d-vector handling (#1945)

* Update BaseDatasetConfig

- Add dataset_name
- Chane name to formatter_name

* Update compute_embedding

- Allow entering dataset by args
- Use released model by default
- Use the new key format

* Update loading

* Update recipes

* Update other dep code

* Update tests

* Fixup

* Load multiple embedding files

* Fix argument names in dep code

* Update docs

* Fix argument name

* Fix linter

TTS/bin/compute_embeddings.py

@@ -6,38 +6,81 @@ import torch
 from tqdm import tqdm
 
 from TTS.config import load_config
+from TTS.config.shared_configs import BaseDatasetConfig
 from TTS.tts.datasets import load_tts_samples
 from TTS.tts.utils.managers import save_file
 from TTS.tts.utils.speakers import SpeakerManager
 
 parser = argparse.ArgumentParser(
-    description="""Compute embedding vectors for each wav file in a dataset.\n\n"""
+    description="""Compute embedding vectors for each audio file in a dataset and store them keyed by `{dataset_name}#{file_path}` in a .pth file\n\n"""
     """
     Example runs:
-    python TTS/bin/compute_embeddings.py speaker_encoder_model.pth speaker_encoder_config.json  dataset_config.json
+    python TTS/bin/compute_embeddings.py --model_path speaker_encoder_model.pth --config_path speaker_encoder_config.json  --config_dataset_path dataset_config.json
+
+    python TTS/bin/compute_embeddings.py --model_path speaker_encoder_model.pth --config_path speaker_encoder_config.json  --fomatter vctk --dataset_path /path/to/vctk/dataset --dataset_name my_vctk
     """,
     formatter_class=RawTextHelpFormatter,
 )
-parser.add_argument("model_path", type=str, help="Path to model checkpoint file.")
+parser.add_argument(
-parser.add_argument("config_path", type=str, help="Path to model config file.")
+    "--model_path",
-parser.add_argument("config_dataset_path", type=str, help="Path to dataset config file.")
+    type=str,
+    help="Path to model checkpoint file. It defaults to the released speaker encoder.",
+    default="https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/model_se.pth.tar",
+)
+parser.add_argument(
+    "--config_path",
+    type=str,
+    help="Path to model config file. It defaults to the released speaker encoder config.",
+    default="https://github.com/coqui-ai/TTS/releases/download/speaker_encoder_model/config_se.json",
+)
+parser.add_argument(
+    "--config_dataset_path",
+    type=str,
+    help="Path to dataset config file. You either need to provide this or `formatter_name`, `dataset_name` and `dataset_path` arguments.",
+    default=None,
+)
 parser.add_argument("--output_path", type=str, help="Path for output `pth` or `json` file.", default="speakers.pth")
 parser.add_argument("--old_file", type=str, help="Previous embedding file to only compute new audios.", default=None)
 parser.add_argument("--disable_cuda", type=bool, help="Flag to disable cuda.", default=False)
 parser.add_argument("--no_eval", type=bool, help="Do not compute eval?. Default False", default=False)
+parser.add_argument(
+    "--formatter_name",
+    type=str,
+    help="Name of the formatter to use. You either need to provicder this or `config_dataset_path`",
+    default=None,
+)
+parser.add_argument(
+    "--dataset_name",
+    type=str,
+    help="Name of the dataset to use. You either need to provicder this or `config_dataset_path`",
+    default=None,
+)
+parser.add_argument(
+    "--dataset_path",
+    type=str,
+    help="Path to the dataset. You either need to provicder this or `config_dataset_path`",
+    default=None,
+)
 
 args = parser.parse_args()
 
 use_cuda = torch.cuda.is_available() and not args.disable_cuda
 
-c_dataset = load_config(args.config_dataset_path)
+if args.config_dataset_path is not None:
+    c_dataset = load_config(args.config_dataset_path)
+    meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=not args.no_eval)
+else:
+    c_dataset = BaseDatasetConfig()
+    c_dataset.formatter = args.formatter_name
+    c_dataset.dataset_name = args.dataset_name
+    c_dataset.path = args.dataset_path
+    meta_data_train, meta_data_eval = load_tts_samples(c_dataset, eval_split=not args.no_eval)
 
-meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=not args.no_eval)
 
 if meta_data_eval is None:
-    wav_files = meta_data_train
+    samples = meta_data_train
 else:
-    wav_files = meta_data_train + meta_data_eval
+    samples = meta_data_train + meta_data_eval
 
 encoder_manager = SpeakerManager(
     encoder_model_path=args.model_path,
@@ -50,25 +93,25 @@ class_name_key = encoder_manager.encoder_config.class_name_key
 
 # compute speaker embeddings
 speaker_mapping = {}
-for idx, wav_file in enumerate(tqdm(wav_files)):
+for idx, fields in enumerate(tqdm(samples)):
-    if isinstance(wav_file, dict):
+    class_name = fields[class_name_key]
-        class_name = wav_file[class_name_key]
+    audio_file = fields["audio_file"]
-        wav_file = wav_file["audio_file"]
+    dataset_name = fields["dataset_name"]
-    else:
+    root_path = fields["root_path"]
-        class_name = None
 
-    wav_file_name = os.path.basename(wav_file)
+    relfilepath = os.path.splitext(audio_file.replace(root_path, ""))[0]
-    if args.old_file is not None and wav_file_name in encoder_manager.clip_ids:
+    embedding_key = f"{dataset_name}#{relfilepath}"
+    if args.old_file is not None and embedding_key in encoder_manager.clip_ids:
         # get the embedding from the old file
-        embedd = encoder_manager.get_embedding_by_clip(wav_file_name)
+        embedd = encoder_manager.get_embedding_by_clip(embedding_key)
     else:
         # extract the embedding
-        embedd = encoder_manager.compute_embedding_from_clip(wav_file)
+        embedd = encoder_manager.compute_embedding_from_clip(audio_file)
 
     # create speaker_mapping if target dataset is defined
-    speaker_mapping[wav_file_name] = {}
+    speaker_mapping[embedding_key] = {}
-    speaker_mapping[wav_file_name]["name"] = class_name
+    speaker_mapping[embedding_key]["name"] = class_name
-    speaker_mapping[wav_file_name]["embedding"] = embedd
+    speaker_mapping[embedding_key]["embedding"] = embedd
 
 if speaker_mapping:
     # save speaker_mapping if target dataset is defined

TTS/bin/extract_tts_spectrograms.py

@@ -37,7 +37,7 @@ def setup_loader(ap, r, verbose=False):
         precompute_num_workers=0,
         use_noise_augment=False,
         verbose=verbose,
-        speaker_id_mapping=speaker_manager.ids if c.use_speaker_embedding else None,
+        speaker_id_mapping=speaker_manager.name_to_id if c.use_speaker_embedding else None,
         d_vector_mapping=speaker_manager.embeddings if c.use_d_vector_file else None,
     )
 

TTS/bin/synthesize.py

@@ -323,7 +323,7 @@ If you don't specify any models, then it uses LJSpeech based English model.
         print(
             " > Available speaker ids: (Set --speaker_idx flag to one of these values to use the multi-speaker model."
         )
-        print(synthesizer.tts_model.speaker_manager.ids)
+        print(synthesizer.tts_model.speaker_manager.name_to_id)
         return
 
     # query langauge ids of a multi-lingual model.

TTS/config/shared_configs.py

@@ -193,21 +193,24 @@ class BaseDatasetConfig(Coqpit):
     """Base config for TTS datasets.
 
     Args:
-        name (str):
+        formatter (str):
-            Dataset name that defines the preprocessor in use. Defaults to None.
+            Formatter name that defines used formatter in ```TTS.tts.datasets.formatter```. Defaults to `""`.
+
+        dataset_name (str):
+            Unique name for the dataset. Defaults to `""`.
 
         path (str):
-            Root path to the dataset files. Defaults to None.
+            Root path to the dataset files. Defaults to `""`.
 
         meta_file_train (str):
             Name of the dataset meta file. Or a list of speakers to be ignored at training for multi-speaker datasets.
-            Defaults to None.
+            Defaults to `""`.
 
         ignored_speakers (List):
             List of speakers IDs that are not used at the training. Default None.
 
         language (str):
-            Language code of the dataset. If defined, it overrides `phoneme_language`. Defaults to None.
+            Language code of the dataset. If defined, it overrides `phoneme_language`. Defaults to `""`.
 
         meta_file_val (str):
             Name of the dataset meta file that defines the instances used at validation.
@@ -217,7 +220,8 @@ class BaseDatasetConfig(Coqpit):
             train the duration predictor.
     """
 
-    name: str = ""
+    formatter: str = ""
+    dataset_name: str = ""
     path: str = ""
     meta_file_train: str = ""
     ignored_speakers: List[str] = None
@@ -230,7 +234,7 @@ class BaseDatasetConfig(Coqpit):
     ):
         """Check config fields"""
         c = asdict(self)
-        check_argument("name", c, restricted=True)
+        check_argument("formatter", c, restricted=True)
         check_argument("path", c, restricted=True)
         check_argument("meta_file_train", c, restricted=True)
         check_argument("meta_file_val", c, restricted=False)

TTS/server/server.py

@@ -147,7 +147,7 @@ def index():
         "index.html",
         show_details=args.show_details,
         use_multi_speaker=use_multi_speaker,
-        speaker_ids=speaker_manager.ids if speaker_manager is not None else None,
+        speaker_ids=speaker_manager.name_to_id if speaker_manager is not None else None,
         use_gst=use_gst,
     )
 

TTS/tts/datasets/__init__.py

@@ -97,7 +97,8 @@ def load_tts_samples(
     if not isinstance(datasets, list):
         datasets = [datasets]
     for dataset in datasets:
-        name = dataset["name"]
+        formatter_name = dataset["formatter"]
+        dataset_name = dataset["dataset_name"]
         root_path = dataset["path"]
         meta_file_train = dataset["meta_file_train"]
         meta_file_val = dataset["meta_file_val"]
@@ -106,17 +107,18 @@ def load_tts_samples(
 
         # setup the right data processor
         if formatter is None:
-            formatter = _get_formatter_by_name(name)
+            formatter = _get_formatter_by_name(formatter_name)
         # load train set
         meta_data_train = formatter(root_path, meta_file_train, ignored_speakers=ignored_speakers)
-        meta_data_train = [{**item, **{"language": language}} for item in meta_data_train]
+        meta_data_train = [{**item, **{"language": language, "dataset_name": dataset_name}} for item in meta_data_train]
-
         print(f" | > Found {len(meta_data_train)} files in {Path(root_path).resolve()}")
         # load evaluation split if set
         if eval_split:
             if meta_file_val:
                 meta_data_eval = formatter(root_path, meta_file_val, ignored_speakers=ignored_speakers)
-                meta_data_eval = [{**item, **{"language": language}} for item in meta_data_eval]
+                meta_data_eval = [
+                    {**item, **{"language": language, "dataset_name": dataset_name}} for item in meta_data_eval
+                ]
             else:
                 meta_data_eval, meta_data_train = split_dataset(meta_data_train, eval_split_max_size, eval_split_size)
             meta_data_eval_all += meta_data_eval

TTS/tts/models/base_tts.py

@@ -144,11 +144,11 @@ class BaseTTS(BaseTrainerModel):
                 if speaker_name is None:
                     speaker_id = self.speaker_manager.get_random_id()
                 else:
-                    speaker_id = self.speaker_manager.ids[speaker_name]
+                    speaker_id = self.speaker_manager.name_to_id[speaker_name]
 
         # get language id
         if hasattr(self, "language_manager") and config.use_language_embedding and language_name is not None:
-            language_id = self.language_manager.ids[language_name]
+            language_id = self.language_manager.name_to_id[language_name]
 
         return {
             "text": text,
@@ -288,11 +288,13 @@ class BaseTTS(BaseTrainerModel):
             # setup multi-speaker attributes
             if hasattr(self, "speaker_manager") and self.speaker_manager is not None:
                 if hasattr(config, "model_args"):
-                    speaker_id_mapping = self.speaker_manager.ids if config.model_args.use_speaker_embedding else None
+                    speaker_id_mapping = (
+                        self.speaker_manager.name_to_id if config.model_args.use_speaker_embedding else None
+                    )
                     d_vector_mapping = self.speaker_manager.embeddings if config.model_args.use_d_vector_file else None
                     config.use_d_vector_file = config.model_args.use_d_vector_file
                 else:
-                    speaker_id_mapping = self.speaker_manager.ids if config.use_speaker_embedding else None
+                    speaker_id_mapping = self.speaker_manager.name_to_id if config.use_speaker_embedding else None
                     d_vector_mapping = self.speaker_manager.embeddings if config.use_d_vector_file else None
             else:
                 speaker_id_mapping = None
@@ -300,7 +302,7 @@ class BaseTTS(BaseTrainerModel):
 
             # setup multi-lingual attributes
             if hasattr(self, "language_manager") and self.language_manager is not None:
-                language_id_mapping = self.language_manager.ids if self.args.use_language_embedding else None
+                language_id_mapping = self.language_manager.name_to_id if self.args.use_language_embedding else None
             else:
                 language_id_mapping = None
 
@@ -363,7 +365,7 @@ class BaseTTS(BaseTrainerModel):
         aux_inputs = {
             "speaker_id": None
             if not self.config.use_speaker_embedding
-            else random.sample(sorted(self.speaker_manager.ids.values()), 1),
+            else random.sample(sorted(self.speaker_manager.name_to_id.values()), 1),
             "d_vector": d_vector,
             "style_wav": None,  # TODO: handle GST style input
         }

TTS/tts/models/vits.py

@@ -1185,7 +1185,6 @@ class Vits(BaseTTS):
         y_lengths = torch.tensor([y.size(-1)]).to(y.device)
         speaker_cond_src = reference_speaker_id if reference_speaker_id is not None else reference_d_vector
         speaker_cond_tgt = speaker_id if speaker_id is not None else d_vector
-        # print(y.shape, y_lengths.shape)
         wav, _, _ = self.voice_conversion(y, y_lengths, speaker_cond_src, speaker_cond_tgt)
         return wav
 
@@ -1402,11 +1401,11 @@ class Vits(BaseTTS):
                 if speaker_name is None:
                     speaker_id = self.speaker_manager.get_random_id()
                 else:
-                    speaker_id = self.speaker_manager.ids[speaker_name]
+                    speaker_id = self.speaker_manager.name_to_id[speaker_name]
 
         # get language id
         if hasattr(self, "language_manager") and config.use_language_embedding and language_name is not None:
-            language_id = self.language_manager.ids[language_name]
+            language_id = self.language_manager.name_to_id[language_name]
 
         return {
             "text": text,
@@ -1461,8 +1460,8 @@ class Vits(BaseTTS):
         d_vectors = None
 
         # get numerical speaker ids from speaker names
-        if self.speaker_manager is not None and self.speaker_manager.ids and self.args.use_speaker_embedding:
+        if self.speaker_manager is not None and self.speaker_manager.name_to_id and self.args.use_speaker_embedding:
-            speaker_ids = [self.speaker_manager.ids[sn] for sn in batch["speaker_names"]]
+            speaker_ids = [self.speaker_manager.name_to_id[sn] for sn in batch["speaker_names"]]
 
         if speaker_ids is not None:
             speaker_ids = torch.LongTensor(speaker_ids)
@@ -1475,8 +1474,8 @@ class Vits(BaseTTS):
             d_vectors = torch.FloatTensor(d_vectors)
 
         # get language ids from language names
-        if self.language_manager is not None and self.language_manager.ids and self.args.use_language_embedding:
+        if self.language_manager is not None and self.language_manager.name_to_id and self.args.use_language_embedding:
-            language_ids = [self.language_manager.ids[ln] for ln in batch["language_names"]]
+            language_ids = [self.language_manager.name_to_id[ln] for ln in batch["language_names"]]
 
         if language_ids is not None:
             language_ids = torch.LongTensor(language_ids)

TTS/tts/utils/languages.py

@@ -37,11 +37,11 @@ class LanguageManager(BaseIDManager):
 
     @property
     def num_languages(self) -> int:
-        return len(list(self.ids.keys()))
+        return len(list(self.name_to_id.keys()))
 
     @property
     def language_names(self) -> List:
-        return list(self.ids.keys())
+        return list(self.name_to_id.keys())
 
     @staticmethod
     def parse_language_ids_from_config(c: Coqpit) -> Dict:
@@ -67,7 +67,7 @@ class LanguageManager(BaseIDManager):
         Args:
             c (Coqpit): Config.
         """
-        self.ids = self.parse_language_ids_from_config(c)
+        self.name_to_id = self.parse_language_ids_from_config(c)
 
     @staticmethod
     def parse_ids_from_data(items: List, parse_key: str) -> Any:
@@ -82,7 +82,7 @@ class LanguageManager(BaseIDManager):
         Args:
             file_path (str): Path to the output file.
         """
-        self._save_json(file_path, self.ids)
+        self._save_json(file_path, self.name_to_id)
 
     @staticmethod
     def init_from_config(config: Coqpit) -> "LanguageManager":

TTS/tts/utils/managers.py

@@ -39,7 +39,7 @@ class BaseIDManager:
     """
 
     def __init__(self, id_file_path: str = ""):
-        self.ids = {}
+        self.name_to_id = {}
 
         if id_file_path:
             self.load_ids_from_file(id_file_path)
@@ -60,7 +60,7 @@ class BaseIDManager:
         Args:
             items (List): Data sampled returned by `load_tts_samples()`.
         """
-        self.ids = self.parse_ids_from_data(items, parse_key=parse_key)
+        self.name_to_id = self.parse_ids_from_data(items, parse_key=parse_key)
 
     def load_ids_from_file(self, file_path: str) -> None:
         """Set IDs from a file.
@@ -68,7 +68,7 @@ class BaseIDManager:
         Args:
             file_path (str): Path to the file.
         """
-        self.ids = load_file(file_path)
+        self.name_to_id = load_file(file_path)
 
     def save_ids_to_file(self, file_path: str) -> None:
         """Save IDs to a json file.
@@ -76,7 +76,7 @@ class BaseIDManager:
         Args:
             file_path (str): Path to the output file.
         """
-        save_file(self.ids, file_path)
+        save_file(self.name_to_id, file_path)
 
     def get_random_id(self) -> Any:
         """Get a random embedding.
@@ -86,8 +86,8 @@ class BaseIDManager:
         Returns:
             np.ndarray: embedding.
         """
-        if self.ids:
+        if self.name_to_id:
-            return self.ids[random.choices(list(self.ids.keys()))[0]]
+            return self.name_to_id[random.choices(list(self.name_to_id.keys()))[0]]
 
         return None
 
@@ -109,11 +109,27 @@ class BaseIDManager:
 class EmbeddingManager(BaseIDManager):
     """Base `Embedding` Manager class. Every new `Embedding` manager must inherit this.
     It defines common `Embedding` manager specific functions.
+
+    It expects embeddings files in the following format:
+
+    ::
+
+        {
+            'audio_file_key':{
+                'name': 'category_name',
+                'embedding'[<embedding_values>]
+            },
+            ...
+        }
+
+    `audio_file_key` is a unique key to the audio file in the dataset. It can be the path to the file or any other unique key.
+    `embedding` is the embedding vector of the audio file.
+    `name` can be name of the speaker of the audio file.
     """
 
     def __init__(
         self,
-        embedding_file_path: str = "",
+        embedding_file_path: Union[str, List[str]] = "",
         id_file_path: str = "",
         encoder_model_path: str = "",
         encoder_config_path: str = "",
@@ -129,11 +145,24 @@ class EmbeddingManager(BaseIDManager):
         self.use_cuda = use_cuda
 
         if embedding_file_path:
-            self.load_embeddings_from_file(embedding_file_path)
+            if isinstance(embedding_file_path, list):
+                self.load_embeddings_from_list_of_files(embedding_file_path)
+            else:
+                self.load_embeddings_from_file(embedding_file_path)
 
         if encoder_model_path and encoder_config_path:
             self.init_encoder(encoder_model_path, encoder_config_path, use_cuda)
 
+    @property
+    def num_embeddings(self):
+        """Get number of embeddings."""
+        return len(self.embeddings)
+
+    @property
+    def num_names(self):
+        """Get number of embeddings."""
+        return len(self.embeddings_by_names)
+
     @property
     def embedding_dim(self):
         """Dimensionality of embeddings. If embeddings are not loaded, returns zero."""
@@ -141,6 +170,11 @@ class EmbeddingManager(BaseIDManager):
             return len(self.embeddings[list(self.embeddings.keys())[0]]["embedding"])
         return 0
 
+    @property
+    def embedding_names(self):
+        """Get embedding names."""
+        return list(self.embeddings_by_names.keys())
+
     def save_embeddings_to_file(self, file_path: str) -> None:
         """Save embeddings to a json file.
 
@@ -149,20 +183,57 @@ class EmbeddingManager(BaseIDManager):
         """
         save_file(self.embeddings, file_path)
 
+    @staticmethod
+    def read_embeddings_from_file(file_path: str):
+        """Load embeddings from a json file.
+
+        Args:
+            file_path (str): Path to the file.
+        """
+        embeddings = load_file(file_path)
+        speakers = sorted({x["name"] for x in embeddings.values()})
+        name_to_id = {name: i for i, name in enumerate(speakers)}
+        clip_ids = list(set(sorted(clip_name for clip_name in embeddings.keys())))
+        # cache embeddings_by_names for fast inference using a bigger speakers.json
+        embeddings_by_names = {}
+        for x in embeddings.values():
+            if x["name"] not in embeddings_by_names.keys():
+                embeddings_by_names[x["name"]] = [x["embedding"]]
+            else:
+                embeddings_by_names[x["name"]].append(x["embedding"])
+        return name_to_id, clip_ids, embeddings, embeddings_by_names
+
     def load_embeddings_from_file(self, file_path: str) -> None:
         """Load embeddings from a json file.
 
         Args:
             file_path (str): Path to the target json file.
         """
-        self.embeddings = load_file(file_path)
+        self.name_to_id, self.clip_ids, self.embeddings, self.embeddings_by_names = self.read_embeddings_from_file(
+            file_path
+        )
 
-        speakers = sorted({x["name"] for x in self.embeddings.values()})
+    def load_embeddings_from_list_of_files(self, file_paths: List[str]) -> None:
-        self.ids = {name: i for i, name in enumerate(speakers)}
+        """Load embeddings from a list of json files and don't allow duplicate keys.
 
-        self.clip_ids = list(set(sorted(clip_name for clip_name in self.embeddings.keys())))
+        Args:
-        # cache embeddings_by_names for fast inference using a bigger speakers.json
+            file_paths (List[str]): List of paths to the target json files.
-        self.embeddings_by_names = self.get_embeddings_by_names()
+        """
+        self.name_to_id = {}
+        self.clip_ids = []
+        self.embeddings_by_names = {}
+        self.embeddings = {}
+        for file_path in file_paths:
+            ids, clip_ids, embeddings, embeddings_by_names = self.read_embeddings_from_file(file_path)
+            # check colliding keys
+            duplicates = set(self.embeddings.keys()) & set(embeddings.keys())
+            if duplicates:
+                raise ValueError(f" [!] Duplicate embedding names <{duplicates}> in {file_path}")
+            # store values
+            self.name_to_id.update(ids)
+            self.clip_ids.extend(clip_ids)
+            self.embeddings_by_names.update(embeddings_by_names)
+            self.embeddings.update(embeddings)
 
     def get_embedding_by_clip(self, clip_idx: str) -> List:
         """Get embedding by clip ID.

TTS/tts/utils/speakers.py

@@ -73,14 +73,14 @@ class SpeakerManager(EmbeddingManager):
 
     @property
     def num_speakers(self):
-        return len(self.ids)
+        return len(self.name_to_id)
 
     @property
     def speaker_names(self):
-        return list(self.ids.keys())
+        return list(self.name_to_id.keys())
 
     def get_speakers(self) -> List:
-        return self.ids
+        return self.name_to_id
 
     @staticmethod
     def init_from_config(config: "Coqpit", samples: Union[List[List], List[Dict]] = None) -> "SpeakerManager":
@@ -182,10 +182,10 @@ def get_speaker_manager(c: Coqpit, data: List = None, restore_path: str = None,
                     speaker_manager.load_embeddings_from_file(c.d_vector_file)
                 speaker_manager.load_embeddings_from_file(speakers_file)
             elif not c.use_d_vector_file:  # restor speaker manager with speaker ID file.
-                speaker_ids_from_data = speaker_manager.ids
+                speaker_ids_from_data = speaker_manager.name_to_id
                 speaker_manager.load_ids_from_file(speakers_file)
                 assert all(
-                    speaker in speaker_manager.ids for speaker in speaker_ids_from_data
+                    speaker in speaker_manager.name_to_id for speaker in speaker_ids_from_data
                 ), " [!] You cannot introduce new speakers to a pre-trained model."
         elif c.use_d_vector_file and c.d_vector_file:
             # new speaker manager with external speaker embeddings.
@@ -199,7 +199,7 @@ def get_speaker_manager(c: Coqpit, data: List = None, restore_path: str = None,
         if speaker_manager.num_speakers > 0:
             print(
                 " > Speaker manager is loaded with {} speakers: {}".format(
-                    speaker_manager.num_speakers, ", ".join(speaker_manager.ids)
+                    speaker_manager.num_speakers, ", ".join(speaker_manager.name_to_id)
                 )
             )
 

TTS/utils/synthesizer.py

@@ -212,7 +212,7 @@ class Synthesizer(object):
         # handle multi-speaker
         speaker_embedding = None
         speaker_id = None
-        if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "ids"):
+        if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "name_to_id"):
             if speaker_name and isinstance(speaker_name, str):
                 if self.tts_config.use_d_vector_file:
                     # get the average speaker embedding from the saved d_vectors.
@@ -222,7 +222,7 @@ class Synthesizer(object):
                     speaker_embedding = np.array(speaker_embedding)[None, :]  # [1 x embedding_dim]
                 else:
                     # get speaker idx from the speaker name
-                    speaker_id = self.tts_model.speaker_manager.ids[speaker_name]
+                    speaker_id = self.tts_model.speaker_manager.name_to_id[speaker_name]
 
             elif not speaker_name and not speaker_wav:
                 raise ValueError(
@@ -244,7 +244,7 @@ class Synthesizer(object):
             hasattr(self.tts_model, "language_manager") and self.tts_model.language_manager is not None
         ):
             if language_name and isinstance(language_name, str):
-                language_id = self.tts_model.language_manager.ids[language_name]
+                language_id = self.tts_model.language_manager.name_to_id[language_name]
 
             elif not language_name:
                 raise ValueError(
@@ -316,7 +316,7 @@ class Synthesizer(object):
             # get the speaker embedding or speaker id for the reference wav file
             reference_speaker_embedding = None
             reference_speaker_id = None
-            if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "ids"):
+            if self.tts_speakers_file or hasattr(self.tts_model.speaker_manager, "name_to_id"):
                 if reference_speaker_name and isinstance(reference_speaker_name, str):
                     if self.tts_config.use_d_vector_file:
                         # get the speaker embedding from the saved d_vectors.
@@ -328,12 +328,11 @@ class Synthesizer(object):
                         ]  # [1 x embedding_dim]
                     else:
                         # get speaker idx from the speaker name
-                        reference_speaker_id = self.tts_model.speaker_manager.ids[reference_speaker_name]
+                        reference_speaker_id = self.tts_model.speaker_manager.name_to_id[reference_speaker_name]
                 else:
                     reference_speaker_embedding = self.tts_model.speaker_manager.compute_embedding_from_clip(
                         reference_wav
                     )
-
             outputs = transfer_voice(
                 model=self.tts_model,
                 CONFIG=self.tts_config,

docs/source/faq.md

@@ -53,7 +53,7 @@ We tried to collect common issues and questions we receive about 🐸TTS. It is
         "mixed_precision": false,
         "output_path": "recipes/ljspeech/glow_tts/",
         "test_sentences": ["Test this sentence.", "This test sentence.", "Sentence this test."],
-        "datasets":[{"name": "ljspeech", "meta_file_train":"metadata.csv", "path": "recipes/ljspeech/LJSpeech-1.1/"}]
+        "datasets":[{"formatter": "ljspeech", "meta_file_train":"metadata.csv", "path": "recipes/ljspeech/LJSpeech-1.1/"}]
     }
     ```
 

docs/source/formatting_your_dataset.md

@@ -88,7 +88,7 @@ from TTS.tts.datasets import load_tts_samples
 
 # dataset config for one of the pre-defined datasets
 dataset_config = BaseDatasetConfig(
-    name="vctk", meta_file_train="", language="en-us", path="dataset-path")
+    formatter="vctk", meta_file_train="", language="en-us", path="dataset-path")
 )
 
 # load training samples

docs/source/tutorial_for_nervous_beginners.md

@@ -84,7 +84,7 @@ We still support running training from CLI like in the old days. The same traini
         "print_eval": true,
         "mixed_precision": false,
         "output_path": "recipes/ljspeech/glow_tts/",
-        "datasets":[{"name": "ljspeech", "meta_file_train":"metadata.csv", "path": "recipes/ljspeech/LJSpeech-1.1/"}]
+        "datasets":[{"formatter": "ljspeech", "meta_file_train":"metadata.csv", "path": "recipes/ljspeech/LJSpeech-1.1/"}]
     }
     ```
 
@@ -120,6 +120,3 @@ $ tts-server -h # see the help
 $ tts-server --list_models  # list the available models.
 ```
 ![server.gif](https://github.com/coqui-ai/TTS/raw/main/images/demo_server.gif)
-
-
-

recipes/blizzard2013/tacotron1-Capacitron/train_capacitron_t1.py

@@ -15,7 +15,7 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 data_path = "/srv/data/"
 
 # Using LJSpeech like dataset processing for the blizzard dataset
-dataset_config = BaseDatasetConfig(name="ljspeech", meta_file_train="metadata.csv", path=data_path)
+dataset_config = BaseDatasetConfig(formatter="ljspeech", meta_file_train="metadata.csv", path=data_path)
 
 audio_config = BaseAudioConfig(
     sample_rate=24000,

recipes/blizzard2013/tacotron2-Capacitron/train_capacitron_t2.py

@@ -16,7 +16,7 @@ data_path = "/srv/data/blizzard2013/segmented"
 
 # Using LJSpeech like dataset processing for the blizzard dataset
 dataset_config = BaseDatasetConfig(
-    name="ljspeech",
+    formatter="ljspeech",
     meta_file_train="metadata.csv",
     path=data_path,
 )

recipes/kokoro/tacotron2-DDC/tacotron2-DDC.json

@@ -1,7 +1,7 @@
 {
     "datasets": [
         {
-            "name": "kokoro",
+            "formatter": "kokoro",
             "path": "DEFINE THIS",
             "meta_file_train": "metadata.csv",
             "meta_file_val": null
@@ -119,7 +119,7 @@
         "phonemes": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
     },
     "use_speaker_embedding": false,
-    "use_gst": false,       			
+    "use_gst": false,
     "use_external_speaker_embedding_file": false,
     "external_speaker_embedding_file": "../../speakers-vctk-en.json"
 }

recipes/ljspeech/align_tts/train_aligntts.py

@@ -13,7 +13,7 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 
 # init configs
 dataset_config = BaseDatasetConfig(
-    name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
+    formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
 )
 config = AlignTTSConfig(
     batch_size=32,

recipes/ljspeech/fast_pitch/train_fast_pitch.py

@@ -14,7 +14,7 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 
 # init configs
 dataset_config = BaseDatasetConfig(
-    name="ljspeech",
+    formatter="ljspeech",
     meta_file_train="metadata.csv",
     # meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"),
     path=os.path.join(output_path, "../LJSpeech-1.1/"),

recipes/ljspeech/fast_speech/train_fast_speech.py

@@ -14,7 +14,7 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 
 # init configs
 dataset_config = BaseDatasetConfig(
-    name="ljspeech",
+    formatter="ljspeech",
     meta_file_train="metadata.csv",
     # meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"),
     path=os.path.join(output_path, "../LJSpeech-1.1/"),

recipes/ljspeech/glow_tts/train_glowtts.py

@@ -21,7 +21,7 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 # Set LJSpeech as our target dataset and define its path.
 # You can also use a simple Dict to define the dataset and pass it to your custom formatter.
 dataset_config = BaseDatasetConfig(
-    name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
+    formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
 )
 
 # INITIALIZE THE TRAINING CONFIGURATION

recipes/ljspeech/speedy_speech/train_speedy_speech.py

@@ -11,7 +11,7 @@ from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
 dataset_config = BaseDatasetConfig(
-    name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
+    formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
 )
 
 audio_config = BaseAudioConfig(

recipes/ljspeech/tacotron2-Capacitron/train_capacitron_t2.py

@@ -16,7 +16,7 @@ data_path = "/srv/data/"
 
 # Using LJSpeech like dataset processing for the blizzard dataset
 dataset_config = BaseDatasetConfig(
-    name="ljspeech",
+    formatter="ljspeech",
     meta_file_train="metadata.csv",
     path=data_path,
 )

recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py

@@ -16,7 +16,7 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 
 # init configs
 dataset_config = BaseDatasetConfig(
-    name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
+    formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
 )
 
 audio_config = BaseAudioConfig(

recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py

@@ -16,7 +16,7 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 
 # init configs
 dataset_config = BaseDatasetConfig(
-    name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
+    formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
 )
 
 audio_config = BaseAudioConfig(

recipes/ljspeech/vits_tts/train_vits.py

@@ -11,7 +11,7 @@ from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
 dataset_config = BaseDatasetConfig(
-    name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
+    formatter="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
 )
 audio_config = VitsAudioConfig(
     sample_rate=22050, win_length=1024, hop_length=256, num_mels=80, mel_fmin=0, mel_fmax=None

recipes/multilingual/vits_tts/train_vits_tts.py

@@ -17,7 +17,7 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 mailabs_path = "/home/julian/workspace/mailabs/**"
 dataset_paths = glob(mailabs_path)
 dataset_config = [
-    BaseDatasetConfig(name="mailabs", meta_file_train=None, path=path, language=path.split("/")[-1])
+    BaseDatasetConfig(formatter="mailabs", meta_file_train=None, path=path, language=path.split("/")[-1])
     for path in dataset_paths
 ]
 

recipes/thorsten_DE/align_tts/train_aligntts.py

@@ -14,7 +14,7 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 
 # init configs
 dataset_config = BaseDatasetConfig(
-    name="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
+    formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
 )
 
 # download dataset if not already present

recipes/thorsten_DE/glow_tts/train_glowtts.py

@@ -22,7 +22,7 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 # Set LJSpeech as our target dataset and define its path.
 # You can also use a simple Dict to define the dataset and pass it to your custom formatter.
 dataset_config = BaseDatasetConfig(
-    name="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
+    formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
 )
 
 # download dataset if not already present

recipes/thorsten_DE/speedy_speech/train_speedy_speech.py

@@ -12,7 +12,7 @@ from TTS.utils.downloaders import download_thorsten_de
 
 output_path = os.path.dirname(os.path.abspath(__file__))
 dataset_config = BaseDatasetConfig(
-    name="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
+    formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
 )
 
 # download dataset if not already present

recipes/thorsten_DE/tacotron2-DDC/train_tacotron_ddc.py

@@ -16,7 +16,7 @@ output_path = os.path.dirname(os.path.abspath(__file__))
 
 # init configs
 dataset_config = BaseDatasetConfig(
-    name="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
+    formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
 )
 
 # download dataset if not already present

recipes/thorsten_DE/vits_tts/train_vits.py

@@ -12,7 +12,7 @@ from TTS.utils.downloaders import download_thorsten_de
 
 output_path = os.path.dirname(os.path.abspath(__file__))
 dataset_config = BaseDatasetConfig(
-    name="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
+    formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
 )
 
 # download dataset if not already present

recipes/vctk/fast_pitch/train_fast_pitch.py

@@ -11,7 +11,7 @@ from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
+dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
 
 audio_config = BaseAudioConfig(
     sample_rate=22050,

recipes/vctk/fast_speech/train_fast_speech.py

@@ -11,7 +11,7 @@ from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
+dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
 
 audio_config = BaseAudioConfig(
     sample_rate=22050,

recipes/vctk/glow_tts/train_glow_tts.py

@@ -22,7 +22,7 @@ if not os.path.exists(dataset_path):
     download_vctk(dataset_path)
 
 # define dataset config
-dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=dataset_path)
+dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=dataset_path)
 
 # define audio config
 # ❗ resample the dataset externally using `TTS/bin/resample.py` and set `resample=False` for faster training

recipes/vctk/resnet_speaker_encoder/train_encoder.py

@@ -31,7 +31,7 @@ config = SpeakerEncoderConfig()
 
 #### DATASET CONFIG ####
 # The formatter need to return the key "speaker_name"  for the speaker encoder and the "emotion_name" for the emotion encoder
-dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", language="en-us", path=VCTK_PATH)
+dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", language="en-us", path=VCTK_PATH)
 
 # add the dataset to the config
 config.datasets = [dataset_config]

recipes/vctk/speedy_speech/train_speedy_speech.py

@@ -11,7 +11,7 @@ from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
+dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
 
 audio_config = BaseAudioConfig(
     sample_rate=22050,

recipes/vctk/tacotron-DDC/train_tacotron-DDC.py

@@ -12,7 +12,7 @@ from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
+dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
 
 audio_config = BaseAudioConfig(
     sample_rate=22050,

recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py

@@ -12,7 +12,7 @@ from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
+dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
 
 audio_config = BaseAudioConfig(
     sample_rate=22050,

recipes/vctk/tacotron2/train_tacotron2.py

@@ -12,7 +12,7 @@ from TTS.tts.utils.text.tokenizer import TTSTokenizer
 from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
-dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
+dataset_config = BaseDatasetConfig(formatter="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
 
 audio_config = BaseAudioConfig(
     sample_rate=22050,

recipes/vctk/vits/train_vits.py

@@ -12,7 +12,7 @@ from TTS.utils.audio import AudioProcessor
 
 output_path = os.path.dirname(os.path.abspath(__file__))
 dataset_config = BaseDatasetConfig(
-    name="vctk", meta_file_train="", language="en-us", path=os.path.join(output_path, "../VCTK/")
+    formatter="vctk", meta_file_train="", language="en-us", path=os.path.join(output_path, "../VCTK/")
 )
 
 

tests/__init__.py

@@ -42,7 +42,7 @@ def run_cli(command):
 
 
 def get_test_data_config():
-    return BaseDatasetConfig(name="ljspeech", path="tests/data/ljspeech/", meta_file_train="metadata.csv")
+    return BaseDatasetConfig(formatter="ljspeech", path="tests/data/ljspeech/", meta_file_train="metadata.csv")
 
 
 def assertHasAttr(test_obj, obj, intendedAttr):

tests/aux_tests/test_embedding_manager.py

@@ -0,0 +1,92 @@
+import os
+import unittest
+
+import numpy as np
+import torch
+
+from tests import get_tests_input_path
+from TTS.config import load_config
+from TTS.encoder.utils.generic_utils import setup_encoder_model
+from TTS.encoder.utils.io import save_checkpoint
+from TTS.tts.utils.managers import EmbeddingManager
+from TTS.utils.audio import AudioProcessor
+
+encoder_config_path = os.path.join(get_tests_input_path(), "test_speaker_encoder_config.json")
+encoder_model_path = os.path.join(get_tests_input_path(), "checkpoint_0.pth")
+sample_wav_path = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0001.wav")
+sample_wav_path2 = os.path.join(get_tests_input_path(), "../data/ljspeech/wavs/LJ001-0002.wav")
+embedding_file_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.json")
+embeddings_file_path2 = os.path.join(get_tests_input_path(), "../data/dummy_speakers2.json")
+embeddings_file_pth_path = os.path.join(get_tests_input_path(), "../data/dummy_speakers.pth")
+
+
+class EmbeddingManagerTest(unittest.TestCase):
+    """Test emEeddingManager for loading embedding files and computing embeddings from waveforms"""
+
+    @staticmethod
+    def test_speaker_embedding():
+        # load config
+        config = load_config(encoder_config_path)
+        config.audio.resample = True
+
+        # create a dummy speaker encoder
+        model = setup_encoder_model(config)
+        save_checkpoint(model, None, None, get_tests_input_path(), 0)
+
+        # load audio processor and speaker encoder
+        manager = EmbeddingManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path)
+
+        # load a sample audio and compute embedding
+        ap = AudioProcessor(**config.audio)
+        waveform = ap.load_wav(sample_wav_path)
+        mel = ap.melspectrogram(waveform)
+        embedding = manager.compute_embeddings(mel)
+        assert embedding.shape[1] == 256
+
+        # compute embedding directly from an input file
+        embedding = manager.compute_embedding_from_clip(sample_wav_path)
+        embedding2 = manager.compute_embedding_from_clip(sample_wav_path)
+        embedding = torch.FloatTensor(embedding)
+        embedding2 = torch.FloatTensor(embedding2)
+        assert embedding.shape[0] == 256
+        assert (embedding - embedding2).sum() == 0.0
+
+        # compute embedding from a list of wav files.
+        embedding3 = manager.compute_embedding_from_clip([sample_wav_path, sample_wav_path2])
+        embedding3 = torch.FloatTensor(embedding3)
+        assert embedding3.shape[0] == 256
+        assert (embedding - embedding3).sum() != 0.0
+
+        # remove dummy model
+        os.remove(encoder_model_path)
+
+    def test_embedding_file_processing(self):  # pylint: disable=no-self-use
+        manager = EmbeddingManager(embedding_file_path=embeddings_file_pth_path)
+        # test embedding querying
+        embedding = manager.get_embedding_by_clip(manager.clip_ids[0])
+        assert len(embedding) == 256
+        embeddings = manager.get_embeddings_by_name(manager.embedding_names[0])
+        assert len(embeddings[0]) == 256
+        embedding1 = manager.get_mean_embedding(manager.embedding_names[0], num_samples=2, randomize=True)
+        assert len(embedding1) == 256
+        embedding2 = manager.get_mean_embedding(manager.embedding_names[0], num_samples=2, randomize=False)
+        assert len(embedding2) == 256
+        assert np.sum(np.array(embedding1) - np.array(embedding2)) != 0
+
+    def test_embedding_file_loading(self):
+        # test loading a json file
+        manager = EmbeddingManager(embedding_file_path=embedding_file_path)
+        self.assertEqual(manager.num_embeddings, 384)
+        self.assertEqual(manager.embedding_dim, 256)
+        # test loading a pth file
+        manager = EmbeddingManager(embedding_file_path=embeddings_file_pth_path)
+        self.assertEqual(manager.num_embeddings, 384)
+        self.assertEqual(manager.embedding_dim, 256)
+        # test loading a pth files with duplicate embedding keys
+        with self.assertRaises(Exception) as context:
+            manager = EmbeddingManager(embedding_file_path=[embeddings_file_pth_path, embeddings_file_pth_path])
+        self.assertTrue("Duplicate embedding names" in str(context.exception))
+        # test loading embedding files with different embedding keys
+        manager = EmbeddingManager(embedding_file_path=[embeddings_file_pth_path, embeddings_file_path2])
+        self.assertEqual(manager.embedding_dim, 256)
+        self.assertEqual(manager.num_embeddings, 384 * 2)

tests/aux_tests/test_find_unique_phonemes.py

@@ -12,7 +12,7 @@ torch.manual_seed(1)
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 
 dataset_config_en = BaseDatasetConfig(
-    name="ljspeech",
+    formatter="ljspeech",
     meta_file_train="metadata.csv",
     meta_file_val="metadata.csv",
     path="tests/data/ljspeech",
@@ -21,7 +21,7 @@ dataset_config_en = BaseDatasetConfig(
 
 """
 dataset_config_pt = BaseDatasetConfig(
-    name="ljspeech",
+    formatter="ljspeech",
     meta_file_train="metadata.csv",
     meta_file_val="metadata.csv",
     path="tests/data/ljspeech",

tests/aux_tests/test_speaker_encoder_train.py

@@ -11,7 +11,7 @@ def run_test_train():
     command = (
         f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_encoder.py --config_path {config_path} "
         f"--coqpit.output_path {output_path} "
-        "--coqpit.datasets.0.name ljspeech_test "
+        "--coqpit.datasets.0.formatter ljspeech_test "
         "--coqpit.datasets.0.meta_file_train metadata.csv "
         "--coqpit.datasets.0.meta_file_val metadata.csv "
         "--coqpit.datasets.0.path tests/data/ljspeech "

tests/aux_tests/test_speaker_manager.py

@@ -59,7 +59,7 @@ class SpeakerManagerTest(unittest.TestCase):
         # remove dummy model
         os.remove(encoder_model_path)
 
-    def test_speakers_file_processing(self):
+    def test_dvector_file_processing(self):
         manager = SpeakerManager(d_vectors_file_path=d_vectors_file_path)
         self.assertEqual(manager.num_speakers, 1)
         self.assertEqual(manager.embedding_dim, 256)

tests/data_tests/test_loader.py

@@ -24,7 +24,7 @@ c.data_path = os.path.join(get_tests_data_path(), "ljspeech/")
 ok_ljspeech = os.path.exists(c.data_path)
 
 dataset_config = BaseDatasetConfig(
-    name="ljspeech_test",  # ljspeech_test to multi-speaker
+    formatter="ljspeech_test",  # ljspeech_test to multi-speaker
     meta_file_train="metadata.csv",
     meta_file_val=None,
     path=c.data_path,

tests/data_tests/test_samplers.py

@@ -15,7 +15,7 @@ from TTS.utils.samplers import BucketBatchSampler, PerfectBatchSampler
 torch.manual_seed(0)
 
 dataset_config_en = BaseDatasetConfig(
-    name="ljspeech",
+    formatter="ljspeech",
     meta_file_train="metadata.csv",
     meta_file_val="metadata.csv",
     path="tests/data/ljspeech",
@@ -23,7 +23,7 @@ dataset_config_en = BaseDatasetConfig(
 )
 
 dataset_config_pt = BaseDatasetConfig(
-    name="ljspeech",
+    formatter="ljspeech",
     meta_file_train="metadata.csv",
     meta_file_val="metadata.csv",
     path="tests/data/ljspeech",

tests/inputs/test_align_tts.json

@@ -148,7 +148,7 @@
     "datasets":   // List of datasets. They all merged and they get different speaker_ids.
     [
         {
-            "name": "ljspeech",
+            "formatter": "ljspeech",
             "path": "tests/data/ljspeech/",
             "meta_file_train": "metadata.csv",
             "meta_file_val": "metadata.csv",

tests/inputs/test_glow_tts.json

@@ -140,12 +140,10 @@
     "datasets":   // List of datasets. They all merged and they get different speaker_ids.
         [
             {
-                "name": "ljspeech",
+                "formatter": "ljspeech",
                 "path": "tests/data/ljspeech/",
                 "meta_file_train": "metadata.csv",
                 "meta_file_val": "metadata.csv"
             }
         ]
 }
-
-

tests/inputs/test_speedy_speech.json

@@ -145,7 +145,7 @@
     "datasets":   // List of datasets. They all merged and they get different speaker_ids.
     [
         {
-            "name": "ljspeech",
+            "formatter": "ljspeech",
             "path": "tests/data/ljspeech/",
             "meta_file_train": "metadata.csv",
             "meta_file_val": "metadata.csv",

tests/inputs/test_tacotron2_config.json

@@ -166,7 +166,7 @@
     "datasets":   // List of datasets. They all merged and they get different speaker_ids.
         [
             {
-                "name": "ljspeech",
+                "formatter": "ljspeech",
                 "path": "tests/data/ljspeech/",
                 "meta_file_train": "metadata.csv",
                 "meta_file_val": "metadata.csv"
@@ -174,4 +174,3 @@
         ]
 
 }
-

tests/inputs/test_tacotron_bd_config.json

@@ -166,7 +166,7 @@
     "datasets":   // List of datasets. They all merged and they get different speaker_ids.
         [
             {
-                "name": "ljspeech",
+                "formatter": "ljspeech",
                 "path": "tests/data/ljspeech/",
                 "meta_file_train": "metadata.csv",
                 "meta_file_val": "metadata.csv"
@@ -174,4 +174,3 @@
         ]
 
 }
-

tests/inputs/test_tacotron_config.json

@@ -166,7 +166,7 @@
     "datasets":   // List of datasets. They all merged and they get different speaker_ids.
         [
             {
-                "name": "ljspeech",
+                "formatter": "ljspeech",
                 "path": "tests/data/ljspeech/",
                 "meta_file_train": "metadata.csv",
                 "meta_file_val": "metadata.csv"
@@ -174,4 +174,3 @@
         ]
 
 }
-

tests/tts_tests/test_align_tts_train.py

@@ -39,7 +39,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech "
+    "--coqpit.datasets.0.formatter ljspeech "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_fast_pitch_speaker_emb_train.py

@@ -56,7 +56,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}'  python TTS/bin/train_tts.py --config_path {config_path}  "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech_test "
+    "--coqpit.datasets.0.formatter ljspeech_test "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_fast_pitch_train.py

@@ -56,7 +56,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}'  python TTS/bin/train_tts.py --config_path {config_path}  "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech "
+    "--coqpit.datasets.0.formatter ljspeech "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_glow_tts_d-vectors_train.py

@@ -43,7 +43,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech_test "
+    "--coqpit.datasets.0.formatter ljspeech_test "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_glow_tts_speaker_emb_train.py

@@ -40,7 +40,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech_test "
+    "--coqpit.datasets.0.formatter ljspeech_test "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_glow_tts_train.py

@@ -39,7 +39,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech "
+    "--coqpit.datasets.0.formatter ljspeech "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_speedy_speech_train.py

@@ -38,7 +38,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}'  python TTS/bin/train_tts.py --config_path {config_path}  "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech "
+    "--coqpit.datasets.0.formatter ljspeech "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_tacotron2_d-vectors_train.py

@@ -44,7 +44,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech_test "
+    "--coqpit.datasets.0.formatter ljspeech_test "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_tacotron2_speaker_emb_train.py

@@ -42,7 +42,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech_test "
+    "--coqpit.datasets.0.formatter ljspeech_test "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_tacotron2_train.py

@@ -39,7 +39,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech "
+    "--coqpit.datasets.0.formatter ljspeech "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_tacotron_train.py

@@ -39,7 +39,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech "
+    "--coqpit.datasets.0.formatter ljspeech "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_vits_d-vectors_train.py

@@ -43,7 +43,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech "
+    "--coqpit.datasets.0.formatter ljspeech "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_vits_multilingual_speaker_emb_train.py

@@ -14,7 +14,7 @@ output_path = os.path.join(get_tests_output_path(), "train_outputs")
 
 
 dataset_config_en = BaseDatasetConfig(
-    name="ljspeech",
+    formatter="ljspeech",
     meta_file_train="metadata.csv",
     meta_file_val="metadata.csv",
     path="tests/data/ljspeech",
@@ -22,7 +22,7 @@ dataset_config_en = BaseDatasetConfig(
 )
 
 dataset_config_pt = BaseDatasetConfig(
-    name="ljspeech",
+    formatter="ljspeech",
     meta_file_train="metadata.csv",
     meta_file_val="metadata.csv",
     path="tests/data/ljspeech",

tests/tts_tests/test_vits_multilingual_train-d_vectors.py

@@ -14,7 +14,7 @@ output_path = os.path.join(get_tests_output_path(), "train_outputs")
 
 
 dataset_config_en = BaseDatasetConfig(
-    name="ljspeech_test",
+    formatter="ljspeech_test",
     meta_file_train="metadata.csv",
     meta_file_val="metadata.csv",
     path="tests/data/ljspeech",
@@ -22,7 +22,7 @@ dataset_config_en = BaseDatasetConfig(
 )
 
 dataset_config_pt = BaseDatasetConfig(
-    name="ljspeech_test",
+    formatter="ljspeech_test",
     meta_file_train="metadata.csv",
     meta_file_val="metadata.csv",
     path="tests/data/ljspeech",

tests/tts_tests/test_vits_speaker_emb_train.py

@@ -47,7 +47,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech_test "
+    "--coqpit.datasets.0.formatter ljspeech_test "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/tts_tests/test_vits_train.py

@@ -38,7 +38,7 @@ config.save_json(config_path)
 command_train = (
     f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} "
     f"--coqpit.output_path {output_path} "
-    "--coqpit.datasets.0.name ljspeech "
+    "--coqpit.datasets.0.formatter ljspeech "
     "--coqpit.datasets.0.meta_file_train metadata.csv "
     "--coqpit.datasets.0.meta_file_val metadata.csv "
     "--coqpit.datasets.0.path tests/data/ljspeech "

tests/zoo_tests/test_models.py

@@ -38,7 +38,7 @@ def test_run_all_models():
                     language_manager = LanguageManager(language_ids_file_path=language_files[0])
                     language_id = language_manager.language_names[0]
 
-                speaker_id = list(speaker_manager.ids.keys())[0]
+                speaker_id = list(speaker_manager.name_to_id.keys())[0]
                 run_cli(
                     f"tts --model_name  {model_name} "
                     f'--text "This is an example." --out_path "{output_path}" --speaker_idx "{speaker_id}" --language_idx "{language_id}" '