Medix-AI/coqui-tts
Medix-AI
/
coqui-tts
mirror of https://github.com/coqui-ai/TTS.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

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
This commit is contained in:
Eren Gölge 2022-09-13 14:10:33 +02:00 committed by GitHub
parent 371772c355
commit 9e5a469c64
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
73 changed files with 100582 additions and 152 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

87
TTS/bin/compute_embeddings.py
View File

@ -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("config_path", type=str, help="Path to model config file.")
parser.add_argument("config_dataset_path", type=str, help="Path to dataset config file.")
parser.add_argument(
"--model_path",
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)):
if isinstance(wav_file, dict):
class_name = wav_file[class_name_key]
wav_file = wav_file["audio_file"]
else:
class_name = None
for idx, fields in enumerate(tqdm(samples)):
class_name = fields[class_name_key]
audio_file = fields["audio_file"]
dataset_name = fields["dataset_name"]
root_path = fields["root_path"]
wav_file_name = os.path.basename(wav_file)
if args.old_file is not None and wav_file_name in encoder_manager.clip_ids:
relfilepath = os.path.splitext(audio_file.replace(root_path, ""))[0]
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[wav_file_name]["name"] = class_name
speaker_mapping[wav_file_name]["embedding"] = embedd
speaker_mapping[embedding_key] = {}
speaker_mapping[embedding_key]["name"] = class_name
speaker_mapping[embedding_key]["embedding"] = embedd
if speaker_mapping:
# save speaker_mapping if target dataset is defined

2
TTS/bin/extract_tts_spectrograms.py
View File

@ -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,
)

2
TTS/bin/synthesize.py
View File

@ -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.

18
TTS/config/shared_configs.py
View File

@ -193,21 +193,24 @@ class BaseDatasetConfig(Coqpit):
"""Base config for TTS datasets.
Args:
name (str):
Dataset name that defines the preprocessor in use. Defaults to None.
formatter (str):
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)

2
TTS/server/server.py
View File

@ -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,
)

12
TTS/tts/datasets/__init__.py
View File

@ -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

14
TTS/tts/models/base_tts.py
View File

@ -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
}

13
TTS/tts/models/vits.py
View File

@ -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:
speaker_ids = [self.speaker_manager.ids[sn] for sn in batch["speaker_names"]]
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.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:
language_ids = [self.language_manager.ids[ln] for ln in batch["language_names"]]
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.name_to_id[ln] for ln in batch["language_names"]]
if language_ids is not None:
language_ids = torch.LongTensor(language_ids)

8
TTS/tts/utils/languages.py
View File

@ -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":

99
TTS/tts/utils/managers.py
View File

@ -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:
return self.ids[random.choices(list(self.ids.keys()))[0]]
if self.name_to_id:
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()})
self.ids = {name: i for i, name in enumerate(speakers)}
def load_embeddings_from_list_of_files(self, file_paths: List[str]) -> None:
"""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())))
# cache embeddings_by_names for fast inference using a bigger speakers.json
self.embeddings_by_names = self.get_embeddings_by_names()
Args:
file_paths (List[str]): List of paths to the target json files.
"""
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.

12
TTS/tts/utils/speakers.py
View File

@ -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)
)
)

11
TTS/utils/synthesizer.py
View File

@ -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,

2
docs/source/faq.md
View File

@ -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/"}]
}
```

2
docs/source/formatting_your_dataset.md
View File

@ -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

5
docs/source/tutorial_for_nervous_beginners.md
View File

@ -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)

2
recipes/blizzard2013/tacotron1-Capacitron/train_capacitron_t1.py
View File

@ -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,

2
recipes/blizzard2013/tacotron2-Capacitron/train_capacitron_t2.py
View File

@ -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,
)

4
recipes/kokoro/tacotron2-DDC/tacotron2-DDC.json
View File

@ -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"
}

2
recipes/ljspeech/align_tts/train_aligntts.py
View File

@ -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,

2
recipes/ljspeech/fast_pitch/train_fast_pitch.py
View File

@ -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/"),

2
recipes/ljspeech/fast_speech/train_fast_speech.py
View File

@ -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/"),

2
recipes/ljspeech/glow_tts/train_glowtts.py
View File

@ -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

2
recipes/ljspeech/speedy_speech/train_speedy_speech.py
View File

@ -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(

2
recipes/ljspeech/tacotron2-Capacitron/train_capacitron_t2.py
View File

@ -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,
)

2
recipes/ljspeech/tacotron2-DCA/train_tacotron_dca.py
View File

@ -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(

2
recipes/ljspeech/tacotron2-DDC/train_tacotron_ddc.py
View File

@ -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(

2
recipes/ljspeech/vits_tts/train_vits.py
View File

@ -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

2
recipes/multilingual/vits_tts/train_vits_tts.py
View File

@ -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
]

2
recipes/thorsten_DE/align_tts/train_aligntts.py
View File

@ -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

2
recipes/thorsten_DE/glow_tts/train_glowtts.py
View File

@ -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

2
recipes/thorsten_DE/speedy_speech/train_speedy_speech.py
View File

@ -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

2
recipes/thorsten_DE/tacotron2-DDC/train_tacotron_ddc.py
View File

@ -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

2
recipes/thorsten_DE/vits_tts/train_vits.py
View File

@ -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

2
recipes/vctk/fast_pitch/train_fast_pitch.py
View File

@ -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,

2
recipes/vctk/fast_speech/train_fast_speech.py
View File

@ -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,

2
recipes/vctk/glow_tts/train_glow_tts.py
View File

@ -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

2
recipes/vctk/resnet_speaker_encoder/train_encoder.py
View File

@ -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]

2
recipes/vctk/speedy_speech/train_speedy_speech.py
View File

@ -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,

2
recipes/vctk/tacotron-DDC/train_tacotron-DDC.py
View File

@ -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,

2
recipes/vctk/tacotron2-DDC/train_tacotron2-ddc.py
View File

@ -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,

2
recipes/vctk/tacotron2/train_tacotron2.py
View File

@ -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,

2
recipes/vctk/vits/train_vits.py
View File

@ -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/")
)

2
tests/__init__.py
View File

@ -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):

92
tests/aux_tests/test_embedding_manager.py Normal file
View File

@ -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)

4
tests/aux_tests/test_find_unique_phonemes.py
View File

@ -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",

2
tests/aux_tests/test_speaker_encoder_train.py
View File

@ -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 "

2
tests/aux_tests/test_speaker_manager.py
View File

@ -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)

100226
tests/data/dummy_speakers2.json Normal file
View File

File diff suppressed because it is too large Load Diff

2
tests/data_tests/test_loader.py
View File

@ -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,

4
tests/data_tests/test_samplers.py
View File

@ -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",

2
tests/inputs/test_align_tts.json
View File

@ -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",

4
tests/inputs/test_glow_tts.json
View File

@ -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"
}
]
}

2
tests/inputs/test_speedy_speech.json
View File

@ -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",

3
tests/inputs/test_tacotron2_config.json
View File

@ -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 @@
]
}

3
tests/inputs/test_tacotron_bd_config.json
View File

@ -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 @@
]
}

3
tests/inputs/test_tacotron_config.json
View File

@ -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 @@
]
}

2
tests/tts_tests/test_align_tts_train.py
View File

@ -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 "

2
tests/tts_tests/test_fast_pitch_speaker_emb_train.py
View File

@ -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 "

2
tests/tts_tests/test_fast_pitch_train.py
View File

@ -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 "

2
tests/tts_tests/test_glow_tts_d-vectors_train.py
View File

@ -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 "

2
tests/tts_tests/test_glow_tts_speaker_emb_train.py
View File

@ -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 "

2
tests/tts_tests/test_glow_tts_train.py
View File

@ -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 "

2
tests/tts_tests/test_speedy_speech_train.py
View File

@ -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 "

2
tests/tts_tests/test_tacotron2_d-vectors_train.py
View File

@ -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 "

2
tests/tts_tests/test_tacotron2_speaker_emb_train.py
View File

@ -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 "

2
tests/tts_tests/test_tacotron2_train.py
View File

@ -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 "

2
tests/tts_tests/test_tacotron_train.py
View File

@ -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 "

2
tests/tts_tests/test_vits_d-vectors_train.py
View File

@ -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 "

4
tests/tts_tests/test_vits_multilingual_speaker_emb_train.py
View File

@ -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",

4
tests/tts_tests/test_vits_multilingual_train-d_vectors.py
View File

@ -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",

2
tests/tts_tests/test_vits_speaker_emb_train.py
View File

@ -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 "

2
tests/tts_tests/test_vits_train.py
View File

@ -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 "

2
tests/zoo_tests/test_models.py
View File

@ -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}" '