diff --git a/speech-to-text/augmentation.py b/speech-to-text/augmentation.py
new file mode 100644
index 0000000..5f622df
--- /dev/null
+++ b/speech-to-text/augmentation.py
@@ -0,0 +1,151 @@
+import numpy as np
+import librosa
+import os
+import scipy
+import json
+
+
+def change_pitch_speech(samples):
+    y_pitch_speed = samples.copy()
+    length_change = np.random.uniform(low = 0.8, high = 1)
+    speed_fac = 1.0 / length_change
+    tmp = np.interp(
+        np.arange(0, len(y_pitch_speed), speed_fac),
+        np.arange(0, len(y_pitch_speed)),
+        y_pitch_speed,
+    )
+    minlen = min(y_pitch_speed.shape[0], tmp.shape[0])
+    y_pitch_speed *= 0
+    y_pitch_speed[0:minlen] = tmp[0:minlen]
+    return y_pitch_speed
+
+
+def change_amplitude(samples):
+    y_aug = samples.copy()
+    dyn_change = np.random.uniform(low = 1.5, high = 3)
+    return y_aug * dyn_change
+
+
+def add_noise(samples):
+    y_noise = samples.copy()
+    noise_amp = 0.01 * np.random.uniform() * np.amax(y_noise)
+    return y_noise.astype('float64') + noise_amp * np.random.normal(
+        size = y_noise.shape[0]
+    )
+
+
+def add_hpss(samples):
+    y_hpss = librosa.effects.hpss(samples.astype('float64'))
+    return y_hpss[1]
+
+
+def strech(samples):
+    input_length = len(samples)
+    streching = samples.copy()
+    random_strech = np.random.uniform(low = 0.5, high = 1.3)
+    print('random_strech = ', random_strech)
+    streching = librosa.effects.time_stretch(
+        streching.astype('float'), random_strech
+    )
+    return streching
+
+
+def random_augmentation(samples):
+    cp = samples.copy()
+    if np.random.randint(0, 2):
+        length_change = np.random.uniform(low = 0.8, high = 1)
+        speed_fac = 1.0 / length_change
+        print('resample length_change = ', length_change)
+        tmp = np.interp(
+            np.arange(0, len(cp), speed_fac), np.arange(0, len(cp)), cp
+        )
+        minlen = min(cp.shape[0], tmp.shape[0])
+        cp *= 0
+        cp[0:minlen] = tmp[0:minlen]
+
+    if np.random.randint(0, 2):
+        dyn_change = np.random.uniform(low = 1.5, high = 3)
+        print('dyn_change = ', dyn_change)
+        cp = cp * dyn_change
+
+    if np.random.randint(0, 2):
+        noise_amp = 0.005 * np.random.uniform() * np.amax(cp)
+        cp = cp.astype('float64') + noise_amp * np.random.normal(
+            size = cp.shape[0]
+        )
+
+    if np.random.randint(0, 2):
+        timeshift_fac = 0.2 * 2 * (np.random.uniform() - 0.5)
+        print('timeshift_fac = ', timeshift_fac)
+        start = int(cp.shape[0] * timeshift_fac)
+        if start > 0:
+            cp = np.pad(cp, (start, 0), mode = 'constant')[0 : cp.shape[0]]
+        else:
+            cp = np.pad(cp, (0, -start), mode = 'constant')[0 : cp.shape[0]]
+    return cp
+
+
+with open('train-test.json') as fopen:
+    wavs = json.load(fopen)['train']
+    
+if not os.path.exists('augment'):
+    os.makedirs('augment')
+
+for no, wav in enumerate(wavs):
+    try:
+        root, ext = os.path.splitext(wav)
+        if (no + 1) % 100 == 0:
+            print(no + 1, root, ext)
+        root = root.replace('/', '<>')
+        root = '%s/%s'%('augment', root)
+        sample_rate, samples = scipy.io.wavfile.read(wav)
+        aug = change_pitch_speech(samples)
+        librosa.output.write_wav(
+            '%s-1%s' % (root, ext),
+            aug.astype('float32'),
+            sample_rate,
+            norm = True,
+        )
+
+        aug = change_amplitude(samples)
+        librosa.output.write_wav(
+            '%s-2%s' % (root, ext),
+            aug.astype('float32'),
+            sample_rate,
+            norm = True,
+        )
+
+        aug = add_noise(samples)
+        librosa.output.write_wav(
+            '%s-3%s' % (root, ext),
+            aug.astype('float32'),
+            sample_rate,
+            norm = True,
+        )
+
+        aug = add_hpss(samples)
+        librosa.output.write_wav(
+            '%s-4%s' % (root, ext),
+            aug.astype('float32'),
+            sample_rate,
+            norm = True,
+        )
+
+        aug = strech(samples)
+        librosa.output.write_wav(
+            '%s-5%s' % (root, ext),
+            aug.astype('float32'),
+            sample_rate,
+            norm = True,
+        )
+
+        aug = random_augmentation(samples)
+        librosa.output.write_wav(
+            '%s-6%s' % (root, ext),
+            aug.astype('float32'),
+            sample_rate,
+            norm = True,
+        )
+    except Exception as e:
+        print(e)
+        pass
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