mirror of https://github.com/coqui-ai/TTS.git
refactor and fix compat issues for speaker encoder
This commit is contained in:
erogol
parent
540d811dd5
commit
f9001a4bdd
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@ -11,10 +11,10 @@ import torch
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from torch.utils.data import DataLoader
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from torch.utils.data import DataLoader
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from TTS.speaker_encoder.dataset import MyDataset
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from TTS.speaker_encoder.dataset import MyDataset
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from TTS.speaker_encoder.generic_utils import save_best_model
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from TTS.speaker_encoder.utils.generic_utils import save_best_model
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from TTS.speaker_encoder.losses import GE2ELoss, AngleProtoLoss
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from TTS.speaker_encoder.losses import GE2ELoss, AngleProtoLoss
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from TTS.speaker_encoder.model import SpeakerEncoder
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from TTS.speaker_encoder.model import SpeakerEncoder
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from TTS.speaker_encoder.visual import plot_embeddings
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from TTS.speaker_encoder.utils.visual import plot_embeddings
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from TTS.tts.datasets.preprocess import load_meta_data
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from TTS.tts.datasets.preprocess import load_meta_data
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from TTS.utils.generic_utils import (
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from TTS.utils.generic_utils import (
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create_experiment_folder, get_git_branch, remove_experiment_folder,
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create_experiment_folder, get_git_branch, remove_experiment_folder,
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@ -7,8 +7,8 @@ from tqdm import tqdm
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import torch
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import torch
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from TTS.speaker_encoder.model import SpeakerEncoder
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from TTS.speaker_encoder.model import SpeakerEncoder
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from TTS.tts.utils.audio import AudioProcessor
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from TTS.utils.audio import AudioProcessor
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from TTS.tts.utils.generic_utils import load_config
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from TTS.utils.io import load_config
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parser = argparse.ArgumentParser(
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parser = argparse.ArgumentParser(
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description='Compute embedding vectors for each wav file in a dataset. ')
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description='Compute embedding vectors for each wav file in a dataset. ')
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@ -80,7 +80,7 @@ if args.use_cuda:
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model.cuda()
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model.cuda()
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for idx, wav_file in enumerate(tqdm(wav_files)):
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for idx, wav_file in enumerate(tqdm(wav_files)):
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mel_spec = ap.melspectrogram(ap.load_wav(wav_file)).T
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mel_spec = ap.melspectrogram(ap.load_wav(wav_file, sr=ap.sample_rate)).T
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mel_spec = torch.FloatTensor(mel_spec[None, :, :])
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mel_spec = torch.FloatTensor(mel_spec[None, :, :])
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if args.use_cuda:
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if args.use_cuda:
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mel_spec = mel_spec.cuda()
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mel_spec = mel_spec.cuda()
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@ -1,61 +0,0 @@
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{
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"run_name": "Model compatible to CorentinJ/Real-Time-Voice-Cloning",
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"run_description": "train speaker encoder with voxceleb1, voxceleb2 and libriSpeech ",
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"audio":{
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// Audio processing parameters
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"num_mels": 40, // size of the mel spec frame.
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"fft_size": 400, // number of stft frequency levels. Size of the linear spectogram frame.
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"sample_rate": 16000, // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
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"win_length": 400, // stft window length in ms.
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"hop_length": 160, // stft window hop-lengh in ms.
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"frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
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"frame_shift_ms": null, // stft window hop-lengh in ms. If null, 'hop_length' is used.
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"preemphasis": 0.98, // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
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"min_level_db": -100, // normalization range
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"ref_level_db": 20, // reference level db, theoretically 20db is the sound of air.
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"power": 1.5, // value to sharpen wav signals after GL algorithm.
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"griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
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// Normalization parameters
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"signal_norm": true, // normalize the spec values in range [0, 1]
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"symmetric_norm": true, // move normalization to range [-1, 1]
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"max_norm": 4.0, // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
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"clip_norm": true, // clip normalized values into the range.
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"mel_fmin": 0.0, // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
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"mel_fmax": 8000.0, // maximum freq level for mel-spec. Tune for dataset!!
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"do_trim_silence": false, // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
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"trim_db": 60 // threshold for timming silence. Set this according to your dataset.
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},
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"reinit_layers": [],
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"loss": "ge2e", // "ge2e" to use Generalized End-to-End loss and "angleproto" to use Angular Prototypical loss (new SOTA)
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"grad_clip": 3.0, // upper limit for gradients for clipping.
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"epochs": 1000, // total number of epochs to train.
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"lr": 0.0001, // Initial learning rate. If Noam decay is active, maximum learning rate.
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"lr_decay": false, // if true, Noam learning rate decaying is applied through training.
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"warmup_steps": 4000, // Noam decay steps to increase the learning rate from 0 to "lr"
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"tb_model_param_stats": false, // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
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"steps_plot_stats": 10, // number of steps to plot embeddings.
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"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'.
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"num_loader_workers": 4, // number of training data loader processes. Don't set it too big. 4-8 are good values.
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"wd": 0.000001, // Weight decay weight.
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"checkpoint": true, // If true, it saves checkpoints per "save_step"
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"save_step": 1000, // Number of training steps expected to save traning stats and checkpoints.
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"print_step": 1, // Number of steps to log traning on console.
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"output_path": "../../checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
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"model": {
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"input_dim": 40,
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"proj_dim": 256,
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"lstm_dim": 256,
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"num_lstm_layers": 3,
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"use_lstm_with_projection": false
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},
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"datasets":
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[
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{
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"name": "vctk",
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"path": "../../../datasets/VCTK-Corpus-removed-silence/",
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"meta_file_train": null,
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"meta_file_val": null
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}
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]
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}
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@ -1,41 +0,0 @@
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import os
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import datetime
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import torch
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def save_checkpoint(model, optimizer, model_loss, out_path,
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current_step, epoch):
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checkpoint_path = 'checkpoint_{}.pth.tar'.format(current_step)
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checkpoint_path = os.path.join(out_path, checkpoint_path)
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print(" | | > Checkpoint saving : {}".format(checkpoint_path))
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new_state_dict = model.state_dict()
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state = {
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'model': new_state_dict,
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'optimizer': optimizer.state_dict() if optimizer is not None else None,
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'step': current_step,
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'epoch': epoch,
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'loss': model_loss,
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'date': datetime.date.today().strftime("%B %d, %Y"),
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}
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torch.save(state, checkpoint_path)
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def save_best_model(model, optimizer, model_loss, best_loss, out_path,
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current_step):
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if model_loss < best_loss:
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new_state_dict = model.state_dict()
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state = {
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'model': new_state_dict,
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'optimizer': optimizer.state_dict(),
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'step': current_step,
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'loss': model_loss,
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'date': datetime.date.today().strftime("%B %d, %Y"),
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}
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best_loss = model_loss
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bestmodel_path = 'best_model.pth.tar'
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bestmodel_path = os.path.join(out_path, bestmodel_path)
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print("\n > BEST MODEL ({0:.5f}) : {1:}".format(
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model_loss, bestmodel_path))
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torch.save(state, bestmodel_path)
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return best_loss
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@ -1,46 +0,0 @@
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import umap
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import numpy as np
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import matplotlib
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import matplotlib.pyplot as plt
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matplotlib.use("Agg")
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colormap = (
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np.array(
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[
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[76, 255, 0],
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[0, 127, 70],
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[255, 0, 0],
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[255, 217, 38],
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[0, 135, 255],
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[165, 0, 165],
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[255, 167, 255],
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[0, 255, 255],
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[255, 96, 38],
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[142, 76, 0],
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[33, 0, 127],
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[0, 0, 0],
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[183, 183, 183],
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],
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dtype=np.float,
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)
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/ 255
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)
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def plot_embeddings(embeddings, num_utter_per_speaker):
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embeddings = embeddings[: 10 * num_utter_per_speaker]
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model = umap.UMAP()
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projection = model.fit_transform(embeddings)
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num_speakers = embeddings.shape[0] // num_utter_per_speaker
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ground_truth = np.repeat(np.arange(num_speakers), num_utter_per_speaker)
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colors = [colormap[i] for i in ground_truth]
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fig, ax = plt.subplots(figsize=(16, 10))
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_ = ax.scatter(projection[:, 0], projection[:, 1], c=colors)
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plt.gca().set_aspect("equal", "datalim")
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plt.title("UMAP projection")
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plt.tight_layout()
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plt.savefig("umap")
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return fig
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