Add base_dir parameter

synthesize.py

@@ -15,10 +15,10 @@ def prepare_run(args):
 	os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 
 	run_name = args.name or args.tacotron_name or args.model
-	taco_checkpoint = os.path.join('logs-' + run_name, 'taco_' + args.checkpoint)
+	taco_checkpoint = os.path.join(args.base_dir, 'logs-' + run_name, 'taco_' + args.checkpoint)
 
 	run_name = args.name or args.wavenet_name or args.model
-	wave_checkpoint = os.path.join('logs-' + run_name, 'wave_' + args.checkpoint)
+	wave_checkpoint = os.path.join(args.base_dir, 'logs-' + run_name, 'wave_' + args.checkpoint)
 	return taco_checkpoint, wave_checkpoint, modified_hp
 
 def get_sentences(args):
@@ -44,16 +44,16 @@ def synthesize(args, hparams, taco_checkpoint, wave_checkpoint, sentences):
 def main():
 	accepted_modes = ['eval', 'synthesis', 'live']
 	parser = argparse.ArgumentParser()
-	parser.add_argument('--checkpoint', default='pretrained/', help='Path to model checkpoint')
+	parser.add_argument('--base_dir', default='')
+	parser.add_argument('--checkpoint', default='pretrained', help='Path to model checkpoint')
 	parser.add_argument('--hparams', default='',
 		help='Hyperparameter overrides as a comma-separated list of name=value pairs')
 	parser.add_argument('--name', help='Name of logging directory if the two models were trained together.')
 	parser.add_argument('--tacotron_name', help='Name of logging directory of Tacotron. If trained separately')
 	parser.add_argument('--wavenet_name', help='Name of logging directory of WaveNet. If trained separately')
 	parser.add_argument('--model', default='Tacotron-2')
-	parser.add_argument('--input_dir', default='training_data/', help='folder to contain inputs sentences/targets')
-	parser.add_argument('--mels_dir', default='tacotron_output/eval/', help='folder to contain mels to synthesize audio from using the Wavenet')
-	parser.add_argument('--output_dir', default='output/', help='folder to contain synthesized mel spectrograms')
+	parser.add_argument('--input_dir', default='training_data', help='folder to contain inputs sentences/targets')
+	parser.add_argument('--output_dir', default='output', help='folder to contain synthesized mel spectrograms')
 	parser.add_argument('--mode', default='eval', help='mode of run: can be one of {}'.format(accepted_modes))
 	parser.add_argument('--GTA', default='True', help='Ground truth aligned synthesis, defaults to True, only considered in synthesis mode')
 	parser.add_argument('--text_list', default='', help='Text file contains list of texts to be synthesized. Valid if mode=eval')

tacotron/synthesize.py

@@ -43,9 +43,6 @@ def run_eval(args, checkpoint_path, output_dir, hparams, sentences):
 	eval_dir = os.path.join(output_dir, 'eval')
 	log_dir = os.path.join(output_dir, 'logs-eval')
 
-	if args.model == 'Tacotron-2':
-		assert os.path.normpath(eval_dir) == os.path.normpath(args.mels_dir) #mels_dir = wavenet_input_dir
-
 	#Create output path if it doesn't exist
 	os.makedirs(eval_dir, exist_ok=True)
 	os.makedirs(log_dir, exist_ok=True)
@@ -56,7 +53,6 @@ def run_eval(args, checkpoint_path, output_dir, hparams, sentences):
 	synth = Synthesizer()
 	synth.load(checkpoint_path, hparams)
 
-
 	with open(os.path.join(eval_dir, 'map.txt'), 'w') as file:
 		for i, text in enumerate(tqdm(sentences)):
 			start = time.time()
@@ -66,7 +62,7 @@ def run_eval(args, checkpoint_path, output_dir, hparams, sentences):
 	log('synthesized mel spectrograms at {}'.format(eval_dir))
 	return eval_dir
 
-def run_synthesis(args, checkpoint_path, output_dir, hparams):
+def run_synthesis(args, checkpoint_path, input_dir, output_dir, hparams):
 	GTA = (args.GTA == 'True')
 	if GTA:
 		synth_dir = os.path.join(output_dir, 'gta')
@@ -80,21 +76,22 @@ def run_synthesis(args, checkpoint_path, output_dir, hparams):
 		os.makedirs(synth_dir, exist_ok=True)
 
 
-	metadata_filename = os.path.join(args.input_dir, 'train.txt')
+	metadata_filename = os.path.join(input_dir, 'train.txt')
 	log(hparams_debug_string())
 	synth = Synthesizer()
 	synth.load(checkpoint_path, hparams, gta=GTA)
+
 	with open(metadata_filename, encoding='utf-8') as f:
 		metadata = [line.strip().split('|') for line in f]
 		frame_shift_ms = hparams.hop_size / hparams.sample_rate
 		hours = sum([int(x[4]) for x in metadata]) * frame_shift_ms / (3600)
 		log('Loaded metadata for {} examples ({:.2f} hours)'.format(len(metadata), hours))
 
-	metadata = [metadata[i: i+hparams.tacotron_synthesis_batch_size] for i in range(0, len(metadata), hparams.tacotron_synthesis_batch_size)]
+	metadata = [metadata[i: i + hparams.tacotron_synthesis_batch_size] for i in range(0, len(metadata), hparams.tacotron_synthesis_batch_size)]
 
 	log('starting synthesis')
-	mel_dir = os.path.join(args.input_dir, 'mels')
-	wav_dir = os.path.join(args.input_dir, 'audio')
+	mel_dir = os.path.join(input_dir, 'mels')
+	wav_dir = os.path.join(input_dir, 'audio')
 	with open(os.path.join(synth_dir, 'map.txt'), 'w') as file:
 		for i, meta in enumerate(tqdm(metadata)):
 			texts = [m[5] for m in meta]
@@ -109,7 +106,8 @@ def run_synthesis(args, checkpoint_path, output_dir, hparams):
 	return os.path.join(synth_dir, 'map.txt')
 
 def tacotron_synthesize(args, hparams, checkpoint, sentences=None):
-	output_dir = 'tacotron_' + args.output_dir
+	input_dir = os.path.join(args.base_dir, args.input_dir)
+	output_dir = os.path.join(args.base_dir, 'tacotron_' + args.output_dir)
 
 	try:
 		checkpoint_path = tf.train.get_checkpoint_state(checkpoint).model_checkpoint_path
@@ -120,6 +118,6 @@ def tacotron_synthesize(args, hparams, checkpoint, sentences=None):
 	if args.mode == 'eval':
 		return run_eval(args, checkpoint_path, output_dir, hparams, sentences)
 	elif args.mode == 'synthesis':
-		return run_synthesis(args, checkpoint_path, output_dir, hparams)
+		return run_synthesis(args, checkpoint_path, input_dir, output_dir, hparams)
 	else:
 		run_live(args, checkpoint_path, hparams)

test_wavenet_feeder.py

@@ -1,12 +1,12 @@
+import argparse
 import numpy as np 
 import os
-import argparse 
-from hparams import hparams
+
 from datasets import audio
+from hparams import hparams
 from tqdm import tqdm
 
 
-
 def _limit_time(hparams):
 	'''Limit time resolution to save GPU memory.
 	'''
@@ -34,6 +34,7 @@ def get_groups(args, hparams, meta, local_condition):
 
 	return (input_data, local_condition_features, None, len(input_data))
 
+
 def _adjust_time_resolution(hparams, batch, local_condition, max_time_steps):
 		'''Adjust time resolution between audio and local condition
 		'''
@@ -65,16 +66,19 @@ def _adjust_time_resolution(hparams, batch, local_condition, max_time_steps):
 				new_batch.append((x, c, g, l))
 			return new_batch
 
+
 def _assert_ready_for_upsample(hparams, x, c):
 	assert len(x) % len(c) == 0 and len(x) // len(c) == audio.get_hop_size(hparams)
 
+
 def check_time_alignment(hparams, batch, local_condition):
 	#No need to check beyond this step when preparing data
 	#Limit time steps to save GPU Memory usage
 	max_time_steps = _limit_time(hparams)
 	#Adjust time resolution for upsampling
 	batch = _adjust_time_resolution(hparams, batch, local_condition, max_time_steps)
 
+
 def _ensure_divisible(length, divisible_by=256, lower=True):
 	if length % divisible_by == 0:
 		return length
@@ -83,20 +87,20 @@ def _ensure_divisible(length, divisible_by=256, lower=True):
 	else:
 		return length + (divisible_by - length % divisible_by)
 
+
 def run(args, hparams):
 	with open(args.metadata, 'r') as file:
 		metadata = [line.strip().split('|') for line in file]
 
 	local_condition = hparams.cin_channels > 0
 
 	examples = [get_groups(args, hparams, meta, local_condition) for meta in metadata]
-	batches = [examples[i: i+hparams.wavenet_batch_size] for i in range(0, len(examples), hparams.wavenet_batch_size)]
+	batches = [examples[i: i + hparams.wavenet_batch_size] for i in range(0, len(examples), hparams.wavenet_batch_size)]
 
 	for batch in tqdm(batches):
 		check_time_alignment(hparams, batch, local_condition)
 
 
-
 def main():
 	parser = argparse.ArgumentParser()
 	parser.add_argument('--base_dir', default='')
@@ -110,4 +114,4 @@ def main():
 
 
 if __name__ == '__main__':
-	main()
+	main()

wavenet_vocoder/synthesize.py

@@ -9,7 +9,7 @@
 from wavenet_vocoder.synthesizer import Synthesizer
 
 
-def run_synthesis(args, checkpoint_path, output_dir, hparams):
+def run_synthesis(args, checkpoint_path, input_dir, output_dir, hparams):
 	log_dir = os.path.join(output_dir, 'plots')
 	wav_dir = os.path.join(output_dir, 'wavs')
 
@@ -20,7 +20,7 @@ def run_synthesis(args, checkpoint_path, output_dir, hparams):
 
 	if args.model == 'Tacotron-2':
 		#If running all Tacotron-2, synthesize audio from evaluated mels
-		metadata_filename = os.path.join(args.mels_dir, 'map.txt')
+		metadata_filename = os.path.join(input_dir, 'map.txt')
 		with open(metadata_filename, encoding='utf-8') as f:
 			metadata = np.array([line.strip().split('|') for line in f])
 
@@ -31,7 +31,7 @@ def run_synthesis(args, checkpoint_path, output_dir, hparams):
 		speaker_ids = None if (speaker_ids == '<no_g>').all() else speaker_ids
 	else:
 		#else Get all npy files in input_dir (supposing they are mels)
-		mel_files  = [os.path.join(args.mels_dir, f) for f in os.listdir(args.mels_dir) if f.split('.')[-1] == 'npy']
+		mel_files  = [os.path.join(input_dir, f) for f in os.listdir(input_dir) if f.split('.')[-1] == 'npy']
 		speaker_ids = None if args.speaker_id is None else args.speaker_id.replace(' ', '').split(',')
 
 		if speaker_ids is not None:
@@ -43,9 +43,9 @@ def run_synthesis(args, checkpoint_path, output_dir, hparams):
 	os.makedirs(log_dir, exist_ok=True)
 	os.makedirs(wav_dir, exist_ok=True)
 
-	mel_files = [mel_files[i: i+hparams.wavenet_synthesis_batch_size] for i in range(0, len(mel_files), hparams.wavenet_synthesis_batch_size)]
-	speaker_ids = None if speaker_ids is None else [speaker_ids[i: i+hparams.wavenet_synthesis_batch_size] for i in range(0, len(speaker_ids), hparams.wavenet_synthesis_batch_size)]
-	texts = None if texts is None else [texts[i: i+hparams.wavenet_synthesis_batch_size] for i in range(0, len(texts), hparams.wavenet_synthesis_batch_size)]
+	mel_files = [mel_files[i: i + hparams.wavenet_synthesis_batch_size] for i in range(0, len(mel_files), hparams.wavenet_synthesis_batch_size)]
+	speaker_ids = None if speaker_ids is None else [speaker_ids[i: i + hparams.wavenet_synthesis_batch_size] for i in range(0, len(speaker_ids), hparams.wavenet_synthesis_batch_size)]
+	texts = None if texts is None else [texts[i: i + hparams.wavenet_synthesis_batch_size] for i in range(0, len(texts), hparams.wavenet_synthesis_batch_size)]
 
 	with open(os.path.join(wav_dir, 'map.txt'), 'w') as file:
 		for i, mel_batch in enumerate(tqdm(mel_files)):
@@ -68,12 +68,13 @@ def run_synthesis(args, checkpoint_path, output_dir, hparams):
 
 
 def wavenet_synthesize(args, hparams, checkpoint):
-	output_dir = 'wavenet_' + args.output_dir
+	input_dir = os.path.join(args.base_dir, 'tacotron_' + args.output_dir, 'eval')
+	output_dir = os.path.join(args.base_dir, 'wavenet_' + args.output_dir)
 
 	try:
 		checkpoint_path = tf.train.get_checkpoint_state(checkpoint).model_checkpoint_path
 		log('loaded model at {}'.format(checkpoint_path))
 	except:
 		raise RuntimeError('Failed to load checkpoint at {}'.format(checkpoint))
 
-	run_synthesis(args, checkpoint_path, output_dir, hparams)
+	run_synthesis(args, checkpoint_path, input_dir, output_dir, hparams)