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coqui-tts
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refactor and fix compat issues for speaker encoder

This commit is contained in:
erogol 2020-09-11 17:17:07 +02:00
parent 540d811dd5
commit f9001a4bdd
5 changed files with 5 additions and 153 deletions
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4
TTS/bin/train_encoder.py
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@ -11,10 +11,10 @@ import torch
from torch.utils.data import DataLoader
from TTS.speaker_encoder.dataset import MyDataset
from TTS.speaker_encoder.generic_utils import save_best_model
from TTS.speaker_encoder.utils.generic_utils import save_best_model
from TTS.speaker_encoder.losses import GE2ELoss, AngleProtoLoss
from TTS.speaker_encoder.model import SpeakerEncoder
from TTS.speaker_encoder.visual import plot_embeddings
from TTS.speaker_encoder.utils.visual import plot_embeddings
from TTS.tts.datasets.preprocess import load_meta_data
from TTS.utils.generic_utils import (
create_experiment_folder, get_git_branch, remove_experiment_folder,

6
TTS/speaker_encoder/compute_embeddings.py
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@ -7,8 +7,8 @@ from tqdm import tqdm
import torch
from TTS.speaker_encoder.model import SpeakerEncoder
from TTS.tts.utils.audio import AudioProcessor
from TTS.tts.utils.generic_utils import load_config
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. ')
@ -80,7 +80,7 @@ if args.use_cuda:
model.cuda()
for idx, wav_file in enumerate(tqdm(wav_files)):
mel_spec = ap.melspectrogram(ap.load_wav(wav_file)).T
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()

61
TTS/speaker_encoder/config.json
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@ -1,61 +0,0 @@
{
"run_name": "Model compatible to CorentinJ/Real-Time-Voice-Cloning",
"run_description": "train speaker encoder with voxceleb1, voxceleb2 and libriSpeech ",
"audio":{
// Audio processing parameters
"num_mels": 40, // size of the mel spec frame.
"fft_size": 400, // number of stft frequency levels. Size of the linear spectogram frame.
"sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
"win_length": 400, // stft window length in ms.
"hop_length": 160, // stft window hop-lengh in ms.
"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
"preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
"min_level_db": -100, // normalization range
"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
"power": 1.5, // value to sharpen wav signals after GL algorithm.
"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
// Normalization parameters
"signal_norm": true, // normalize the spec values in range [0, 1]
"symmetric_norm": true, // move normalization to range [-1, 1]
"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
"clip_norm": true, // clip normalized values into the range.
"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
"do_trim_silence": false, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
"trim_db": 60 // threshold for timming silence. Set this according to your dataset.
},
"reinit_layers": [],
"loss": "ge2e", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
"grad_clip": 3.0, // upper limit for gradients for clipping.
"epochs": 1000, // total number of epochs to train.
"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
"steps_plot_stats": 10, // number of steps to plot embeddings.
"num_speakers_in_batch": 32, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
"num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values.
"wd": 0.000001, // Weight decay weight.
"checkpoint": true, // If true, it saves checkpoints per "save_step"
"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
"print_step": 1, // Number of steps to log traning on console.
"output_path": "../../checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
"model": {
"input_dim": 40,
"proj_dim": 256,
"lstm_dim": 256,
"num_lstm_layers": 3,
"use_lstm_with_projection": false
},
"datasets":
[
{
"name": "vctk",
"path": "../../../datasets/VCTK-Corpus-removed-silence/",
"meta_file_train": null,
"meta_file_val": null
}
]
}

41
TTS/speaker_encoder/generic_utils.py
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@ -1,41 +0,0 @@
import os
import datetime
import torch
def save_checkpoint(model, optimizer, model_loss, out_path,
current_step, epoch):
checkpoint_path = 'checkpoint_{}.pth.tar'.format(current_step)
checkpoint_path = os.path.join(out_path, checkpoint_path)
print(" | | > Checkpoint saving : {}".format(checkpoint_path))
new_state_dict = model.state_dict()
state = {
'model': new_state_dict,
'optimizer': optimizer.state_dict() if optimizer is not None else None,
'step': current_step,
'epoch': epoch,
'loss': model_loss,
'date': datetime.date.today().strftime("%B %d, %Y"),
}
torch.save(state, checkpoint_path)
def save_best_model(model, optimizer, model_loss, best_loss, out_path,
current_step):
if model_loss < best_loss:
new_state_dict = model.state_dict()
state = {
'model': new_state_dict,
'optimizer': optimizer.state_dict(),
'step': current_step,
'loss': model_loss,
'date': datetime.date.today().strftime("%B %d, %Y"),
}
best_loss = model_loss
bestmodel_path = 'best_model.pth.tar'
bestmodel_path = os.path.join(out_path, bestmodel_path)
print("\n > BEST MODEL ({0:.5f}) : {1:}".format(
model_loss, bestmodel_path))
torch.save(state, bestmodel_path)
return best_loss

46
TTS/speaker_encoder/visual.py
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@ -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