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coqui-tts
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compute_embedding update

This commit is contained in:
erogol 2020-11-25 14:46:08 +01:00
parent aa2b31a1b0
commit 8a820930c6
1 changed files with 6 additions and 4 deletions
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10
TTS/bin/compute_embeddings.py
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@ -9,7 +9,7 @@ import torch
from TTS.speaker_encoder.model import SpeakerEncoder from TTS.speaker_encoder.model import SpeakerEncoder
from TTS.utils.audio import AudioProcessor from TTS.utils.audio import AudioProcessor
from TTS.utils.io import load_config from TTS.utils.io import load_config
from TTS.utils.io import save_speaker_mapping from TTS.tts.utils.speakers import save_speaker_mapping
from TTS.tts.datasets.preprocess import load_meta_data from TTS.tts.datasets.preprocess import load_meta_data
parser = argparse.ArgumentParser( parser = argparse.ArgumentParser(
@ -108,21 +108,23 @@ for idx, wav_file in enumerate(tqdm(wav_files)):
if isinstance(wav_file, list): if isinstance(wav_file, list):
speaker_name = wav_file[2] speaker_name = wav_file[2]
wav_file = wav_file[1] wav_file = wav_file[1]
mel_spec = ap.melspectrogram(ap.load_wav(wav_file, sr=ap.sample_rate)).T mel_spec = ap.melspectrogram(ap.load_wav(wav_file, sr=ap.sample_rate)).T
mel_spec = torch.FloatTensor(mel_spec[None, :, :]) mel_spec = torch.FloatTensor(mel_spec[None, :, :])
if args.use_cuda: if args.use_cuda:
mel_spec = mel_spec.cuda() mel_spec = mel_spec.cuda()
embedd = model.compute_embedding(mel_spec) embedd = model.compute_embedding(mel_spec)
np.save(output_files[idx], embedd.detach().cpu().numpy()) embedd = embedd.detach().cpu().numpy()
np.save(output_files[idx], embedd)
if args.target_dataset != '': if args.target_dataset != '':
# create speaker_mapping if target dataset is defined # create speaker_mapping if target dataset is defined
wav_file_name = os.path.basename(wav_file) wav_file_name = os.path.basename(wav_file)
speaker_mapping[wav_file_name] = {} speaker_mapping[wav_file_name] = {}
speaker_mapping[wav_file_name]['name'] = speaker_name speaker_mapping[wav_file_name]['name'] = speaker_name
speaker_mapping[wav_file_name]['embedding'] = embedd.detach().cpu().numpy() 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 # save speaker_mapping if target dataset is defined
mapping_file_path = os.path.join(args.output_path, 'speakers.json') mapping_file_path = os.path.join(args.output_path, 'speakers.json')
save_speaker_mapping(mapping_file_path, speaker_mapping) save_speaker_mapping(args.output_path, speaker_mapping)