REBASED: Transform Speaker Encoder in a Generic Encoder and Implement Emotion Encoder training support (#1349)

* Rename Speaker encoder module to encoder

* Add a generic emotion dataset formatter

* Transform the Speaker Encoder dataset to a generic dataset and create emotion encoder config

* Add class map in emotion config

* Add Base encoder config

* Add evaluation encoder script

* Fix the bug in plot_embeddings

* Enable Weight decay for encoder training

* Add argumnet to disable storage

* Add Perfect Sampler and remove storage

* Add evaluation during encoder training

* Fix lint checks

* Remove useless config parameter

* Active evaluation in speaker encoder test and use multispeaker dataset for this test

* Unit tests fixs

* Remove useless tests for speedup the aux_tests

* Use get_optimizer in Encoder

* Add BaseEncoder Class

* Fix the unitests

* Add Perfect Batch Sampler unit test

* Add compute encoder accuracy in a function

tests/data_tests/test_samplers.py

@@ -8,6 +8,7 @@ from TTS.config.shared_configs import BaseDatasetConfig
 from TTS.tts.datasets import load_tts_samples
 from TTS.tts.utils.languages import get_language_balancer_weights
 from TTS.tts.utils.speakers import get_speaker_balancer_weights
+from TTS.encoder.utils.samplers import PerfectBatchSampler
 
 # Fixing random state to avoid random fails
 torch.manual_seed(0)
@@ -82,3 +83,51 @@ class TestSamplers(unittest.TestCase):
                 spk2 += 1
 
         assert is_balanced(spk1, spk2), "Speaker Weighted sampler is supposed to be balanced"
+
+    def test_perfect_sampler(self):  # pylint: disable=no-self-use
+        classes = set()
+        for item in train_samples:
+            classes.add(item["speaker_name"])
+
+        sampler = PerfectBatchSampler(
+            train_samples,
+            classes,
+            batch_size=2 * 3, # total batch size
+            num_classes_in_batch=2,
+            label_key="speaker_name",
+            shuffle=False,
+            drop_last=True)
+        batchs = functools.reduce(lambda a, b: a + b, [list(sampler) for i in range(100)])
+        for batch in batchs:
+            spk1, spk2 = 0, 0
+            # for in each batch
+            for index in batch:
+                if train_samples[index]["speaker_name"] == "ljspeech-0":
+                    spk1 += 1
+                else:
+                    spk2 += 1
+            assert spk1 == spk2, "PerfectBatchSampler is supposed to be perfectly balanced"
+
+    def test_perfect_sampler_shuffle(self):  # pylint: disable=no-self-use
+        classes = set()
+        for item in train_samples:
+            classes.add(item["speaker_name"])
+
+        sampler = PerfectBatchSampler(
+            train_samples,
+            classes,
+            batch_size=2 * 3, # total batch size
+            num_classes_in_batch=2,
+            label_key="speaker_name",
+            shuffle=True,
+            drop_last=False)
+        batchs = functools.reduce(lambda a, b: a + b, [list(sampler) for i in range(100)])
+        for batch in batchs:
+            spk1, spk2 = 0, 0
+            # for in each batch
+            for index in batch:
+                if train_samples[index]["speaker_name"] == "ljspeech-0":
+                    spk1 += 1
+                else:
+                    spk2 += 1
+            assert spk1 == spk2, "PerfectBatchSampler is supposed to be perfectly balanced"