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-# audio-embedding-clmr
+# Pipeline: Audio Embedding using CLMR
 
-This is another test repo
\ No newline at end of file
+Authors: Jael Gu
+
+## Overview
+
+The pipeline uses a pre-trained CLMR model to extract embeddings of a given audio. It first transforms the input audio to a wave file with sample rate of 22050. Then the model splits the audio data into shorter clips with a fixed length. Finally it generates vectors of each clip, which composes the fingerprint of the input audio.
+
+## Interface
+
+**Input Arguments:**
+
+- filepath:
+  - the input audio
+  - supported types: `str` (path to the audio)
+
+**Pipeline Output:**
+
+The Operator returns a tuple `Tuple[('embs', numpy.ndarray)]` containing following fields:
+
+- embs:
+  - embeddings of input audio
+  - data type: numpy.ndarray
+  - shape: (num_clips,512)
+
+## How to use
+
+1. Install [Towhee](https://github.com/towhee-io/towhee)
+
+```bash
+$ pip3 install towhee
+```
+
+> You can refer to [Getting Started with Towhee](https://towhee.io/) for more details. If you have any questions, you can [submit an issue to the towhee repository](https://github.com/towhee-io/towhee/issues).
+
+2. Run it with Towhee
+
+```python
+>>> from towhee import pipeline
+
+>>> embedding_pipeline = pipeline('towhee/audio-embedding-clmr')
+>>> embedding = embedding_pipeline('path/to/your/audio')
+```
+
+## How it works
+
+This pipeline includes a main operator: [audio embedding](https://hub.towhee.io/towhee/audio-embedding-operator-template) (implemented as [towhee/clmr-magnatagatune](https://hub.towhee.io/towhee/clmr-magnatagatune)). The audio embedding operator encodes fixed-length clips of an audio data and finally output a set of vectors of the given audio.
+