Spaces:
Paused
Paused
File size: 3,127 Bytes
a153039 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
import argparse
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
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__":
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", type=bool, help="flag to set cuda.", default=True)
parser.add_argument("--eval", type=bool, 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)
|