Added test for the train command

tests/test_train.py

+#!/usr/bin/env python
+# coding: utf8
+
+""" Unit testing for Separator class. """
+
+__email__ = 'research@deezer.com'
+__author__ = 'Deezer Research'
+__license__ = 'MIT License'
+
+import filecmp
+import itertools
+import os
+from os import makedirs
+from os.path import splitext, basename, exists, join
+from tempfile import TemporaryDirectory
+
+import numpy as np
+import pandas as pd
+import json
+
+import tensorflow as tf
+
+from spleeter.audio.adapter import get_default_audio_adapter
+from spleeter.commands import create_argument_parser
+
+from spleeter.commands import train
+
+from spleeter.utils.configuration import load_configuration
+
+TRAIN_CONFIG = {
+    "mix_name": "mix",
+    "instrument_list": ["vocals", "other"],
+    "sample_rate":44100,
+    "frame_length":4096,
+    "frame_step":1024,
+    "T":128,
+    "F":128,
+    "n_channels":2,
+    "chunk_duration":4,
+    "n_chunks_per_song":1,
+    "separation_exponent":2,
+    "mask_extension":"zeros",
+    "learning_rate": 1e-4,
+    "batch_size":2,
+    "train_max_steps": 10,
+    "throttle_secs":20,
+    "save_checkpoints_steps":100,
+    "save_summary_steps":5,
+    "random_seed":0,
+    "model":{
+        "type":"unet.unet",
+        "params":{
+               "conv_activation":"ELU",
+               "deconv_activation":"ELU"
+        }
+    }
+}
+
+
+def generate_fake_training_dataset(path, instrument_list=["vocals", "other"]):
+    """
+        generates a fake training dataset in path:
+        - generates audio files
+        - generates a csv file describing the dataset
+    """
+    aa = get_default_audio_adapter()
+    n_songs = 2
+    fs = 44100
+    duration = 6
+    n_channels = 2
+    rng = np.random.RandomState(seed=0)
+    dataset_df = pd.DataFrame(columns=["mix_path"]+[f"{instr}_path" for instr in instrument_list]+["duration"])
+    for song in range(n_songs):
+        song_path = join(path, "train", f"song{song}")
+        makedirs(song_path, exist_ok=True)
+        dataset_df.loc[song, f"duration"] = duration
+        for instr in instrument_list+["mix"]:
+            filename = join(song_path, f"{instr}.wav")
+            data = rng.rand(duration*fs, n_channels)-0.5
+            aa.save(filename, data, fs)
+            dataset_df.loc[song, f"{instr}_path"] = join("train", f"song{song}", f"{instr}.wav")
+
+    dataset_df.to_csv(join(path, "train", "train.csv"), index=False)
+
+
+
+def test_train():
+
+
+    with TemporaryDirectory() as path:
+
+        # generate training dataset
+        generate_fake_training_dataset(path)
+
+        # set training command aruments
+        p = create_argument_parser()
+        arguments = p.parse_args(["train", "-p", "useless_config.json", "-d", path])
+        TRAIN_CONFIG["train_csv"] = join(path, "train", "train.csv")
+        TRAIN_CONFIG["validation_csv"] = join(path, "train", "train.csv")
+        TRAIN_CONFIG["model_dir"] = join(path, "model")
+        TRAIN_CONFIG["training_cache"] = join(path, "cache", "training")
+        TRAIN_CONFIG["validation_cache"] = join(path, "cache", "validation")
+
+        # execute training
+        res = train.entrypoint(arguments, TRAIN_CONFIG)
+
+        # assert that model checkpoint was created.
+        assert os.path.exists(join(path,'model','model.ckpt-10.index'))
+        assert os.path.exists(join(path,'model','checkpoint'))
+        assert os.path.exists(join(path,'model','model.ckpt-0.meta'))
+
+if __name__=="__main__":
+    test_train()
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