diff --git a/TTS/speaker_encoder/speaker_encoder_config.py b/TTS/speaker_encoder/speaker_encoder_config.py
new file mode 100644
index 00000000..402ac892
--- /dev/null
+++ b/TTS/speaker_encoder/speaker_encoder_config.py
@@ -0,0 +1,54 @@
+from coqpit import MISSING
+from dataclasses import dataclass, field, asdict
+from typing import List
+from TTS.config.shared_configs import BaseTrainingConfig, BaseAudioConfig, BaseDatasetConfig
+
+
+@dataclass
+class SpeakerEncoderConfig(BaseTrainingConfig):
+    """Defines parameters for Speaker Encoder model."""
+
+    model: str = "speaker_encoder"
+    audio: BaseAudioConfig = field(default_factory=BaseAudioConfig)
+    datasets: List[BaseDatasetConfig] = field(default_factory=lambda: [BaseDatasetConfig()])
+
+    # model params
+    model_params: dict = field(default_factory=lambda: {
+        "input_dim": 40,
+        "proj_dim": 256,
+        "lstm_dim": 768,
+        "num_lstm_layers": 3,
+        "use_lstm_with_projection": True
+    })
+
+    storage: dict = field(default_factory=lambda:{
+        "sample_from_storage_p": 0.66,  # the probability with which we'll sample from the DataSet in-memory storage
+        "storage_size": 15,   # the size of the in-memory storage with respect to a single batch
+        "additive_noise": 1e-5   # add very small gaussian noise to the data in order to increase robustness
+    })
+
+    # training params
+    max_train_step: int = 1000  # end training when number of training steps reaches this value.
+    loss: str = 'angleproto'
+    grad_clip: float = 3.0
+    lr: float = 0.0001
+    lr_decay: bool = False
+    warmup_steps: int = 4000
+    wd: float = 1e-6
+
+    # logging params
+    tb_model_param_stats: bool = False
+    steps_plot_stats: int = 10
+    checkpoint: bool = True
+    save_step: int = 1000
+    print_step: int = 20
+
+    # data loader
+    num_speakers_in_batch: int = MISSING
+    num_utters_per_speaker: int = MISSING
+    num_loader_workers: int = MISSING
+
+    def check_values(self):
+        super().check_values()
+        c = asdict(self)
+        assert c['model_params']['input_dim'] == self.audio.num_mels, " [!] model input dimendion must be equal to melspectrogram dimension."