diff --git a/mozilla_voice_tts/speaker_encoder/config.json b/mozilla_voice_tts/speaker_encoder/config.json
index 5f72135f..f9166066 100644
--- a/mozilla_voice_tts/speaker_encoder/config.json
+++ b/mozilla_voice_tts/speaker_encoder/config.json
@@ -1,24 +1,30 @@
+
 {
-    "run_name": "libritts_360-half",
-    "run_description": "train speaker encoder for libritts 360",
-    "audio": {
+    "run_name": "libritts_100+360-angleproto",
+    "run_description": "train speaker encoder for libritts 100 and 360",
+    "audio":{
         // Audio processing parameters
-        "num_mels": 40, // size of the mel spec frame. 
-        "num_freq": 1025, // number of stft frequency levels. Size of the linear spectogram frame.
-        "sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "frame_length_ms": 50, // stft window length in ms.
-        "frame_shift_ms": 12.5, // stft window hop-lengh in ms.
-        "preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
-        "min_level_db": -100, // normalization range
-        "ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
+        "num_mels": 80,         // size of the mel spec frame. 
+        "num_freq": 1024,       // number of stft frequency levels. Size of the linear spectogram frame.
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
+        "win_length": 1024,     // stft window length in ms.
+        "hop_length": 256,      // stft window hop-lengh in ms.
+        "frame_length_ms": null,  // stft window length in ms.If null, 'win_length' is used.
+        "frame_shift_ms": null,   // stft window hop-lengh in ms. If null, 'hop_length' is used.
+        "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
+        "min_level_db": -100,   // normalization range
+        "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
+        "power": 1.5,           // value to sharpen wav signals after GL algorithm.
+        "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
         // Normalization parameters
-        "signal_norm": true, // normalize the spec values in range [0, 1]
+        "signal_norm": true,    // normalize the spec values in range [0, 1]
         "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
-        "clip_norm": true, // clip normalized values into the range.
-        "mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
-        "mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
-        "do_trim_silence": false // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "max_norm": 4.0,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "clip_norm": true,      // clip normalized values into the range.
+        "mel_fmin": 0.0,         // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
+        "mel_fmax": 8000.0,        // maximum freq level for mel-spec. Tune for dataset!!
+        "do_trim_silence": false,  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
+        "trim_db": 60          // threshold for timming silence. Set this according to your dataset.
     },
     "reinit_layers": [],
     "loss": "angleproto", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
@@ -34,10 +40,9 @@
     "checkpoint": true, // If true, it saves checkpoints per "save_step"
     "save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
     "print_step": 1, // Number of steps to log traning on console.
-    "output_path": "/media/erogol/data_ssd/Models/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
-    "num_loader_workers": 0, // number of training data loader processes. Don't set it too big. 4-8 are good values.
+    "output_path": "../../checkpoints/libri_tts/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
     "model": {
-        "input_dim": 40,
+        "input_dim": 80, // input_dim == num_mels
         "proj_dim": 128,
         "lstm_dim": 384,
         "num_lstm_layers": 3
@@ -46,13 +51,13 @@
         [
             {
                 "name": "libri_tts",
-                "path": "/home/erogol/Data/Libri-TTS/train-clean-360/",
+                "path": "../../datasets/LibriTTS/train-clean-360/",
                 "meta_file_train": null,
                 "meta_file_val": null
             },
             {
                 "name": "libri_tts",
-                "path": "/home/erogol/Data/Libri-TTS/train-clean-100/",
+                "path": "../../datasets/LibriTTS/train-clean-100/",
                 "meta_file_train": null,
                 "meta_file_val": null
             }