Merge pull request #4 from andreasjansson/main

Add Cog config and demo link

README.md

@@ -7,6 +7,7 @@ This is the official repository of **EMOPIA: A Multi-Modal Pop Piano Dataset For
 
 - [Paper on Arxiv](https://arxiv.org/abs/2108.01374)
 - [Demo Page](https://annahung31.github.io/EMOPIA/)
+- [Interactive demo and Docker image on Replicate](https://replicate.ai/annahung31/emopia)
 - [Dataset at Zenodo](https://zenodo.org/record/5090631#.YPPo-JMzZz8)
 
 * Note: We release the transcribed MIDI files. As for the audio part, due to the copyright issue, we will only release the YouTube ID of the tracks and the timestamp of them. You might use [open source crawler](https://github.com/ytdl-org/youtube-dl) to get the audio file.

cog.yaml

@@ -0,0 +1,22 @@
+predict: "predict.py:Predictor"
+build:
+  gpu: true
+  system_packages:
+    - "ffmpeg"
+    - "fluidsynth"
+  python_packages:
+    - "torch==1.7.0"
+    - "scikit-learn==0.24.1"
+    - "seaborn==0.11.1"
+    - "numpy==1.19.5"
+    - "miditoolkit==0.1.14"
+    - "pandas==1.1.5"
+    - "tqdm==4.62.2"
+    - "matplotlib==3.4.3"
+    - "scipy==1.7.1"
+    - "midiSynth==0.3"
+    - "wheel==0.37.0"
+    - "ipdb===0.13.9"
+    - "pyfluidsynth==1.3.0"
+  pre_install:
+    - "pip install pytorch-fast-transformers==0.4.0"  # needs to be installed after the main pip install

predict.py

@@ -0,0 +1,90 @@
+# based on workspace/transformer/generate.ipynb
+
+import subprocess
+from pathlib import Path
+import tempfile
+import os
+import pickle
+import sys
+import torch
+import numpy as np
+from midiSynth.synth import MidiSynth
+import cog
+
+sys.path.insert(0, "workspace/transformer")
+from utils import write_midi
+from models import TransformerModel
+
+
+EMOTIONS = {
+    "High valence, high arousal": 1,
+    "Low valence, high arousal": 2,
+    "Low valence, low arousal": 3,
+    "High valence, low arousal": 4,
+}
+
+
+class Predictor(cog.Predictor):
+    def setup(self):
+        print("Loading dictionary...")
+        path_dictionary = "dataset/co-representation/dictionary.pkl"
+        with open(path_dictionary, "rb") as f:
+            self.dictionary = pickle.load(f)
+        event2word, self.word2event = self.dictionary
+
+        n_class = []  # num of classes for each token
+        for key in event2word.keys():
+            n_class.append(len(event2word[key]))
+        n_token = len(n_class)
+
+        print("Loading model...")
+        path_saved_ckpt = "exp/pretrained_transformer/loss_25_params.pt"
+        self.net = TransformerModel(n_class, is_training=False)
+        self.net.cuda()
+        self.net.eval()
+
+        self.net.load_state_dict(torch.load(path_saved_ckpt))
+
+        self.midi_synth = MidiSynth()
+
+    @cog.input(
+        "emotion",
+        type=str,
+        default="High valence, high arousal",
+        options=EMOTIONS.keys(),
+        help="Emotion to generate for",
+    )
+    @cog.input("seed", type=int, default=-1, help="Random seed, -1 for random")
+    def predict(self, emotion, seed):
+        if seed < 0:
+            seed = int.from_bytes(os.urandom(2), "big")
+        torch.manual_seed(seed)
+        np.random.seed(seed)
+        print(f"Prediction seed: {seed}")
+
+        out_dir = Path(tempfile.mkdtemp())
+        midi_path = out_dir / "out.midi"
+        wav_path = out_dir / "out.wav"
+        mp3_path = out_dir / "out.mp3"
+
+        emotion_tag = EMOTIONS[emotion]
+        res, _ = self.net.inference_from_scratch(
+            self.dictionary, emotion_tag, n_token=8
+        )
+        try:
+            write_midi(res, str(midi_path), self.word2event)
+            self.midi_synth.midi2audio(str(midi_path), str(wav_path))
+            subprocess.check_output(
+                [
+                    "ffmpeg",
+                    "-i",
+                    str(wav_path),
+                    "-af",
+                    "silenceremove=1:0:-50dB,aformat=dblp,areverse,silenceremove=1:0:-50dB,aformat=dblp,areverse",  # strip silence
+                    str(mp3_path),
+                ],
+            )
+            return mp3_path
+        finally:
+            midi_path.unlink(missing_ok=True)
+            wav_path.unlink(missing_ok=True)