mirror of https://github.com/coqui-ai/TTS.git
132 lines
5.1 KiB
Python
132 lines
5.1 KiB
Python
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
|
|
|
|
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 --fomatter vctk --dataset_path /path/to/vctk/dataset --dataset_name my_vctk --metafile /path/to/vctk/metafile.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="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 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(
|
|
"--metafile",
|
|
type=str,
|
|
help="Path to the 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()
|
|
|
|
use_cuda = torch.cuda.is_available() and not args.disable_cuda
|
|
|
|
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
|
|
c_dataset.meta_file_train = args.metafile if args.metafile else None
|
|
meta_data_train, meta_data_eval = load_tts_samples(c_dataset, eval_split=not args.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=args.model_path,
|
|
encoder_config_path=args.config_path,
|
|
d_vectors_file_path=args.old_file,
|
|
use_cuda=use_cuda,
|
|
)
|
|
|
|
class_name_key = encoder_manager.encoder_config.class_name_key
|
|
|
|
# compute speaker embeddings
|
|
speaker_mapping = {}
|
|
for idx, fields in enumerate(tqdm(samples)):
|
|
class_name = fields[class_name_key]
|
|
audio_file = fields["audio_file"]
|
|
embedding_key = fields["audio_unique_name"]
|
|
root_path = fields["root_path"]
|
|
|
|
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(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(args.output_path):
|
|
mapping_file_path = os.path.join(args.output_path, "speakers.pth")
|
|
else:
|
|
mapping_file_path = args.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)
|