Improve runtime of __parse_items() from O(|speakers|*|items|) to O(|items|)

TTS/speaker_encoder/config.json

@@ -1,6 +1,6 @@
 
 {
-    "run_name": "Model compatible to  CorentinJ/Real-Time-Voice-Cloning",
+    "run_name": "mueller91",
     "run_description": "train speaker encoder with voxceleb1, voxceleb2 and libriSpeech ",
     "audio":{
         // Audio processing parameters
@@ -41,7 +41,7 @@
     "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": "../../checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
+    "output_path": "../../MozillaTTSOutput/checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
     "model": {
         "input_dim": 40,
         "proj_dim": 256,
@@ -52,8 +52,38 @@
     "datasets": 
         [
             {
-                "name": "vctk",
+                "name": "voxceleb1",
-                "path": "../../../datasets/VCTK-Corpus-removed-silence/",
+                "path": "../../audio-datasets/en/voxceleb1/",
+                "meta_file_train": null,
+                "meta_file_val": null
+            },
+//            {
+//                "name": "voxceleb2",
+//                "path": "../../audio-datasets/en/voxceleb2/",
+//                "meta_file_train": null,
+//                "meta_file_val": null
+//            },
+//            {
+//                "name": "vctk",
+//                "path": "../../audio-datasets/en/VCTK-Corpus/",
+//                "meta_file_train": null,
+//                "meta_file_val": null
+//            },
+//            {
+//                "name": "libri_tts",
+//                "path": "../../audio-datasets/en/LibriTTS/train-clean-100",
+//                "meta_file_train": null,
+//                "meta_file_val": null
+//            },
+//            {
+//                "name": "libri_tts",
+//                "path": "../../audio-datasets/en/LibriTTS/train-clean-360",
+//                "meta_file_train": null,
+//                "meta_file_val": null
+//            },
+            {
+                "name": "libri_tts",
+                "path": "../../audio-datasets/en/LibriTTS/train-other-500",
                 "meta_file_train": null,
                 "meta_file_val": null
             }

TTS/speaker_encoder/dataset.py

@@ -2,6 +2,7 @@ import numpy as np
 import torch
 import random
 from torch.utils.data import Dataset
+from tqdm import tqdm
 
 
 class MyDataset(Dataset):
@@ -53,6 +54,7 @@ class MyDataset(Dataset):
     def __parse_items(self):
         self.speaker_to_utters = {}
         for i in self.items:
+            text_ = i[0]
             path_ = i[1]
             speaker_ = i[2]
             if speaker_ in self.speaker_to_utters.keys():
@@ -60,11 +62,11 @@ class MyDataset(Dataset):
             else:
                 self.speaker_to_utters[speaker_] = [path_, ]
 
-            if self.skip_speakers:
+        if self.skip_speakers:
-                self.speaker_to_utters = {k: v for (k, v) in self.speaker_to_utters.items() if
+            self.speaker_to_utters = {k: v for (k, v) in self.speaker_to_utters.items() if
-                                          len(v) >= self.num_utter_per_speaker}
+                                      len(v) >= self.num_utter_per_speaker}
 
-            self.speakers = [k for (k, v) in self.speaker_to_utters]
+        self.speakers = [k for (k, v) in self.speaker_to_utters.items()]
 
     # def __parse_items(self):
     #     """

TTS/tts/datasets/preprocess.py

@@ -2,6 +2,10 @@ import os
 from glob import glob
 import re
 import sys
+from pathlib import Path
+
+from tqdm import tqdm
+
 from TTS.tts.utils.generic_utils import split_dataset
 
 
@@ -16,6 +20,7 @@ def load_meta_data(datasets):
         preprocessor = get_preprocessor_by_name(name)
 
         meta_data_train = preprocessor(root_path, meta_file_train)
+        print(f"Found {len(meta_data_train)} files in {Path(root_path).absolute()}")
         if meta_file_val is None:
             meta_data_eval, meta_data_train = split_dataset(meta_data_train)
         else:
@@ -187,7 +192,7 @@ def libri_tts(root_path, meta_files=None):
                 cols = line.split('\t')
                 wav_file = os.path.join(_root_path, cols[0] + '.wav')
                 text = cols[1]
-                items.append([text, wav_file, speaker_name])
+                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]}"
@@ -235,8 +240,7 @@ def vctk(root_path, meta_files=None, wavs_path='wav48'):
     """homepages.inf.ed.ac.uk/jyamagis/release/VCTK-Corpus.tar.gz"""
     test_speakers = meta_files
     items = []
-    meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt",
+    meta_files = glob(f"{os.path.join(root_path,'txt')}/**/*.txt", recursive=True)
-                      recursive=True)
     for meta_file in meta_files:
         _, speaker_id, txt_file = os.path.relpath(meta_file,
                                                   root_path).split(os.sep)
@@ -247,8 +251,50 @@ def vctk(root_path, meta_files=None, wavs_path='wav48'):
                 continue
         with open(meta_file) as file_text:
             text = file_text.readlines()[0]
-        wav_file = os.path.join(root_path, wavs_path, speaker_id,
+        wav_file = os.path.join(root_path, wavs_path, 'VCTK_' + speaker_id,
                                 file_id + '.wav')
         items.append([text, wav_file, speaker_id])
 
     return items
+
+# ======================================== VOX CELEB ===========================================
+def voxceleb2(root_path, meta_file):
+    """
+    :param meta_file   Used only for consistency with load_meta_data api
+    """
+    return _voxcel_x(root_path, voxcel_idx="2")
+
+
+def voxceleb1(root_path, meta_file):
+    """
+    :param meta_file   Used only for consistency with load_meta_data api
+    """
+    return _voxcel_x(root_path, voxcel_idx="1")
+
+
+def _voxcel_x(root_path, voxcel_idx):
+    assert voxcel_idx in ["1", "2"]
+    expected_count = 148_000 if voxcel_idx == "1" else 1_000_000
+    voxceleb_path = Path(root_path)
+    cache_to = voxceleb_path / f"metafile_voxceleb{voxcel_idx}.csv"
+    cache_to.parent.mkdir(exist_ok=True)
+
+    # if not exists meta file, crawl recursively for 'wav' files
+    if not cache_to.exists():
+        cnt = 0
+        meta_data = ""
+        wav_files = voxceleb_path.rglob("**/*.wav")
+        for path in tqdm(wav_files, desc=f"Building VoxCeleb {voxcel_idx} Meta file ... this needs to be done only once.",
+                         total=expected_count):
+            speaker_id = str(Path(path).parent.parent.stem)
+            assert speaker_id.startswith('id')
+            text = None  # VoxCel does not provide transciptions, and they are not needed for training the SE
+            meta_data += f"{text}|{path}|voxcel{voxcel_idx}_{speaker_id}\n"
+            cnt += 1
+        with open(str(cache_to), 'w') as f:
+            f.write(meta_data)
+        if cnt < expected_count:
+            raise ValueError(f"Found too few instances for Voxceleb. Should be around {expected_count}, is: {cnt}")
+
+    with open(str(cache_to), 'r') as f:
+        return [x.strip().split('|') for x in f.readlines()]