chore: update docs

.github/workflows/wiki-publish.yml

@@ -14,7 +14,6 @@ jobs:
       - uses: actions/[email protected]
       - uses: nglehuy/github-wiki-action@master
         with:
-          strategy: clone
           token: ${{ secrets.TOKEN }}
-          path: ./
+          path: docs
           preprocess: true

README.md

@@ -1,5 +1,6 @@
 <h1 align="center">
-<p>TensorFlowASR :zap:</p>
+TensorFlowASR :zap:
+</h1>
 <p align="center">
 <a href="https://github.com/TensorSpeech/TensorFlowASR/blob/main/LICENSE">
   <img alt="GitHub" src="https://img.shields.io/github/license/TensorSpeech/TensorFlowASR?logo=apache&logoColor=green">
@@ -10,9 +11,8 @@
   <img alt="PyPI" src="https://img.shields.io/pypi/v/TensorFlowASR?color=%234285F4&label=release&logo=pypi&logoColor=%234285F4">
 </a>
 </p>
-</h1>
 <h2 align="center">
-<p>Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2</p>
+Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2
 </h2>
 
 <p align="center">
@@ -161,32 +161,9 @@ See [augmentations](./tensorflow_asr/augmentations/README.md)
 
 After converting to tflite, the tflite model is like a function that transforms directly from an **audio signal** to **unicode code points**, then we can convert unicode points to string.
 
-1. Install `tf-nightly` using `pip install tf-nightly`
-2. Build a model with the same architecture as the trained model _(if model has tflite argument, you must set it to True)_, then load the weights from trained model to the built model
-3. Load `TFSpeechFeaturizer` and `TextFeaturizer` to model using function `add_featurizers`
-4. Convert model's function to tflite as follows:
-
-```python
-func = model.make_tflite_function(**options) # options are the arguments of the function
-concrete_func = func.get_concrete_function()
-converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
-converter.experimental_new_converter = True
-converter.optimizations = [tf.lite.Optimize.DEFAULT]
-converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
-                                       tf.lite.OpsSet.SELECT_TF_OPS]
-tflite_model = converter.convert()
-```
-
-5. Save the converted tflite model as follows:
-
-```python
-if not os.path.exists(os.path.dirname(tflite_path)):
-    os.makedirs(os.path.dirname(tflite_path))
-with open(tflite_path, "wb") as tflite_out:
-    tflite_out.write(tflite_model)
-```
+See [tflite_convertion](./docs/tutorials/tflite.md)
 
-5. Then the `.tflite` model is ready to be deployed
+1. Then the `.tflite` model is ready to be deployed
 
 ## Pretrained Models
 

docs/tutorials/tflite.md

@@ -0,0 +1,12 @@
+# TFLite Conversion Tutorial
+
+## Run
+
+```bash
+python examples/train.py \
+    --config-path=/path/to/config.yml.j2 \
+    --h5=/path/to/weight.h5 \
+    --output=/path/to/output.tflite
+## See others params
+python examples/tflite.py --help
+```