samples_per_segment

# extract mfcc
mfcc = librosa.feature.mfcc(signal[start:finish], sample_rate, n_mfcc=num_mfcc, n_fft=n_fft, hop_length=hop_length)
mfcc = librosa.feature.mfcc(y=signal[start:finish], sr=sample_rate, n_mfcc=num_mfcc, n_fft=n_fft, hop_length=hop_length)
mfcc = mfcc.T

# store only mfcc feature with expected number of vectors
Expand Down