Merge pull request #581 from Edresson/dev

Compute speaker embeddings in batch for the LSTM  Speaker Encoder and Compute embeddings/ finding chars using config file.

TTS/bin/compute_embeddings.py

@@ -1,80 +1,46 @@
+
 import argparse
-import glob
 import os
 
-import torch
 from tqdm import tqdm
 
-from TTS.config import BaseDatasetConfig, load_config
-from TTS.speaker_encoder.utils.generic_utils import setup_model
+from argparse import RawTextHelpFormatter
+from TTS.config import load_config
 from TTS.tts.datasets import load_meta_data
 from TTS.tts.utils.speakers import SpeakerManager
-from TTS.utils.audio import AudioProcessor
 
 parser = argparse.ArgumentParser(
-    description='Compute embedding vectors for each wav file in a dataset. If "target_dataset" is defined, it generates "speakers.json" necessary for training a multi-speaker model.'
+    description="""Compute embedding vectors for each wav file in a dataset.\n\n"""
+        """
+    Example runs:
+    python TTS/bin/compute_embeddings.py speaker_encoder_model.pth.tar speaker_encoder_config.json  dataset_config.json embeddings_output_path/
+    """,
+        formatter_class=RawTextHelpFormatter,
 )
-parser.add_argument("model_path", type=str, help="Path to model outputs (checkpoint, tensorboard etc.).")
+parser.add_argument("model_path", type=str, help="Path to model checkpoint file.")
 parser.add_argument(
     "config_path",
     type=str,
-    help="Path to config file for training.",
+    help="Path to model config file.",
 )
-parser.add_argument("data_path", type=str, help="Data path for wav files - directory or CSV file")
-parser.add_argument("output_path", type=str, help="path for output speakers.json.")
+
 parser.add_argument(
-    "--target_dataset",
+    "config_dataset_path",
     type=str,
-    default="",
-    help="Target dataset to pick a processor from TTS.tts.dataset.preprocess. Necessary to create a speakers.json file.",
+    help="Path to dataset config file.",
 )
+parser.add_argument("output_path", type=str, help="path for output speakers.json and/or speakers.npy.")
 parser.add_argument("--use_cuda", type=bool, help="flag to set cuda.", default=True)
-parser.add_argument("--separator", type=str, help="Separator used in file if CSV is passed for data_path", default="|")
+parser.add_argument("--eval", type=bool, help="compute eval.", default=True)
+
 args = parser.parse_args()
 
+c_dataset = load_config(args.config_dataset_path)
 
-c = load_config(args.config_path)
-ap = AudioProcessor(**c["audio"])
+meta_data_train, meta_data_eval = load_meta_data(c_dataset.datasets, eval_split=args.eval)
+wav_files = meta_data_train + meta_data_eval
 
-data_path = args.data_path
-split_ext = os.path.splitext(data_path)
-sep = args.separator
-
-if args.target_dataset != "":
-    # if target dataset is defined
-    dataset_config = [
-        BaseDatasetConfig(name=args.target_dataset, path=args.data_path, meta_file_train=None, meta_file_val=None),
-    ]
-    wav_files, _ = load_meta_data(dataset_config, eval_split=False)
-else:
-    # if target dataset is not defined
-    if len(split_ext) > 0 and split_ext[1].lower() == ".csv":
-        # Parse CSV
-        print(f"CSV file: {data_path}")
-        with open(data_path) as f:
-            wav_path = os.path.join(os.path.dirname(data_path), "wavs")
-            wav_files = []
-            print(f"Separator is: {sep}")
-            for line in f:
-                components = line.split(sep)
-                if len(components) != 2:
-                    print("Invalid line")
-                    continue
-                wav_file = os.path.join(wav_path, components[0] + ".wav")
-                # print(f'wav_file: {wav_file}')
-                if os.path.exists(wav_file):
-                    wav_files.append(wav_file)
-        print(f"Count of wavs imported: {len(wav_files)}")
-    else:
-        # Parse all wav files in data_path
-        wav_files = glob.glob(data_path + "/**/*.wav", recursive=True)
-
-# define Encoder model
-model = setup_model(c)
-model.load_state_dict(torch.load(args.model_path)["model"])
-model.eval()
-if args.use_cuda:
-    model.cuda()
+speaker_manager = SpeakerManager(encoder_model_path=args.model_path, encoder_config_path=args.config_path, use_cuda=args.use_cuda)
 
 # compute speaker embeddings
 speaker_mapping = {}
@@ -85,27 +51,24 @@ for idx, wav_file in enumerate(tqdm(wav_files)):
     else:
         speaker_name = None
 
-    mel_spec = ap.melspectrogram(ap.load_wav(wav_file, sr=ap.sample_rate)).T
-    mel_spec = torch.FloatTensor(mel_spec[None, :, :])
-    if args.use_cuda:
-        mel_spec = mel_spec.cuda()
-    embedd = model.compute_embedding(mel_spec)
-    embedd = embedd.detach().cpu().numpy()
+    # extract the embedding
+    embedd = speaker_manager.compute_d_vector_from_clip(wav_file)
 
     # create speaker_mapping if target dataset is defined
     wav_file_name = os.path.basename(wav_file)
     speaker_mapping[wav_file_name] = {}
     speaker_mapping[wav_file_name]["name"] = speaker_name
-    speaker_mapping[wav_file_name]["embedding"] = embedd.flatten().tolist()
+    speaker_mapping[wav_file_name]["embedding"] = embedd
 
 if speaker_mapping:
     # save speaker_mapping if target dataset is defined
-    if ".json" not in args.output_path:
+    if '.json' not in args.output_path:
         mapping_file_path = os.path.join(args.output_path, "speakers.json")
     else:
         mapping_file_path = args.output_path
+
     os.makedirs(os.path.dirname(mapping_file_path), exist_ok=True)
-    speaker_manager = SpeakerManager()
+
     # pylint: disable=W0212
     speaker_manager._save_json(mapping_file_path, speaker_mapping)
     print("Speaker embeddings saved at:", mapping_file_path)

TTS/bin/extract_tts_spectrograms.py

@@ -227,7 +227,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     ap = AudioProcessor(**c.audio)
 
     # load data instances
-    meta_data_train, meta_data_eval = load_meta_data(c.datasets)
+    meta_data_train, meta_data_eval = load_meta_data(c.datasets, eval_split=args.eval)
 
     # use eval and training partitions
     meta_data = meta_data_train + meta_data_eval
@@ -271,6 +271,7 @@ if __name__ == "__main__":
     parser.add_argument("--debug", default=False, action="store_true", help="Save audio files for debug")
     parser.add_argument("--save_audio", default=False, action="store_true", help="Save audio files")
     parser.add_argument("--quantized", action="store_true", help="Save quantized audio files")
+    parser.add_argument("--eval", type=bool, help="compute eval.", default=True)
     args = parser.parse_args()
 
     c = load_config(args.config_path)

TTS/bin/find_unique_chars.py

@@ -1,41 +1,42 @@
 """Find all the unique characters in a dataset"""
 import argparse
-import os
 from argparse import RawTextHelpFormatter
-
-from TTS.tts.datasets import _get_preprocessor_by_name
+from TTS.tts.datasets import load_meta_data
+from TTS.config import load_config
 
 
 def main():
     # pylint: disable=bad-option-value
     parser = argparse.ArgumentParser(
         description="""Find all the unique characters or phonemes in a dataset.\n\n"""
-        """Target dataset must be defined in TTS.tts.datasets.formatters\n\n"""
         """
     Example runs:
 
-    python TTS/bin/find_unique_chars.py --dataset ljspeech --meta_file /path/to/LJSpeech/metadata.csv
+    python TTS/bin/find_unique_chars.py --config_path config.json
     """,
         formatter_class=RawTextHelpFormatter,
     )
-
     parser.add_argument(
-        "--dataset", type=str, default="", help="One of the target dataset names in TTS.tts.datasets.formatters."
+        "--config_path", type=str, help="Path to dataset config file.", required=True
     )
-
-    parser.add_argument("--meta_file", type=str, default=None, help="Path to the transcriptions file of the dataset.")
-
     args = parser.parse_args()
 
-    preprocessor = _get_preprocessor_by_name(args.dataset)
-    items = preprocessor(os.path.dirname(args.meta_file), os.path.basename(args.meta_file))
+    c = load_config(args.config_path)
+
+    # load all datasets
+    train_items, eval_items = load_meta_data(c.datasets, eval_split=True)
+    items = train_items + eval_items
+
     texts = "".join(item[0] for item in items)
     chars = set(texts)
     lower_chars = filter(lambda c: c.islower(), chars)
+    chars_force_lower = [c.lower() for c in chars]
+    chars_force_lower = set(chars_force_lower)
+
     print(f" > Number of unique characters: {len(chars)}")
     print(f" > Unique characters: {''.join(sorted(chars))}")
     print(f" > Unique lower characters: {''.join(sorted(lower_chars))}")
-
+    print(f" > Unique all forced to lower characters: {''.join(sorted(chars_force_lower))}")
 
 if __name__ == "__main__":
     main()

TTS/speaker_encoder/models/lstm.py

@@ -1,4 +1,5 @@
 import torch
+import numpy as np
 from torch import nn
 
 
@@ -70,24 +71,32 @@ class LSTMSpeakerEncoder(nn.Module):
             d = torch.nn.functional.normalize(d, p=2, dim=1)
         return d
 
-    def compute_embedding(self, x, num_frames=160, overlap=0.5):
+    def compute_embedding(self, x, num_frames=250, num_eval=10, return_mean=True):
         """
         Generate embeddings for a batch of utterances
         x: 1xTxD
         """
-        num_overlap = int(num_frames * overlap)
         max_len = x.shape[1]
-        embed = None
-        cur_iter = 0
-        for offset in range(0, max_len, num_frames - num_overlap):
-            cur_iter += 1
-            end_offset = min(x.shape[1], offset + num_frames)
+
+        if max_len < num_frames:
+            num_frames = max_len
+
+        offsets = np.linspace(0, max_len-num_frames, num=num_eval)
+
+        frames_batch = []
+        for offset in offsets:
+            offset = int(offset)
+            end_offset = int(offset+num_frames)
             frames = x[:, offset:end_offset]
-            if embed is None:
-                embed = self.inference(frames)
-            else:
-                embed += self.inference(frames)
-        return embed / cur_iter
+            frames_batch.append(frames)
+
+        frames_batch = torch.cat(frames_batch, dim=0)
+        embeddings = self.inference(frames_batch)
+
+        if return_mean:
+            embeddings = torch.mean(embeddings, dim=0, keepdim=True)
+
+        return embeddings
 
     def batch_compute_embedding(self, x, seq_lens, num_frames=160, overlap=0.5):
         """
@@ -110,9 +119,11 @@ class LSTMSpeakerEncoder(nn.Module):
         return embed / num_iters
 
     # pylint: disable=unused-argument, redefined-builtin
-    def load_checkpoint(self, config: dict, checkpoint_path: str, eval: bool = False):
+    def load_checkpoint(self, config: dict, checkpoint_path: str, eval: bool = False, use_cuda: bool = False):
         state = torch.load(checkpoint_path, map_location=torch.device("cpu"))
         self.load_state_dict(state["model"])
+        if use_cuda:
+            self.cuda()
         if eval:
             self.eval()
             assert not self.training

TTS/speaker_encoder/models/resnet.py

@@ -199,3 +199,12 @@ class ResNetSpeakerEncoder(nn.Module):
             embeddings = torch.mean(embeddings, dim=0, keepdim=True)
 
         return embeddings
+
+    def load_checkpoint(self, config: dict, checkpoint_path: str, eval: bool = False, use_cuda: bool = False):
+        state = torch.load(checkpoint_path, map_location=torch.device("cpu"))
+        self.load_state_dict(state["model"])
+        if use_cuda:
+            self.cuda()
+        if eval:
+            self.eval()
+            assert not self.training

TTS/tts/datasets/formatters.py

@@ -202,16 +202,20 @@ def libri_tts(root_path, meta_files=None):
     items = []
     if meta_files is None:
         meta_files = glob(f"{root_path}/**/*trans.tsv", recursive=True)
+    else:
+        if isinstance(meta_files, str):
+            meta_files = [os.path.join(root_path, meta_files)]
+
     for meta_file in meta_files:
         _meta_file = os.path.basename(meta_file).split(".")[0]
-        speaker_name = _meta_file.split("_")[0]
-        chapter_id = _meta_file.split("_")[1]
-        _root_path = os.path.join(root_path, f"{speaker_name}/{chapter_id}")
         with open(meta_file, "r") as ttf:
             for line in ttf:
                 cols = line.split("\t")
-                wav_file = os.path.join(_root_path, cols[0] + ".wav")
-                text = cols[1]
+                file_name = cols[0]
+                speaker_name, chapter_id, *_ = cols[0].split("_")
+                _root_path = os.path.join(root_path, f"{speaker_name}/{chapter_id}")
+                wav_file = os.path.join(_root_path, file_name + ".wav")
+                text = cols[2]
                 items.append([text, wav_file, "LTTS_" + speaker_name])
     for item in items:
         assert os.path.exists(item[1]), f" [!] wav files don't exist - {item[1]}"
@@ -287,6 +291,17 @@ def vctk_slim(root_path, meta_files=None, wavs_path="wav48"):
 
     return items
 
+def mls(root_path, meta_files=None):
+    """http://www.openslr.org/94/"""
+    items = []
+    with open(os.path.join(root_path, meta_files), "r") as meta:
+        for line in meta:
+            file, text = line.split('\t')
+            text = text[:-1]
+            speaker, book, *_ = file.split('_')
+            wav_file = os.path.join(root_path, os.path.dirname(meta_files), 'audio', speaker, book, file + ".wav")
+            items.append([text, wav_file, "MLS_" + speaker])
+    return items
 
 # ======================================== VOX CELEB ===========================================
 def voxceleb2(root_path, meta_file=None):

TTS/tts/utils/speakers.py

@@ -59,6 +59,7 @@ class SpeakerManager:
         speaker_id_file_path: str = "",
         encoder_model_path: str = "",
         encoder_config_path: str = "",
+        use_cuda: bool = False,
     ):
 
         self.data_items = []
@@ -67,6 +68,7 @@ class SpeakerManager:
         self.clip_ids = []
         self.speaker_encoder = None
         self.speaker_encoder_ap = None
+        self.use_cuda = use_cuda
 
         if data_items:
             self.speaker_ids, self.speaker_names, _ = self.parse_speakers_from_data(self.data_items)
@@ -222,11 +224,11 @@ class SpeakerManager:
         """
         self.speaker_encoder_config = load_config(config_path)
         self.speaker_encoder = setup_model(self.speaker_encoder_config)
-        self.speaker_encoder.load_checkpoint(config_path, model_path, True)
+        self.speaker_encoder.load_checkpoint(config_path, model_path, eval=True, use_cuda=self.use_cuda)
         self.speaker_encoder_ap = AudioProcessor(**self.speaker_encoder_config.audio)
         # normalize the input audio level and trim silences
-        self.speaker_encoder_ap.do_sound_norm = True
-        self.speaker_encoder_ap.do_trim_silence = True
+        # self.speaker_encoder_ap.do_sound_norm = True
+        # self.speaker_encoder_ap.do_trim_silence = True
 
     def compute_d_vector_from_clip(self, wav_file: Union[str, list]) -> list:
         """Compute a d_vector from a given audio file.
@@ -242,6 +244,8 @@ class SpeakerManager:
             waveform = self.speaker_encoder_ap.load_wav(wav_file, sr=self.speaker_encoder_ap.sample_rate)
             spec = self.speaker_encoder_ap.melspectrogram(waveform)
             spec = torch.from_numpy(spec.T)
+            if self.use_cuda:
+                spec = spec.cuda()
             spec = spec.unsqueeze(0)
             d_vector = self.speaker_encoder.compute_embedding(spec)
             return d_vector
@@ -272,6 +276,8 @@ class SpeakerManager:
             feats = torch.from_numpy(feats)
         if feats.ndim == 2:
             feats = feats.unsqueeze(0)
+        if self.use_cuda:
+            feats = feats.cuda()
         return self.speaker_encoder.compute_embedding(feats)
 
     def run_umap(self):

notebooks/PlotUmapLibriTTS.ipynb

@@ -13,11 +13,7 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 2,
-=======
    "execution_count": null,
->>>>>>> dev
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,15 +25,10 @@
     "import umap\n",
     "\n",
     "from TTS.speaker_encoder.model import SpeakerEncoder\n",
-<<<<<<< HEAD
-    "from TTS.utils.audio import AudioProcessor\n",
-    "from TTS.utils.io import load_config\n",
-=======
     "from TTS.utils.audio import AudioProcessor
 
 \n",
     "from TTS.tts.utils.generic_utils import load_config\n",
->>>>>>> dev
     "\n",
     "from bokeh.io import output_notebook, show\n",
     "from bokeh.plotting import figure\n",
@@ -59,331 +50,9 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "    <div class=\"bk-root\">\n",
-       "        <a href=\"https://bokeh.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
-       "        <span id=\"1001\">Loading BokehJS ...</span>\n",
-       "    </div>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/javascript": [
-       "\n",
-       "(function(root) {\n",
-       "  function now() {\n",
-       "    return new Date();\n",
-       "  }\n",
-       "\n",
-       "  var force = true;\n",
-       "\n",
-       "  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
-       "    root._bokeh_onload_callbacks = [];\n",
-       "    root._bokeh_is_loading = undefined;\n",
-       "  }\n",
-       "\n",
-       "  var JS_MIME_TYPE = 'application/javascript';\n",
-       "  var HTML_MIME_TYPE = 'text/html';\n",
-       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
-       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
-       "\n",
-       "  /**\n",
-       "   * Render data to the DOM node\n",
-       "   */\n",
-       "  function render(props, node) {\n",
-       "    var script = document.createElement(\"script\");\n",
-       "    node.appendChild(script);\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when an output is cleared or removed\n",
-       "   */\n",
-       "  function handleClearOutput(event, handle) {\n",
-       "    var cell = handle.cell;\n",
-       "\n",
-       "    var id = cell.output_area._bokeh_element_id;\n",
-       "    var server_id = cell.output_area._bokeh_server_id;\n",
-       "    // Clean up Bokeh references\n",
-       "    if (id != null && id in Bokeh.index) {\n",
-       "      Bokeh.index[id].model.document.clear();\n",
-       "      delete Bokeh.index[id];\n",
-       "    }\n",
-       "\n",
-       "    if (server_id !== undefined) {\n",
-       "      // Clean up Bokeh references\n",
-       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
-       "      cell.notebook.kernel.execute(cmd, {\n",
-       "        iopub: {\n",
-       "          output: function(msg) {\n",
-       "            var id = msg.content.text.trim();\n",
-       "            if (id in Bokeh.index) {\n",
-       "              Bokeh.index[id].model.document.clear();\n",
-       "              delete Bokeh.index[id];\n",
-       "            }\n",
-       "          }\n",
-       "        }\n",
-       "      });\n",
-       "      // Destroy server and session\n",
-       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
-       "      cell.notebook.kernel.execute(cmd);\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  /**\n",
-       "   * Handle when a new output is added\n",
-       "   */\n",
-       "  function handleAddOutput(event, handle) {\n",
-       "    var output_area = handle.output_area;\n",
-       "    var output = handle.output;\n",
-       "\n",
-       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
-       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
-       "      return\n",
-       "    }\n",
-       "\n",
-       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
-       "\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
-       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
-       "      // store reference to embed id on output_area\n",
-       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
-       "    }\n",
-       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
-       "      var bk_div = document.createElement(\"div\");\n",
-       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
-       "      var script_attrs = bk_div.children[0].attributes;\n",
-       "      for (var i = 0; i < script_attrs.length; i++) {\n",
-       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
-       "      }\n",
-       "      // store reference to server id on output_area\n",
-       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  function register_renderer(events, OutputArea) {\n",
-       "\n",
-       "    function append_mime(data, metadata, element) {\n",
-       "      // create a DOM node to render to\n",
-       "      var toinsert = this.create_output_subarea(\n",
-       "        metadata,\n",
-       "        CLASS_NAME,\n",
-       "        EXEC_MIME_TYPE\n",
-       "      );\n",
-       "      this.keyboard_manager.register_events(toinsert);\n",
-       "      // Render to node\n",
-       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
-       "      render(props, toinsert[toinsert.length - 1]);\n",
-       "      element.append(toinsert);\n",
-       "      return toinsert\n",
-       "    }\n",
-       "\n",
-       "    /* Handle when an output is cleared or removed */\n",
-       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
-       "    events.on('delete.Cell', handleClearOutput);\n",
-       "\n",
-       "    /* Handle when a new output is added */\n",
-       "    events.on('output_added.OutputArea', handleAddOutput);\n",
-       "\n",
-       "    /**\n",
-       "     * Register the mime type and append_mime function with output_area\n",
-       "     */\n",
-       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
-       "      /* Is output safe? */\n",
-       "      safe: true,\n",
-       "      /* Index of renderer in `output_area.display_order` */\n",
-       "      index: 0\n",
-       "    });\n",
-       "  }\n",
-       "\n",
-       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
-       "  if (root.Jupyter !== undefined) {\n",
-       "    var events = require('base/js/events');\n",
-       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
-       "\n",
-       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
-       "      register_renderer(events, OutputArea);\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "  \n",
-       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
-       "    root._bokeh_timeout = Date.now() + 5000;\n",
-       "    root._bokeh_failed_load = false;\n",
-       "  }\n",
-       "\n",
-       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
-       "     \"<div style='background-color: #fdd'>\\n\"+\n",
-       "     \"<p>\\n\"+\n",
-       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
-       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
-       "     \"</p>\\n\"+\n",
-       "     \"<ul>\\n\"+\n",
-       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
-       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
-       "     \"</ul>\\n\"+\n",
-       "     \"<code>\\n\"+\n",
-       "     \"from bokeh.resources import INLINE\\n\"+\n",
-       "     \"output_notebook(resources=INLINE)\\n\"+\n",
-       "     \"</code>\\n\"+\n",
-       "     \"</div>\"}};\n",
-       "\n",
-       "  function display_loaded() {\n",
-       "    var el = document.getElementById(\"1001\");\n",
-       "    if (el != null) {\n",
-       "      el.textContent = \"BokehJS is loading...\";\n",
-       "    }\n",
-       "    if (root.Bokeh !== undefined) {\n",
-       "      if (el != null) {\n",
-       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
-       "      }\n",
-       "    } else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(display_loaded, 100)\n",
-       "    }\n",
-       "  }\n",
-       "\n",
-       "\n",
-       "  function run_callbacks() {\n",
-       "    try {\n",
-       "      root._bokeh_onload_callbacks.forEach(function(callback) {\n",
-       "        if (callback != null)\n",
-       "          callback();\n",
-       "      });\n",
-       "    } finally {\n",
-       "      delete root._bokeh_onload_callbacks\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: all callbacks have finished\");\n",
-       "  }\n",
-       "\n",
-       "  function load_libs(css_urls, js_urls, callback) {\n",
-       "    if (css_urls == null) css_urls = [];\n",
-       "    if (js_urls == null) js_urls = [];\n",
-       "\n",
-       "    root._bokeh_onload_callbacks.push(callback);\n",
-       "    if (root._bokeh_is_loading > 0) {\n",
-       "      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
-       "      return null;\n",
-       "    }\n",
-       "    if (js_urls == null || js_urls.length === 0) {\n",
-       "      run_callbacks();\n",
-       "      return null;\n",
-       "    }\n",
-       "    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
-       "    root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
-       "\n",
-       "    function on_load() {\n",
-       "      root._bokeh_is_loading--;\n",
-       "      if (root._bokeh_is_loading === 0) {\n",
-       "        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
-       "        run_callbacks()\n",
-       "      }\n",
-       "    }\n",
-       "\n",
-       "    function on_error() {\n",
-       "      console.error(\"failed to load \" + url);\n",
-       "    }\n",
-       "\n",
-       "    for (var i = 0; i < css_urls.length; i++) {\n",
-       "      var url = css_urls[i];\n",
-       "      const element = document.createElement(\"link\");\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error;\n",
-       "      element.rel = \"stylesheet\";\n",
-       "      element.type = \"text/css\";\n",
-       "      element.href = url;\n",
-       "      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
-       "      document.body.appendChild(element);\n",
-       "    }\n",
-       "\n",
-       "    for (var i = 0; i < js_urls.length; i++) {\n",
-       "      var url = js_urls[i];\n",
-       "      var element = document.createElement('script');\n",
-       "      element.onload = on_load;\n",
-       "      element.onerror = on_error;\n",
-       "      element.async = false;\n",
-       "      element.src = url;\n",
-       "      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
-       "      document.head.appendChild(element);\n",
-       "    }\n",
-       "  };var element = document.getElementById(\"1001\");\n",
-       "  if (element == null) {\n",
-       "    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n",
-       "    return false;\n",
-       "  }\n",
-       "\n",
-       "  function inject_raw_css(css) {\n",
-       "    const element = document.createElement(\"style\");\n",
-       "    element.appendChild(document.createTextNode(css));\n",
-       "    document.body.appendChild(element);\n",
-       "  }\n",
-       "\n",
-       "  \n",
-       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n",
-       "  var css_urls = [];\n",
-       "  \n",
-       "\n",
-       "  var inline_js = [\n",
-       "    function(Bokeh) {\n",
-       "      Bokeh.set_log_level(\"info\");\n",
-       "    },\n",
-       "    function(Bokeh) {\n",
-       "    \n",
-       "    \n",
-       "    }\n",
-       "  ];\n",
-       "\n",
-       "  function run_inline_js() {\n",
-       "    \n",
-       "    if (root.Bokeh !== undefined || force === true) {\n",
-       "      \n",
-       "    for (var i = 0; i < inline_js.length; i++) {\n",
-       "      inline_js[i].call(root, root.Bokeh);\n",
-       "    }\n",
-       "    if (force === true) {\n",
-       "        display_loaded();\n",
-       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
-       "      setTimeout(run_inline_js, 100);\n",
-       "    } else if (!root._bokeh_failed_load) {\n",
-       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
-       "      root._bokeh_failed_load = true;\n",
-       "    } else if (force !== true) {\n",
-       "      var cell = $(document.getElementById(\"1001\")).parents('.cell').data().cell;\n",
-       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
-       "    }\n",
-       "\n",
-       "  }\n",
-       "\n",
-       "  if (root._bokeh_is_loading === 0) {\n",
-       "    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
-       "    run_inline_js();\n",
-       "  } else {\n",
-       "    load_libs(css_urls, js_urls, function() {\n",
-       "      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
-       "      run_inline_js();\n",
-       "    });\n",
-       "  }\n",
-       "}(window));"
-      ],
-      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1001\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\"1001\");\n  if (element == null) {\n    console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  \n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n  var css_urls = [];\n  \n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    function(Bokeh) {\n    \n    \n    }\n  ];\n\n  function run_inline_js() {\n    \n    if (root.Bokeh !== undefined || force === true) {\n      \n    for (var i = 0; i < inline_js.length; i++) {\n      inline_js[i].call(root, root.Bokeh);\n    }\n    if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_loLine truncated
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-=======
    "execution_count": null,
    "metadata": {},
    "outputs": [],
->>>>>>> dev
    "source": [
     "output_notebook()"
    ]
@@ -397,20 +66,11 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#MODEL_RUN_PATH = \"libritts_360-half-October-31-2019_04+54PM-19d2f5f/\"\n",
-    "MODEL_RUN_PATH = \"libritts_360-half-September-28-2019_10+46AM-8565c50/\"\n",
-=======
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "MODEL_RUN_PATH = \"/media/erogol/data_ssd/Models/libri_tts/speaker_encoder/libritts_360-half-October-31-2019_04+54PM-19d2f5f/\"\n",
->>>>>>> dev
     "MODEL_PATH = MODEL_RUN_PATH + \"best_model.pth.tar\"\n",
     "CONFIG_PATH = MODEL_RUN_PATH + \"config.json\"\n",
     "\n",
@@ -420,81 +80,23 @@
     "\n",
     "# My multi speaker locations\n",
     "EMBED_PATH = \"/home/erogol/Data/Libri-TTS/train-clean-360-embed_128/\"\n",
-<<<<<<< HEAD
-    "AUDIO_PATH = \"datasets/LibriTTS/test-clean/\""
-=======
     "AUDIO_PATH = \"/home/erogol/Data/Libri-TTS/train-clean-360/\""
->>>>>>> dev
    ]
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "best_model.pth.tar\r\n",
-      "config.json\r\n",
-      "events.out.tfevents.1569660396.erogol-desktop\r\n"
-     ]
-    }
-   ],
-=======
    "execution_count": null,
    "metadata": {},
    "outputs": [],
->>>>>>> dev
    "source": [
     "!ls -1 $MODEL_RUN_PATH"
    ]
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " > Setting up Audio Processor...\n",
-      " | > sample_rate:16000\n",
-      " | > num_mels:40\n",
-      " | > min_level_db:-100\n",
-      " | > frame_shift_ms:12.5\n",
-      " | > frame_length_ms:50\n",
-      " | > ref_level_db:20\n",
-      " | > fft_size:1024\n",
-      " | > power:None\n",
-      " | > preemphasis:0.98\n",
-      " | > griffin_lim_iters:None\n",
-      " | > signal_norm:True\n",
-      " | > symmetric_norm:True\n",
-      " | > mel_fmin:0\n",
-      " | > mel_fmax:8000.0\n",
-      " | > spec_gain:20.0\n",
-      " | > stft_pad_mode:reflect\n",
-      " | > max_norm:4.0\n",
-      " | > clip_norm:True\n",
-      " | > do_trim_silence:False\n",
-      " | > trim_db:60\n",
-      " | > do_sound_norm:False\n",
-      " | > stats_path:None\n",
-      " | > hop_length:200\n",
-      " | > win_length:800\n"
-     ]
-    }
-   ],
-=======
    "execution_count": null,
    "metadata": {},
    "outputs": [],
->>>>>>> dev
    "source": [
     "CONFIG = load_config(CONFIG_PATH)\n",
     "ap = AudioProcessor(**CONFIG['audio'])"
@@ -509,23 +111,9 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Embeddings found: 0\n"
-     ]
-    }
-   ],
-=======
    "execution_count": null,
    "metadata": {},
    "outputs": [],
->>>>>>> dev
    "source": [
     "embed_files = glob.glob(EMBED_PATH+\"/**/*.npy\", recursive=True)\n",
     "print(f'Embeddings found: {len(embed_files)}')"
@@ -540,27 +128,9 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "list index out of range",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-f67d64b1abbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0membed_files\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
-     ]
-    }
-   ],
-=======
    "execution_count": null,
    "metadata": {},
    "outputs": [],
->>>>>>> dev
    "source": [
     "embed_files[0]"
    ]
@@ -576,23 +146,9 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Speaker count: 0\n"
-     ]
-    }
-   ],
-=======
    "execution_count": null,
    "metadata": {},
    "outputs": [],
->>>>>>> dev
    "source": [
     "speaker_paths = list(set([os.path.dirname(os.path.dirname(embed_file)) for embed_file in embed_files]))\n",
     "speaker_to_utter = {}\n",
@@ -616,32 +172,11 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "'a' cannot be empty unless no samples are taken",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-11-aabd2a5031f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mspeaker_idxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspeaker_paths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_speakers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mspeaker_num\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspeaker_idx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mspeaker_idxs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: 'a' cannot be empty unless no samples are taken"
-     ]
-    }
-   ],
-   "source": [
-    "ttsembeds = []\n",
-=======
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "embeds = []\n",
->>>>>>> dev
     "labels = []\n",
     "locations = []\n",
     "\n",
@@ -665,11 +200,7 @@
     "            embed = np.load(embed_path)\n",
     "            embeds.append(embed)\n",
     "            labels.append(str(speaker_num))\n",
-<<<<<<< HEAD
-    "            #locations.append(embed_path.replace(EMBED_PATH, '').replace('.npy','.wav'))\n",
-=======
     "            locations.append(embed_path.replace(EMBED_PATH, '').replace('.npy','.wav'))\n",
->>>>>>> dev
     "embeds = np.concatenate(embeds)"
    ]
   },
@@ -682,27 +213,9 @@
   },
   {
    "cell_type": "code",
-<<<<<<< HEAD
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "module 'umap' has no attribute 'UMAP'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-32709017067f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mumap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUMAP\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprojection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: module 'umap' has no attribute 'UMAP'"
-     ]
-    }
-   ],
-=======
    "execution_count": null,
    "metadata": {},
    "outputs": [],
->>>>>>> dev
    "source": [
     "model = umap.UMAP()\n",
     "projection = model.fit_transform(embeds)"
@@ -806,11 +319,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-<<<<<<< HEAD
-   "version": "3.8.5"
-=======
    "version": "3.7.4"
->>>>>>> dev
   }
  },
  "nbformat": 4,

tests/test_speaker_encoder.py

@@ -35,7 +35,7 @@ class LSTMSpeakerEncoderTests(unittest.TestCase):
         assert abs(assert_diff) < 1e-4, f" [!] output_norm has wrong values - {assert_diff}"
         # compute d for a given batch
         dummy_input = T.rand(1, 240, 80)  # B x T x D
-        output = model.compute_embedding(dummy_input, num_frames=160, overlap=0.5)
+        output = model.compute_embedding(dummy_input, num_frames=160, num_eval=5)
         assert output.shape[0] == 1
         assert output.shape[1] == 256
         assert len(output.shape) == 2