Medix-AI/coqui-tts
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coqui-tts
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update Compute embeddings script

This commit is contained in:
Edresson 2021-05-27 00:45:18 -03:00
parent 5af505ff33
commit 1496f271dc
1 changed files with 6 additions and 5 deletions
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11
TTS/bin/compute_embeddings.py
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@ -10,7 +10,7 @@ from TTS.speaker_encoder.utils.generic_utils import setup_model
from TTS.tts.datasets.preprocess import load_meta_data from TTS.tts.datasets.preprocess import load_meta_data
from TTS.tts.utils.speakers import save_speaker_mapping from TTS.tts.utils.speakers import save_speaker_mapping
from TTS.utils.audio import AudioProcessor from TTS.utils.audio import AudioProcessor
from TTS.utils.io import load_config from TTS.config import load_config, BaseDatasetConfig
parser = argparse.ArgumentParser( parser = argparse.ArgumentParser(
description='Compute embedding vectors for each wav file in a dataset. If "target_dataset" is defined, it generates "speakers.json" necessary for training a multi-speaker model.' description='Compute embedding vectors for each wav file in a dataset. If "target_dataset" is defined, it generates "speakers.json" necessary for training a multi-speaker model.'
@ -44,7 +44,7 @@ sep = args.separator
if args.target_dataset != "": if args.target_dataset != "":
# if target dataset is defined # if target dataset is defined
dataset_config = [ dataset_config = [
{"name": args.target_dataset, "path": args.data_path, "meta_file_train": None, "meta_file_val": None}, BaseDatasetConfig(name=args.target_dataset, path=args.data_path, meta_file_train=None, meta_file_val=None),
] ]
wav_files, _ = load_meta_data(dataset_config, eval_split=False) wav_files, _ = load_meta_data(dataset_config, eval_split=False)
output_files = [wav_file[1].replace(data_path, args.output_path).replace(".wav", ".npy") for wav_file in wav_files] output_files = [wav_file[1].replace(data_path, args.output_path).replace(".wav", ".npy") for wav_file in wav_files]
@ -106,6 +106,7 @@ for idx, wav_file in enumerate(tqdm(wav_files)):
speaker_mapping[wav_file_name]["embedding"] = embedd.flatten().tolist() speaker_mapping[wav_file_name]["embedding"] = embedd.flatten().tolist()
if args.target_dataset != "": if args.target_dataset != "":
# save speaker_mapping if target dataset is defined if speaker_mapping:
mapping_file_path = os.path.join(args.output_path, "speakers.json") # save speaker_mapping if target dataset is defined
save_speaker_mapping(args.output_path, speaker_mapping) mapping_file_path = os.path.join(args.output_path, "speakers.json")
save_speaker_mapping(args.output_path, speaker_mapping)