mirror of https://github.com/coqui-ai/TTS.git
format speaker encoder imports
This commit is contained in:
erogol
parent
e097bc6c5d
commit
154f90bc44
|
|
@ -9,20 +9,19 @@ import traceback
|
|||
|
||||
import torch
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from TTS.speaker_encoder.dataset import MyDataset
|
||||
from TTS.speaker_encoder.losses import GE2ELoss, AngleProtoLoss
|
||||
from TTS.speaker_encoder.losses import AngleProtoLoss, GE2ELoss
|
||||
from TTS.speaker_encoder.model import SpeakerEncoder
|
||||
from TTS.speaker_encoder.utils import check_config_speaker_encoder
|
||||
from TTS.speaker_encoder.visuals import plot_embeddings
|
||||
from TTS.speaker_encoder.utils.generic_utils import \
|
||||
check_config_speaker_encoder
|
||||
from TTS.speaker_encoder.utils.visual import plot_embeddings
|
||||
from TTS.tts.datasets.preprocess import load_meta_data
|
||||
from TTS.tts.utils.io import save_best_model
|
||||
from TTS.utils.generic_utils import (
|
||||
create_experiment_folder, get_git_branch, remove_experiment_folder,
|
||||
set_init_dict)
|
||||
from TTS.utils.io import copy_config_file, load_config
|
||||
from TTS.utils.audio import AudioProcessor
|
||||
from TTS.utils.generic_utils import count_parameters
|
||||
from TTS.utils.generic_utils import (count_parameters,
|
||||
create_experiment_folder, get_git_branch,
|
||||
remove_experiment_folder, set_init_dict)
|
||||
from TTS.utils.io import copy_config_file, load_config
|
||||
from TTS.utils.radam import RAdam
|
||||
from TTS.utils.tensorboard_logger import TensorboardLogger
|
||||
from TTS.utils.training import NoamLR, check_update
|
||||
|
|
|
|||
|
|
@ -1,88 +0,0 @@
|
|||
import argparse
|
||||
import glob
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
from tqdm import tqdm
|
||||
|
||||
import torch
|
||||
from TTS.speaker_encoder.model import SpeakerEncoder
|
||||
from TTS.utils.audio import AudioProcessor
|
||||
from TTS.utils.io import load_config
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
description='Compute embedding vectors for each wav file in a dataset. ')
|
||||
parser.add_argument(
|
||||
'model_path',
|
||||
type=str,
|
||||
help='Path to model outputs (checkpoint, tensorboard etc.).')
|
||||
parser.add_argument(
|
||||
'config_path',
|
||||
type=str,
|
||||
help='Path to config file for training.',
|
||||
)
|
||||
parser.add_argument(
|
||||
'data_path',
|
||||
type=str,
|
||||
help='Data path for wav files - directory or CSV file')
|
||||
parser.add_argument(
|
||||
'output_path',
|
||||
type=str,
|
||||
help='path for training outputs.')
|
||||
parser.add_argument(
|
||||
'--use_cuda', type=bool, help='flag to set cuda.', default=False
|
||||
)
|
||||
parser.add_argument(
|
||||
'--separator', type=str, help='Separator used in file if CSV is passed for data_path', default='|'
|
||||
)
|
||||
args = parser.parse_args()
|
||||
|
||||
|
||||
c = load_config(args.config_path)
|
||||
ap = AudioProcessor(**c['audio'])
|
||||
|
||||
data_path = args.data_path
|
||||
split_ext = os.path.splitext(data_path)
|
||||
sep = args.separator
|
||||
|
||||
if len(split_ext) > 0 and split_ext[1].lower() == '.csv':
|
||||
# Parse CSV
|
||||
print(f'CSV file: {data_path}')
|
||||
with open(data_path) as f:
|
||||
wav_path = os.path.join(os.path.dirname(data_path), 'wavs')
|
||||
wav_files = []
|
||||
print(f'Separator is: {sep}')
|
||||
for line in f:
|
||||
components = line.split(sep)
|
||||
if len(components) != 2:
|
||||
print("Invalid line")
|
||||
continue
|
||||
wav_file = os.path.join(wav_path, components[0] + '.wav')
|
||||
#print(f'wav_file: {wav_file}')
|
||||
if os.path.exists(wav_file):
|
||||
wav_files.append(wav_file)
|
||||
print(f'Count of wavs imported: {len(wav_files)}')
|
||||
else:
|
||||
# Parse all wav files in data_path
|
||||
wav_path = data_path
|
||||
wav_files = glob.glob(data_path + '/**/*.wav', recursive=True)
|
||||
|
||||
output_files = [wav_file.replace(wav_path, args.output_path).replace(
|
||||
'.wav', '.npy') for wav_file in wav_files]
|
||||
|
||||
for output_file in output_files:
|
||||
os.makedirs(os.path.dirname(output_file), exist_ok=True)
|
||||
|
||||
model = SpeakerEncoder(**c.model)
|
||||
model.load_state_dict(torch.load(args.model_path)['model'])
|
||||
model.eval()
|
||||
if args.use_cuda:
|
||||
model.cuda()
|
||||
|
||||
for idx, wav_file in enumerate(tqdm(wav_files)):
|
||||
mel_spec = ap.melspectrogram(ap.load_wav(wav_file, sr=ap.sample_rate)).T
|
||||
mel_spec = torch.FloatTensor(mel_spec[None, :, :])
|
||||
if args.use_cuda:
|
||||
mel_spec = mel_spec.cuda()
|
||||
embedd = model.compute_embedding(mel_spec)
|
||||
np.save(output_files[idx], embedd.detach().cpu().numpy())
|
||||
|
|
@ -1,61 +0,0 @@
|
|||
from TTS.utils.generic_utils import check_argument
|
||||
|
||||
|
||||
def check_config_speaker_encoder(c):
|
||||
"""Check the config.json file of the speaker encoder"""
|
||||
check_argument('run_name', c, restricted=True, val_type=str)
|
||||
check_argument('run_description', c, val_type=str)
|
||||
|
||||
# audio processing parameters
|
||||
check_argument('audio', c, restricted=True, val_type=dict)
|
||||
check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056)
|
||||
check_argument('fft_size', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
|
||||
check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000)
|
||||
check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000, alternative='win_length')
|
||||
check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000, alternative='hop_length')
|
||||
check_argument('preemphasis', c['audio'], restricted=True, val_type=float, min_val=0, max_val=1)
|
||||
check_argument('min_level_db', c['audio'], restricted=True, val_type=int, min_val=-1000, max_val=10)
|
||||
check_argument('ref_level_db', c['audio'], restricted=True, val_type=int, min_val=0, max_val=1000)
|
||||
check_argument('power', c['audio'], restricted=True, val_type=float, min_val=1, max_val=5)
|
||||
check_argument('griffin_lim_iters', c['audio'], restricted=True, val_type=int, min_val=10, max_val=1000)
|
||||
|
||||
# training parameters
|
||||
check_argument('loss', c, enum_list=['ge2e', 'angleproto'], restricted=True, val_type=str)
|
||||
check_argument('grad_clip', c, restricted=True, val_type=float)
|
||||
check_argument('epochs', c, restricted=True, val_type=int, min_val=1)
|
||||
check_argument('lr', c, restricted=True, val_type=float, min_val=0)
|
||||
check_argument('lr_decay', c, restricted=True, val_type=bool)
|
||||
check_argument('warmup_steps', c, restricted=True, val_type=int, min_val=0)
|
||||
check_argument('tb_model_param_stats', c, restricted=True, val_type=bool)
|
||||
check_argument('num_speakers_in_batch', c, restricted=True, val_type=int)
|
||||
check_argument('num_loader_workers', c, restricted=True, val_type=int)
|
||||
check_argument('wd', c, restricted=True, val_type=float, min_val=0.0, max_val=1.0)
|
||||
|
||||
# checkpoint and output parameters
|
||||
check_argument('steps_plot_stats', c, restricted=True, val_type=int)
|
||||
check_argument('checkpoint', c, restricted=True, val_type=bool)
|
||||
check_argument('save_step', c, restricted=True, val_type=int)
|
||||
check_argument('print_step', c, restricted=True, val_type=int)
|
||||
check_argument('output_path', c, restricted=True, val_type=str)
|
||||
|
||||
# model parameters
|
||||
check_argument('model', c, restricted=True, val_type=dict)
|
||||
check_argument('input_dim', c['model'], restricted=True, val_type=int)
|
||||
check_argument('proj_dim', c['model'], restricted=True, val_type=int)
|
||||
check_argument('lstm_dim', c['model'], restricted=True, val_type=int)
|
||||
check_argument('num_lstm_layers', c['model'], restricted=True, val_type=int)
|
||||
check_argument('use_lstm_with_projection', c['model'], restricted=True, val_type=bool)
|
||||
|
||||
# in-memory storage parameters
|
||||
check_argument('storage', c, restricted=True, val_type=dict)
|
||||
check_argument('sample_from_storage_p', c['storage'], restricted=True, val_type=float, min_val=0.0, max_val=1.0)
|
||||
check_argument('storage_size', c['storage'], restricted=True, val_type=int, min_val=1, max_val=100)
|
||||
check_argument('additive_noise', c['storage'], restricted=True, val_type=float, min_val=0.0, max_val=1.0)
|
||||
|
||||
# datasets - checking only the first entry
|
||||
check_argument('datasets', c, restricted=True, val_type=list)
|
||||
for dataset_entry in c['datasets']:
|
||||
check_argument('name', dataset_entry, restricted=True, val_type=str)
|
||||
check_argument('path', dataset_entry, restricted=True, val_type=str)
|
||||
check_argument('meta_file_train', dataset_entry, restricted=True, val_type=[str, list])
|
||||
check_argument('meta_file_val', dataset_entry, restricted=True, val_type=str)
|
||||
|
|
@ -1,46 +0,0 @@
|
|||
import umap
|
||||
import numpy as np
|
||||
import matplotlib
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
matplotlib.use("Agg")
|
||||
|
||||
|
||||
colormap = (
|
||||
np.array(
|
||||
[
|
||||
[76, 255, 0],
|
||||
[0, 127, 70],
|
||||
[255, 0, 0],
|
||||
[255, 217, 38],
|
||||
[0, 135, 255],
|
||||
[165, 0, 165],
|
||||
[255, 167, 255],
|
||||
[0, 255, 255],
|
||||
[255, 96, 38],
|
||||
[142, 76, 0],
|
||||
[33, 0, 127],
|
||||
[0, 0, 0],
|
||||
[183, 183, 183],
|
||||
],
|
||||
dtype=np.float,
|
||||
)
|
||||
/ 255
|
||||
)
|
||||
|
||||
|
||||
def plot_embeddings(embeddings, num_utter_per_speaker):
|
||||
embeddings = embeddings[: 10 * num_utter_per_speaker]
|
||||
model = umap.UMAP()
|
||||
projection = model.fit_transform(embeddings)
|
||||
num_speakers = embeddings.shape[0] // num_utter_per_speaker
|
||||
ground_truth = np.repeat(np.arange(num_speakers), num_utter_per_speaker)
|
||||
colors = [colormap[i] for i in ground_truth]
|
||||
|
||||
fig, ax = plt.subplots(figsize=(16, 10))
|
||||
_ = ax.scatter(projection[:, 0], projection[:, 1], c=colors)
|
||||
plt.gca().set_aspect("equal", "datalim")
|
||||
plt.title("UMAP projection")
|
||||
plt.tight_layout()
|
||||
plt.savefig("umap")
|
||||
return fig
|
||||
|
|
@ -40,12 +40,9 @@
|
|||
// "url": "tcp:\/\/localhost:54321"
|
||||
// },
|
||||
|
||||
// MODEL PARAMETERS
|
||||
"use_pqmf": true,
|
||||
|
||||
// LOSS PARAMETERS
|
||||
"use_stft_loss": true,
|
||||
"use_subband_stft_loss": true,
|
||||
"use_subband_stft_loss": true, // use only with multi-band models.
|
||||
"use_mse_gan_loss": true,
|
||||
"use_hinge_gan_loss": false,
|
||||
"use_feat_match_loss": false, // use only with melgan discriminators
|
||||
|
|
|
|||
Loading…
Reference in New Issue