Update README.md

README.md

@@ -3,4 +3,94 @@ license: mit
 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)
+```