| --- |
| license: mit |
| task_categories: |
| - audio-classification |
| modalities: |
| - audio |
| language: |
| - en |
| tags: |
| - MNIST |
| - Audio |
| - Classification |
| - Audio Classification |
| pretty_name: Audio MNIST |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Audio MNIST |
|
|
| Based on [`AudioMNIST`](https://github.com/soerenab/AudioMNIST). |
|
|
| ## Generation of the Parquet File |
|
|
| Given the path to the `data` folder from the [source](https://github.com/soerenab/AudioMNIST) as `audioMNISTFolderPath`: |
|
|
| ```python |
| import numpy as np |
| import pandas as pd |
| |
| import json |
| |
| # Load all wave files in AudioMNIST dataset |
| # Parse each file name as <digit>_<speaker>_<index>.wav |
| |
| dfData = pd.DataFrame(columns = ['Digit', 'Speaker', 'Index', 'SampleRate', 'NumSamples', 'Accent', 'Age', 'Gender', 'NativeSpeaker', 'Continent', 'Country', 'City', 'Room']) |
| |
| with open(os.path.join(audioMNISTFolderPath, 'audioMNIST_meta.txt'), 'r') as f: |
| dMetadata = json.load(f) |
| |
| lWaveFiles = [] |
| |
| lFolders = os.listdir(audioMNISTFolderPath) |
| lFolders = [fld for fld in lFolders if os.path.isdir(os.path.join(audioMNISTFolderPath, fld))] |
| lFolders.sort() |
| |
| fileIdx = -1 |
| for fld in lFolders: |
| lFiles = os.listdir(os.path.join(audioMNISTFolderPath, fld)) |
| lFiles = [f for f in lFiles if f.endswith('.wav')] |
| lFiles.sort() |
| print(f'Folder {fld}: {len(lFiles)} files') |
| for f in lFiles: |
| fileIdx += 1 |
| # Parse File Name |
| digitIdx, speakerIdx, recIdx = f[:-4].split('_') |
| sampleRate, vAudioData = sp.io.wavfile.read(os.path.join(audioMNISTFolderPath, fld, f)) |
| lWaveFiles.append(vAudioData) |
| |
| dfData.loc[fileIdx, 'Digit'] = int(digitIdx) |
| dfData.loc[fileIdx, 'Speaker'] = int(speakerIdx) |
| dfData.loc[fileIdx, 'Index'] = int(recIdx) |
| dfData.loc[fileIdx, 'SampleRate'] = int(sampleRate) |
| dfData.loc[fileIdx, 'NumSamples'] = int(len(vAudioData)) |
| # Parse Metadata |
| metaIdx = f'{int(speakerIdx):02d}' |
| dfData.loc[fileIdx, 'Accent'] = dMetadata[metaIdx]['accent'] |
| dfData.loc[fileIdx, 'Age'] = int(dMetadata[metaIdx]['age']) |
| dfData.loc[fileIdx, 'Gender'] = dMetadata[metaIdx]['gender'] |
| dfData.loc[fileIdx, 'NativeSpeaker'] = dMetadata[metaIdx]['native speaker'] |
| # Parse Continent, Country, City |
| locationStr = dMetadata[metaIdx]['origin'] |
| # Remove spaces, split by ',' |
| locationStr = locationStr.replace(' ', '') |
| contStr, countryStr, cityStr = locationStr.split(',') |
| dfData.loc[fileIdx, 'Continent'] = contStr |
| dfData.loc[fileIdx, 'Country'] = countryStr |
| dfData.loc[fileIdx, 'City'] = cityStr |
| dfData.loc[fileIdx, 'Room'] = dMetadata[metaIdx]['recordingroom'] |
| |
| # Generate DataFrame of the Audio Data |
| maxSignals = dfData.shape[0] |
| maxNumSamples = dfData['NumSamples'].max() |
| |
| mA = np.zeros((maxSignals, maxNumSamples), dtype = np.int16) |
| for ii, vA in enumerate(lWaveFiles): |
| mA[ii, :len(vA)] = vA |
| |
| dfAudio = pd.DataFrame(data = mA, columns = [f'{sampleIdx:d}' for sampleIdx in range(maxNumSamples)]) |
| |
| # Generate the AudioMNIST Data Frame |
| dfAudioMnist = pd.concat([dfData, dfAudio], axis = 1) |
| |
| # Set the Type per column |
| dfAudioMnist['Digit'] = dfAudioMnist['Digit'].astype(np.int8) |
| dfAudioMnist['Speaker'] = dfAudioMnist['Speaker'].astype(np.int8) |
| dfAudioMnist['Index'] = dfAudioMnist['Index'].astype(np.int32) |
| dfAudioMnist['SampleRate'] = dfAudioMnist['SampleRate'].astype(np.int32) |
| dfAudioMnist['NumSamples'] = dfAudioMnist['NumSamples'].astype(np.int32) |
| dfAudioMnist['Age'] = dfAudioMnist['Age'].astype(np.int32) |
| dfAudioMnist['Gender'] = dfAudioMnist['Gender'].map({'male': 'Male', 'female': 'Female'}) |
| dfAudioMnist['NativeSpeaker'] = dfAudioMnist['NativeSpeaker'].map({'yes': True, 'no': False}) |
| dfAudioMnist['NativeSpeaker'] = dfAudioMnist['NativeSpeaker'].astype(bool) |
| |
| # Export to Parquet |
| dfAudioMnist.to_parquet(os.path.join(audioMNISTFolderPath, 'AudioMNIST.parquet'), index = False) |
| ``` |
