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import argparse
import os
from argparse import RawTextHelpFormatter
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
def compute_embeddings(
model_path,
config_path,
output_path,
old_speakers_file=None,
old_append=False,
config_dataset_path=None,
formatter_name=None,
dataset_name=None,
dataset_path=None,
meta_file_train=None,
meta_file_val=None,
disable_cuda=False,
no_eval=False,
):
use_cuda = torch.cuda.is_available() and not disable_cuda
if config_dataset_path is not None:
c_dataset = load_config(config_dataset_path)
meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=not no_eval)
else:
c_dataset = BaseDatasetConfig()
c_dataset.formatter = formatter_name
c_dataset.dataset_name = dataset_name
c_dataset.path = dataset_path
if meta_file_train is not None:
c_dataset.meta_file_train = meta_file_train
if meta_file_val is not None:
c_dataset.meta_file_val = meta_file_val
meta_data_train, meta_data_eval = load_tts_samples(c_dataset, eval_split=not no_eval)
if meta_data_eval is None:
samples = meta_data_train
else:
samples = meta_data_train + meta_data_eval
encoder_manager = SpeakerManager(
encoder_model_path=model_path,
encoder_config_path=config_path,
d_vectors_file_path=old_speakers_file,
use_cuda=use_cuda,
)
class_name_key = encoder_manager.encoder_config.class_name_key
# compute speaker embeddings
if old_speakers_file is not None and old_append:
speaker_mapping = encoder_manager.embeddings
else:
speaker_mapping = {}
for fields in tqdm(samples):
class_name = fields[class_name_key]
audio_file = fields["audio_file"]
embedding_key = fields["audio_unique_name"]
# Only update the speaker name when the embedding is already in the old file.
if embedding_key in speaker_mapping:
speaker_mapping[embedding_key]["name"] = class_name
continue
if old_speakers_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(embedding_key)
else:
# extract the embedding
embedd = encoder_manager.compute_embedding_from_clip(audio_file)
# create speaker_mapping if target dataset is defined
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
if os.path.isdir(output_path):
mapping_file_path = os.path.join(output_path, "speakers.pth")
else:
mapping_file_path = output_path
if os.path.dirname(mapping_file_path) != "":
os.makedirs(os.path.dirname(mapping_file_path), exist_ok=True)
save_file(speaker_mapping, mapping_file_path)
print("Speaker embeddings saved at:", mapping_file_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser(
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 --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 --formatter_name coqui --dataset_path /path/to/vctk/dataset --dataset_name my_vctk --meta_file_train /path/to/vctk/metafile_train.csv --meta_file_val /path/to/vctk/metafile_eval.csv
""",
formatter_class=RawTextHelpFormatter,
)
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="The old existing embedding file, from which the embeddings will be directly loaded for already computed audio clips.",
default=None,
)
parser.add_argument(
"--old_append",
help="Append new audio clip embeddings to the old embedding file, generate a new non-duplicated merged embedding file. Default False",
default=False,
action="store_true",
)
parser.add_argument("--disable_cuda", type=bool, help="Flag to disable cuda.", default=False)
parser.add_argument("--no_eval", help="Do not compute eval?. Default False", default=False, action="store_true")
parser.add_argument(
"--formatter_name",
type=str,
help="Name of the formatter to use. You either need to provide 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 provide this or `config_dataset_path`",
default=None,
)
parser.add_argument(
"--dataset_path",
type=str,
help="Path to the dataset. You either need to provide this or `config_dataset_path`",
default=None,
)
parser.add_argument(
"--meta_file_train",
type=str,
help="Path to the train meta file. If not set, dataset formatter uses the default metafile if it is defined in the formatter. You either need to provide this or `config_dataset_path`",
default=None,
)
parser.add_argument(
"--meta_file_val",
type=str,
help="Path to the evaluation meta file. If not set, dataset formatter uses the default metafile if it is defined in the formatter. You either need to provide this or `config_dataset_path`",
default=None,
)
args = parser.parse_args()
compute_embeddings(
args.model_path,
args.config_path,
args.output_path,
old_speakers_file=args.old_file,
old_append=args.old_append,
config_dataset_path=args.config_dataset_path,
formatter_name=args.formatter_name,
dataset_name=args.dataset_name,
dataset_path=args.dataset_path,
meta_file_train=args.meta_file_train,
meta_file_val=args.meta_file_val,
disable_cuda=args.disable_cuda,
no_eval=args.no_eval,
)
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