import argparse import os import matplotlib import numpy as np import pandas as pd import torch matplotlib.rcParams['mathtext.fontset'] = 'stix' matplotlib.rcParams['font.family'] = 'STIXGeneral' matplotlib.rcParams['font.size'] = 7 import matplotlib.pyplot as plt from Utility.utils import load_json_from_path from Utility.storage_config import MODELS_DIR def compute_loss_for_approximated_embeddings(csv_path, iso_lookup, language_embeddings, weighted_avg=False, min_n_langs=5, max_n_langs=30, threshold_percentile=95, loss_fn="MSE"): df = pd.read_csv(csv_path, sep="|") if loss_fn == "L1": loss_fn = torch.nn.L1Loss() else: loss_fn = torch.nn.MSELoss() features_per_closest_lang = 2 # for combined, df has up to 5 features (if containing individual distances) per closest lang + 1 target lang column if "combined_dist_0" in df.columns: if "map_dist_0" in df.columns: features_per_closest_lang += 1 if "asp_dist_0" in df.columns: features_per_closest_lang += 1 if "tree_dist_0" in df.columns: features_per_closest_lang += 1 n_closest = len(df.columns) // features_per_closest_lang distance_type = "combined" # else, df has 2 features per closest lang + 1 target lang column else: n_closest = len(df.columns) // features_per_closest_lang if "map_dist_0" in df.columns: distance_type = "map" elif "tree_dist_0" in df.columns: distance_type = "tree" elif "asp_dist_0" in df.columns: distance_type = "asp" elif "learned_dist_0" in df.columns: distance_type = "learned" elif "oracle_dist_0" in df.columns: distance_type = "oracle" else: distance_type = "random" closest_lang_columns = [f"closest_lang_{i}" for i in range(n_closest)] closest_dist_columns = [f"{distance_type}_dist_{i}" for i in range(n_closest)] closest_lang_columns = closest_lang_columns[:max_n_langs] closest_dist_columns = closest_dist_columns[:max_n_langs] threshold = np.percentile(df[closest_dist_columns[-1]], threshold_percentile) print(f"threshold: {threshold}") all_losses = [] for row in df.itertuples(): try: y = language_embeddings[iso_lookup[-1][row.target_lang]] except KeyError: print(f"KeyError: Unable to retrieve language embedding for {row.target_lang}") continue avg_emb = torch.zeros([16]) dists = [getattr(row, d) for i, d in enumerate(closest_dist_columns) if i < min_n_langs or getattr(row, d) < threshold] langs = [getattr(row, l) for l in closest_lang_columns[:len(dists)]] if weighted_avg: for lang, dist in zip(langs, dists): lang_emb = language_embeddings[iso_lookup[-1][lang]] avg_emb += lang_emb * dist normalization_factor = sum(dists) else: for lang in langs: lang_emb = language_embeddings[iso_lookup[-1][lang]] avg_emb += lang_emb normalization_factor = len(langs) avg_emb /= normalization_factor # normalize current_loss = loss_fn(avg_emb, y).item() all_losses.append(current_loss) return all_losses if __name__ == "__main__": default_model_path = os.path.join("../..", MODELS_DIR, "ToucanTTS_Meta", "best.pt") # MODELS_DIR must be absolute path, the relative path will fail at this location parser = argparse.ArgumentParser() parser.add_argument("--model_path", type=str, default=default_model_path, help="model path that should be used for creating oracle lang emb distance cache") parser.add_argument("--min_n_langs", type=int, default=5, help="minimum amount of languages used for averaging") parser.add_argument("--max_n_langs", type=int, default=30, help="maximum amount of languages used for averaging") parser.add_argument("--threshold_percentile", type=int, default=95, help="percentile of the furthest used languages \ used as cutoff threshold (no langs >= the threshold are used for averagin)") parser.add_argument("--loss_fn", choices=["MSE", "L1"], type=str, default="MSE", help="loss function used") args = parser.parse_args() csv_paths = [ "distance_datasets/dataset_map_top30_furthest.csv", "distance_datasets/dataset_random_top30.csv", "distance_datasets/dataset_asp_top30.csv", "distance_datasets/dataset_tree_top30.csv", "distance_datasets/dataset_map_top30.csv", "distance_datasets/dataset_combined_top30_indiv-dists.csv", "distance_datasets/dataset_learned_top30.csv", "distance_datasets/dataset_oracle_top30.csv", ] weighted = [False] lang_embs = torch.load(args.model_path)["model"]["encoder.language_embedding.weight"] lang_embs.requires_grad_(False) iso_lookup = load_json_from_path("iso_lookup.json") losses_of_multiple_datasets = [] OUT_DIR = "plots" os.makedirs(OUT_DIR, exist_ok=True) fig, ax = plt.subplots(figsize=(3.15022, 3.15022*(2/3)), constrained_layout=True) plt.ylabel(args.loss_fn) for i, csv_path in enumerate(csv_paths): print(f"csv_path: {os.path.basename(csv_path)}") for condition in weighted: losses = compute_loss_for_approximated_embeddings(csv_path, iso_lookup, lang_embs, condition, min_n_langs=args.min_n_langs, max_n_langs=args.max_n_langs, threshold_percentile=args.threshold_percentile, loss_fn=args.loss_fn) print(f"weighted average: {condition} | mean loss: {np.mean(losses)}") losses_of_multiple_datasets.append(losses) bp_dict = ax.boxplot(losses_of_multiple_datasets, labels = [ "map furthest", "random", "inv. ASP", "tree", "map", "avg", "meta-learned", "oracle", ], patch_artist=True, boxprops=dict(facecolor = "lightblue", ), showfliers=False, widths=0.45 ) # major ticks every 0.1, minor ticks every 0.05, between 0.0 and 0.6 major_ticks = np.arange(0, 0.6, 0.1) minor_ticks = np.arange(0, 0.6, 0.05) ax.set_yticks(major_ticks) ax.set_yticks(minor_ticks, minor=True) # horizontal grid lines for minor and major ticks ax.grid(which='both', linestyle='-', color='lightgray', linewidth=0.3, axis='y') ax.set_aspect(4.5) plt.title(f"min. {args.min_n_langs} kNN, max. {args.max_n_langs}\nthreshold: {args.threshold_percentile}th-percentile distance of {args.max_n_langs}th-closest language") plt.xticks(rotation=45) plt.savefig(os.path.join(OUT_DIR, "example_boxplot_release.pdf"), bbox_inches='tight')