diff --git a/docs/README.md b/docs/README.md
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+++ b/docs/README.md
@@ -16,3 +16,4 @@ pip install -r requirements.txt
 * Data & Data Pipeline Design
   * [Data Format & Prepare](./data_format.md)
   * [Data pack(sequence pack)](./data_pack.md)
+  * [Data Extractor](./data_extractor.md)
diff --git a/docs/data_extractor.md b/docs/data_extractor.md
new file mode 100644
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+## Data Extactor
+
+We designed Model Extractor for each model to extract the inputs required by that model.
+Each Model Extractor inherits from the `Extractor` defined by WEST, as shown below:
+
+``` python
+class Extractor(ABC):
+
+    model_type = 'model'
+
+    # Batch/Pack fileds for dataset
+    fields_batch_static = {}
+    fields_batch_dynamic = {}
+    fields_pack_offset = {}
+
+    def __init__(self, tokenizer, model_config, inference=False):
+        self.tokenizer = tokenizer
+        self.model_config = model_config
+        self.inference = inference
+
+    @abstractmethod
+    def extract(self, item):
+        pass
+```
+
+* `fields_batch_static`: fileds which has static shape, such as text lengths, speech feature lengths.
+* `fields_batch_dynamic`: fileds which has dynamic shape, which requires padding when batching, such as speech feature.
+* `fields_pack_offset`: fileds we want to keep offset record when packing sequence, such as the speech token start index.
+
+The `extract` function returns a dict object, and here `fileds` are keys in the returned dict.
+
+
+Here is an example of `TouchTTS` Extactor.
+
+
+``` python
+
+class ExtractorTouchTTS(Extractor):
+    model_type = 'touch_tts'
+    fields_batch_static = {'audio_offsets', 'text_lengths'}
+    fields_batch_dynamic = {'audio_features', 'input_ids', 'labels'}
+    fields_pack_offset = {'audio_offsets'}
+
+    def extract(self, item):
+        import s3tokenizer
+        IGNORE_TOKEN_ID = LabelSmoother.ignore_index
+        waveform, sample_rate = torchaudio.load(item['wav'])
+        audio = torchaudio.transforms.Resample(sample_rate, 16000)(waveform)
+        audio = audio[0]  # get the first channel
+        mel = s3tokenizer.log_mel_spectrogram(audio)
+        mel = mel.transpose(0, 1)
+        # There is 100 frames mel per second, and 25 tokens per second
+        num_audio_token = math.ceil(mel.size(0) * 25 / 100.0 - 1e-9)
+        if not self.inference:
+            content = item['txt'] + '<|audio_bos|>'
+            token_lengths = 0
+        else:
+            content = item['txt'] + item['syn_txt'] + '<|audio_bos|>'
+            token_lengths = len(self.tokenizer.encode(item['syn_txt']))
+        ids_text = [self.tokenizer.bos_token_id
+                    ] + self.tokenizer.encode(content)
+        tgt_text = [IGNORE_TOKEN_ID] * len(ids_text)
+        ids_audio = [0] * num_audio_token
+        if not self.inference:
+            ids = ids_text + ids_audio + [self.tokenizer.eos_token_id]
+            tgt = tgt_text + ids_audio + [self.tokenizer.eos_token_id]
+        else:
+            ids = ids_text + ids_audio
+            tgt = tgt_text + ids_audio
+        input_ids = torch.tensor(ids, dtype=torch.long)
+        tgt_ids = torch.tensor(tgt, dtype=torch.long)
+        return {
+            'input_ids': input_ids,
+            'labels': tgt_ids,
+            'audio_features': mel,
+            'audio_offsets': len(ids_text),
+            'text_lengths': token_lengths
+        }
+```
+
+
+The `ExtractorTouchTTS` returns a dict and in which:
+
+* `input_ids`, `labels`, `audio_features` are in dyanmic shape.
+* `audio_offsets`, `text_lengths` are in static shape.
+* `audio_offsets` is the speech token start offset.