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