mirror of https://github.com/coqui-ai/TTS.git
89 lines
2.9 KiB
Python
89 lines
2.9 KiB
Python
import argparse
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import os
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import torch
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from argparse import RawTextHelpFormatter
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from tqdm import tqdm
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from TTS.config import load_config
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.utils.speakers import SpeakerManager
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parser = argparse.ArgumentParser(
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description="""Compute the accuracy of the encoder.\n\n"""
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"""
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Example runs:
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python TTS/bin/eval_encoder.py emotion_encoder_model.pth.tar emotion_encoder_config.json dataset_config.json
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""",
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formatter_class=RawTextHelpFormatter,
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)
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parser.add_argument("model_path", type=str, help="Path to model checkpoint file.")
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parser.add_argument(
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"config_path",
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type=str,
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help="Path to model config file.",
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)
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parser.add_argument(
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"config_dataset_path",
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type=str,
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help="Path to dataset config file.",
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)
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parser.add_argument("--use_cuda", type=bool, help="flag to set cuda.", default=True)
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parser.add_argument("--eval", type=bool, help="compute eval.", default=True)
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args = parser.parse_args()
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c_dataset = load_config(args.config_dataset_path)
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meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=args.eval)
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wav_files = meta_data_train + meta_data_eval
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speaker_manager = SpeakerManager(
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encoder_model_path=args.model_path, encoder_config_path=args.config_path, use_cuda=args.use_cuda
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)
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if speaker_manager.speaker_encoder_config.map_classid_to_classname is not None:
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map_classid_to_classname = speaker_manager.speaker_encoder_config.map_classid_to_classname
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else:
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map_classid_to_classname = None
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# compute speaker embeddings
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class_acc_dict = {}
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for idx, wav_file in enumerate(tqdm(wav_files)):
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if isinstance(wav_file, list):
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class_name = wav_file[2]
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wav_file = wav_file[1]
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else:
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class_name = None
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# extract the embedding
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embedd = speaker_manager.compute_d_vector_from_clip(wav_file)
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if speaker_manager.speaker_encoder_criterion is not None and map_classid_to_classname is not None:
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embedding = torch.FloatTensor(embedd).unsqueeze(0)
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if args.use_cuda:
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embedding = embedding.cuda()
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class_id = speaker_manager.speaker_encoder_criterion.softmax.inference(embedding).item()
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predicted_label = map_classid_to_classname[str(class_id)]
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else:
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predicted_label = None
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if class_name is not None and predicted_label is not None:
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is_equal = int(class_name == predicted_label)
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if class_name not in class_acc_dict:
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class_acc_dict[class_name] = [is_equal]
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else:
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class_acc_dict[class_name].append(is_equal)
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else:
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print("Error: class_name or/and predicted_label are None")
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exit()
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acc_avg = 0
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for key in class_acc_dict:
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acc = sum(class_acc_dict[key])/len(class_acc_dict[key])
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print("Class", key, "Accuracy:", acc)
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acc_avg += acc
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print("Average Accuracy:", acc_avg/len(class_acc_dict)) |