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| import argparse | |
| import logging | |
| import sys | |
| from argparse import RawTextHelpFormatter | |
| import torch | |
| from tqdm import tqdm | |
| from TTS.config import load_config | |
| from TTS.tts.datasets import load_tts_samples | |
| from TTS.tts.utils.speakers import SpeakerManager | |
| from TTS.utils.generic_utils import ConsoleFormatter, setup_logger | |
| def compute_encoder_accuracy(dataset_items, encoder_manager): | |
| class_name_key = encoder_manager.encoder_config.class_name_key | |
| map_classid_to_classname = getattr(encoder_manager.encoder_config, "map_classid_to_classname", None) | |
| class_acc_dict = {} | |
| # compute embeddings for all wav_files | |
| for item in tqdm(dataset_items): | |
| class_name = item[class_name_key] | |
| wav_file = item["audio_file"] | |
| # extract the embedding | |
| embedd = encoder_manager.compute_embedding_from_clip(wav_file) | |
| if encoder_manager.encoder_criterion is not None and map_classid_to_classname is not None: | |
| embedding = torch.FloatTensor(embedd).unsqueeze(0) | |
| if encoder_manager.use_cuda: | |
| embedding = embedding.cuda() | |
| class_id = encoder_manager.encoder_criterion.softmax.inference(embedding).item() | |
| predicted_label = map_classid_to_classname[str(class_id)] | |
| else: | |
| predicted_label = None | |
| if class_name is not None and predicted_label is not None: | |
| is_equal = int(class_name == predicted_label) | |
| if class_name not in class_acc_dict: | |
| class_acc_dict[class_name] = [is_equal] | |
| else: | |
| class_acc_dict[class_name].append(is_equal) | |
| else: | |
| raise RuntimeError("Error: class_name or/and predicted_label are None") | |
| acc_avg = 0 | |
| for key, values in class_acc_dict.items(): | |
| acc = sum(values) / len(values) | |
| print("Class", key, "Accuracy:", acc) | |
| acc_avg += acc | |
| print("Average Accuracy:", acc_avg / len(class_acc_dict)) | |
| if __name__ == "__main__": | |
| setup_logger("TTS", level=logging.INFO, stream=sys.stdout, formatter=ConsoleFormatter()) | |
| parser = argparse.ArgumentParser( | |
| description="""Compute the accuracy of the encoder.\n\n""" | |
| """ | |
| Example runs: | |
| python TTS/bin/eval_encoder.py emotion_encoder_model.pth emotion_encoder_config.json dataset_config.json | |
| """, | |
| formatter_class=RawTextHelpFormatter, | |
| ) | |
| parser.add_argument("model_path", type=str, help="Path to model checkpoint file.") | |
| parser.add_argument( | |
| "config_path", | |
| type=str, | |
| help="Path to model config file.", | |
| ) | |
| parser.add_argument( | |
| "config_dataset_path", | |
| type=str, | |
| help="Path to dataset config file.", | |
| ) | |
| parser.add_argument("--use_cuda", action=argparse.BooleanOptionalAction, help="flag to set cuda.", default=True) | |
| parser.add_argument("--eval", action=argparse.BooleanOptionalAction, help="compute eval.", default=True) | |
| args = parser.parse_args() | |
| c_dataset = load_config(args.config_dataset_path) | |
| meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=args.eval) | |
| items = meta_data_train + meta_data_eval | |
| enc_manager = SpeakerManager( | |
| encoder_model_path=args.model_path, encoder_config_path=args.config_path, use_cuda=args.use_cuda | |
| ) | |
| compute_encoder_accuracy(items, enc_manager) | |