mirror of https://github.com/coqui-ai/TTS.git
fix: broken imports and missing files after merging in latest commits from mozilla/dev into mueller91/dev.
speaker_encoder's config.json and visuals.py are missing in the current dev branch of MozillaTTS, and some imports are broken.
This commit is contained in:
mueller91
parent
1fe5eb054f
commit
cfeeef7a7f
|
|
@ -11,13 +11,12 @@ import torch
|
|||
from torch.utils.data import DataLoader
|
||||
|
||||
from TTS.speaker_encoder.dataset import MyDataset
|
||||
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.utils.visual import plot_embeddings
|
||||
from TTS.speaker_encoder.utils import check_config_speaker_encoder
|
||||
from TTS.speaker_encoder.visual import plot_embeddings
|
||||
from TTS.speaker_encoder.visuals 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)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,103 @@
|
|||
|
||||
{
|
||||
"run_name": "mueller91",
|
||||
"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": true, // 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": "angleproto", // "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": 64, // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
|
||||
"num_utters_per_speaker": 10, //
|
||||
"num_loader_workers": 8, // 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": 20, // Number of steps to log traning on console.
|
||||
"output_path": "../../MozillaTTSOutput/checkpoints/voxceleb_librispeech/speaker_encoder/", // DATASET-RELATED: output path for all training outputs.
|
||||
"model": {
|
||||
"input_dim": 40,
|
||||
"proj_dim": 256,
|
||||
"lstm_dim": 768,
|
||||
"num_lstm_layers": 3,
|
||||
"use_lstm_with_projection": true
|
||||
},
|
||||
"storage": {
|
||||
"sample_from_storage_p": 0.66, // the probability with which we'll sample from the DataSet in-memory storage
|
||||
"storage_size": 15, // the size of the in-memory storage with respect to a single batch
|
||||
"additive_noise": 1e-5 // add very small gaussian noise to the data in order to increase robustness
|
||||
},
|
||||
"datasets":
|
||||
[
|
||||
{
|
||||
"name": "vctk_slim",
|
||||
"path": "../../../audio-datasets/en/VCTK-Corpus/",
|
||||
"meta_file_train": null,
|
||||
"meta_file_val": null
|
||||
},
|
||||
{
|
||||
"name": "libri_tts",
|
||||
"path": "../../../audio-datasets/en/LibriTTS/train-clean-100",
|
||||
"meta_file_train": null,
|
||||
"meta_file_val": null
|
||||
},
|
||||
{
|
||||
"name": "libri_tts",
|
||||
"path": "../../../audio-datasets/en/LibriTTS/train-clean-360",
|
||||
"meta_file_train": null,
|
||||
"meta_file_val": null
|
||||
},
|
||||
{
|
||||
"name": "libri_tts",
|
||||
"path": "../../../audio-datasets/en/LibriTTS/train-other-500",
|
||||
"meta_file_train": null,
|
||||
"meta_file_val": null
|
||||
},
|
||||
{
|
||||
"name": "voxceleb1",
|
||||
"path": "../../../audio-datasets/en/voxceleb1/",
|
||||
"meta_file_train": null,
|
||||
"meta_file_val": null
|
||||
},
|
||||
{
|
||||
"name": "voxceleb2",
|
||||
"path": "../../../audio-datasets/en/voxceleb2/",
|
||||
"meta_file_train": null,
|
||||
"meta_file_val": null
|
||||
},
|
||||
{
|
||||
"name": "common_voice",
|
||||
"path": "../../../audio-datasets/en/MozillaCommonVoice",
|
||||
"meta_file_train": "train.tsv",
|
||||
"meta_file_val": "test.tsv"
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
@ -0,0 +1,46 @@
|
|||
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
|
||||
Loading…
Reference in New Issue