hp.set_hparam_yaml(args.case)
# model
audio_meta_train = VoxCelebMeta(hp.train.data_path, hp.train.meta_path)
model = ClassificationModel(num_classes=audio_meta_train.num_speaker, **hp.model)
# data loader
audio_meta_class = globals()[hp.embed.audio_meta_class]
params = {'data_path': hp.embed.data_path}
if hp.embed.meta_path:
params['meta_path'] = hp.embed.meta_path
audio_meta = audio_meta_class(**params)
data_loader = DataLoader(audio_meta, hp.embed.batch_size)
# samples
wav, mel_spec, speaker_id = data_loader.dataflow().get_data().next()
ckpt = args.ckpt if args.ckpt else tf.train.latest_checkpoint(hp.logdir)
pred_conf = PredictConfig(
model=model,
input_names=['x'],
output_names=['embedding/embedding', 'prediction'],
session_init=SaverRestore(ckpt) if ckpt else None)
embedding_pred = OfflinePredictor(pred_conf)
embedding, pred_speaker_id = embedding_pred(mel_spec)
# get a random audio of the predicted speaker.
wavfile_pred_speaker = np.array(map(lambda s: audio_meta_train.get_random_audio(s), pred_speaker_id))
length = int(hp.signal.duration * hp.signal.sr)
# !/usr/bin/env python
import argparse
from tensorpack.dataflow.remote import send_dataflow_zmq
from data_load import DataLoader, AudioMeta
from hparam import hparam as hp
import multiprocessing
if __name__ == '__main__':
# get arguments
parser = argparse.ArgumentParser()
parser.add_argument('case', type=str, help='experiment case name.')
parser.add_argument('-data_path', type=str)
parser.add_argument('-dest_url', type=str)
parser.add_argument('-num_thread', type=int, default=1)
args = parser.parse_args()
# set hyper-parameters from yaml file
hp.set_hparam_yaml(case=args.case)
if args.data_path:
hp.train.data_path = args.data_path
# dataflow
audio_meta = AudioMeta(hp.train.data_path)
data_loader = DataLoader(audio_meta, 1)
num_thread = args.num_thread if args.num_thread else multiprocessing.cpu_count(
) // 1.5
data_loader = data_loader.dataflow(nr_prefetch=5000,
nr_thread=args.num_thread)
send_dataflow_zmq(data_loader, args.dest_url)