diff --git a/CMakeLists.txt b/CMakeLists.txt
index 2134663..5cf0675 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -4,7 +4,7 @@ cmake_minimum_required(VERSION 3.8 FATAL_ERROR)
 
 project(kaldifeat)
 
-set(kaldifeat_VERSION "1.1")
+set(kaldifeat_VERSION "1.2")
 
 set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib")
 set(CMAKE_LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib")
diff --git a/README.md b/README.md
index a149f21..9b36a27 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,121 @@
 # kaldifeat
 
-Feature extraction compatible with kaldi using PyTorch, supporting
-CUDA, batch process, and autograd.
+Feature extraction compatible with `Kaldi` using PyTorch, supporting
+CUDA, batch processing, chunk processing, and autograd.
 
-See [colab notebook][1] for example usage.
 
-[1]: https://colab.research.google.com/drive/16LXJ5QlqO0mQRYK4q1NxOBdM2yL0CYNJ#scrollTo=dTYi4IVJro0j
+# Usage
+
+Let us first generate a test wave using sox:
+
+```bash
+# generate a wave of 1.2 seconds, containing a sine-wave
+# swept from 300 Hz to 3300 Hz
+sox -n -r 16000 -b 16 test.wav synth 1.2 sine 300-3300
+```
+
+**HINT**: Download [test_wav][test_wav].
+
+[test_wav]: kaldifeat/python/tests/test_data/test.wav
+
+## Fbank
+
+```python
+import torchaudio
+
+import kaldifeat
+
+filename = "./test.wav"
+wave, samp_freq = torchaudio.load(filename)
+
+wave = wave.squeeze()
+
+opts = kaldifeat.FbankOptions()
+opts.frame_opts.dither = 0
+# Yes, it has same options like `Kaldi`
+
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave)
+```
+
+To compute features that are compatible with `Kaldi`, wave samples have to be
+scaled to the range `[-32768, 32768]`. WARNING: You don't have to do this if
+you don't care about the compatibility with `Kaldi`
+
+The following is an example:
+
+```python
+wave *= 32768
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave)
+print(features[:3])
+```
+
+The output is:
+
+```
+tensor([[15.0074, 21.1730, 25.5286, 24.4644, 16.6994, 13.8480, 11.2087, 11.7952,
+         10.3911, 10.4491, 10.3012,  9.8743,  9.6997,  9.3751,  9.3476,  9.3559,
+          9.1074,  9.0032,  9.0312,  8.8399,  9.0822,  8.7442,  8.4023],
+        [13.8785, 20.5647, 25.4956, 24.6966, 16.9541, 13.9163, 11.3364, 11.8449,
+         10.2565, 10.5871, 10.3484,  9.7474,  9.6123,  9.3964,  9.0695,  9.1177,
+          8.9136,  8.8425,  8.5920,  8.8315,  8.6226,  8.8605,  8.9763],
+        [13.9475, 19.9410, 25.4494, 24.9051, 17.0004, 13.9207, 11.6667, 11.8217,
+         10.3411, 10.7258, 10.0983,  9.8109,  9.6762,  9.4218,  9.1246,  8.7744,
+          9.0863,  8.7488,  8.4695,  8.6710,  8.7728,  8.7405,  8.9824]])
+```
+
+You can compute the fbank feature for the same wave with `Kaldi` using the following commands:
+
+```bash
+echo "1 test.wav" > test.scp
+compute-fbank-feats --dither=0 scp:test.scp ark,t:test.txt
+head -n4 test.txt
+```
+
+The output is:
+
+```
+1  [
+  15.00744 21.17303 25.52861 24.46438 16.69938 13.84804 11.2087 11.79517 10.3911 10.44909 10.30123 9.874329 9.699727 9.37509 9.347578 9.355928 9.107419 9.00323 9.031268 8.839916 9.082197 8.744139 8.40221
+  13.87853 20.56466 25.49562 24.69662 16.9541 13.91633 11.33638 11.84495 10.25656 10.58718 10.34841 9.747416 9.612316 9.39642 9.06955 9.117751 8.913527 8.842571 8.59212 8.831518 8.622513 8.86048 8.976251
+  13.94753 19.94101 25.4494 24.90511 17.00044 13.92074 11.66673 11.82172 10.34108 10.72575 10.09829 9.810879 9.676199 9.421767 9.124647 8.774353 9.086291 8.74897 8.469534 8.670973 8.772754 8.740549 8.982433
+```
+
+You can see that ``kaldifeat`` produces the same output as `Kaldi` (within some tolerance due to numerical precision).
+
+
+**HINT**: Download [test_scp][test_scp] and [test_txt][test_txt].
+
+[test_scp]: kaldifeat/python/tests/test_data/test.scp
+[test_txt]: kaldifeat/python/tests/test_data/test.txt
+
+
+To use GPU, you can use:
+
+```python
+import torch
+
+opts = kaldifeat.FbankOptions()
+opts.device = torch.device("cuda", 0)
+
+fbank = kaldifeat.Fbank(opts)
+features = fbank(wave.to(opts.device))
+```
+
+## MFCC
+
+To compute MFCC features, please replace `kaldifeat.FbankOptions` and `kaldifeat.Fbank`
+with `kaldifeat.MfccOptions` and `kaldifeat.Mfcc`, respectively.
+
+Please refer to [kaldifeat/python/tests/test_fbank.py](kaldifeat/python/tests/test_fbank.py)
+and [kaldifeat/python/tests/test_mfcc.py](kaldifeat/python/tests/test_mfcc.py)
+for more examples.
+
+**HINT**: In the examples, you can find that
+
+- ``kaldifeat`` supports batch processing as well as chunk processing
+- ``kaldifeat`` uses the same options as `Kaldi`'s `compute-fbank-feats` and `compute-mfcc-feats`
 
 # Installation
 
@@ -34,11 +144,6 @@ python setup.py install
 ```
 
 To test whether `kaldifeat` was installed successfully, you can run:
-```
+```bash
 python3 -c "import kaldifeat; print(kaldifeat.__version__)"
 ```
-
-## Usage
-
-Please refer to <https://kaldifeat.readthedocs.io/en/latest/usage.html>
-for how to use `kaldifeat`.
diff --git a/kaldifeat/python/tests/test_data/run.sh b/kaldifeat/python/tests/test_data/run.sh
index 3875252..394e2f3 100755
--- a/kaldifeat/python/tests/test_data/run.sh
+++ b/kaldifeat/python/tests/test_data/run.sh
@@ -6,7 +6,7 @@ KALDI_ROOT=/root/fangjun/open-source/kaldi
 export PATH=${KALDI_ROOT}/src/featbin:$PATH
 
 if [ ! -f test.wav ]; then
-  # generate a wav of two seconds, containing a sine-wave
+  # generate a wav of 1.2 seconds, containing a sine-wave
   # swept from 300 Hz to 3300 Hz
   sox -n -r 16000 -b 16 test.wav synth 1.2 sine 300-3300
 fi