Merge pull request #526 from mueller91/dev

Fix: Check storage params only for speaker encoder

TTS/bin/train_encoder.py

@@ -11,11 +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.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)
@@ -235,6 +236,7 @@ if __name__ == '__main__':
 
     # setup output paths and read configs
     c = load_config(args.config_path)
+    check_config_speaker_encoder(c)
     _ = os.path.dirname(os.path.realpath(__file__))
     if args.data_path != '':
         c.data_path = args.data_path

TTS/bin/train_tts.py

@@ -17,7 +17,7 @@ from TTS.tts.layers.losses import TacotronLoss
 from TTS.tts.utils.distribute import (DistributedSampler,
                                       apply_gradient_allreduce,
                                       init_distributed, reduce_tensor)
-from TTS.tts.utils.generic_utils import check_config, setup_model
+from TTS.tts.utils.generic_utils import setup_model, check_config_tts
 from TTS.tts.utils.io import save_best_model, save_checkpoint
 from TTS.tts.utils.measures import alignment_diagonal_score
 from TTS.tts.utils.speakers import (get_speakers, load_speaker_mapping,
@@ -670,7 +670,7 @@ if __name__ == '__main__':
 
     # setup output paths and read configs
     c = load_config(args.config_path)
-    check_config(c)
+    check_config_tts(c)
     _ = os.path.dirname(os.path.realpath(__file__))
 
     if c.apex_amp_level == 'O1':

TTS/speaker_encoder/config.json

@@ -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"
+            }
+        ]
+}

TTS/speaker_encoder/utils.py

@@ -0,0 +1,61 @@
+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)

TTS/speaker_encoder/visuals.py

@@ -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

TTS/tts/utils/generic_utils.py

@@ -127,7 +127,7 @@ def setup_model(num_chars, num_speakers, c, speaker_embedding_dim=None):
     return model
 
 
-def check_config(c):
+def check_config_tts(c):
     check_argument('model', c, enum_list=['tacotron', 'tacotron2'], restricted=True, val_type=str)
     check_argument('run_name', c, restricted=True, val_type=str)
     check_argument('run_description', c, val_type=str)
@@ -167,11 +167,6 @@ def check_config(c):
     check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
     check_argument('trim_db', c['audio'], restricted=True, val_type=int)
 
-    # storage parameters
-    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)
-
     # training parameters
     check_argument('batch_size', c, restricted=True, val_type=int, min_val=1)
     check_argument('eval_batch_size', c, restricted=True, val_type=int, min_val=1)

requirements.txt

@@ -21,4 +21,5 @@ nose==1.3.7
 cardboardlint==1.3.0
 pylint==2.5.3
 gdown
+umap
 cython