Python hparams.hparams_debug_string() Examples

def run_eval(args, checkpoint_path, output_dir):
	print(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path)
	eval_dir = os.path.join(output_dir, 'eval')
	log_dir = os.path.join(output_dir, 'logs-eval')
	wav = load_wav(args.reference_audio)
	reference_mel = melspectrogram(wav).transpose()
	#Create output path if it doesn't exist
	os.makedirs(eval_dir, exist_ok=True)
	os.makedirs(log_dir, exist_ok=True)
	os.makedirs(os.path.join(log_dir, 'wavs'), exist_ok=True)
	os.makedirs(os.path.join(log_dir, 'plots'), exist_ok=True)

	with open(os.path.join(eval_dir, 'map.txt'), 'w') as file:
		for i, text in enumerate(tqdm(hparams.sentences)):
			start = time.time()
			mel_filename = synth.synthesize(text, i+1, eval_dir, log_dir, None, reference_mel)

			file.write('{}|{}\n'.format(text, mel_filename))
	print('synthesized mel spectrograms at {}'.format(eval_dir)) 

def run_live(args, checkpoint_path, hparams):
	#Log to Terminal without keeping any records in files
	log(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path, hparams)

	#Generate fast greeting message
	greetings = 'Hello, Welcome to the Live testing tool. Please type a message and I will try to read it!'
	log(greetings)
	generate_fast(synth, greetings)

	#Interaction loop
	while True:
		try:
			text = input()
			generate_fast(synth, text)

		except KeyboardInterrupt:
			leave = 'Thank you for testing our features. see you soon.'
			log(leave)
			generate_fast(synth, leave)
			sleep(2)
			break 

def main():
    args = docopt(__doc__)
    print("Command line args:\n", args)
    checkpoint_dir = args["--checkpoint-dir"]
    data_root = args["--data-root"]
    dataset_name = args["--dataset"]
    assert dataset_name in ["blizzard2012", "ljspeech"]
    corpus = importlib.import_module("datasets." + dataset_name)
    corpus_instance = corpus.instantiate(in_dir="", out_dir=data_root)

    hparams.parse(args["--hparams"])
    print(hparams_debug_string())

    tf.logging.set_verbosity(tf.logging.INFO)
    train_and_evaluate(hparams,
                       checkpoint_dir,
                       corpus_instance.training_source_files,
                       corpus_instance.training_target_files,
                       corpus_instance.validation_source_files,
                       corpus_instance.validation_target_files) 

def run_live(args, checkpoint_path, hparams):
	#Log to Terminal without keeping any records in files
	log(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path, hparams)

	#Generate fast greeting message
	greetings = 'Hello, Welcome to the Live testing tool. Please type a message and I will try to read it!'
	log(greetings)
	generate_fast(synth, greetings)

	#Interaction loop
	while True:
		try:
			text = input()
			generate_fast(synth, text)

		except KeyboardInterrupt:
			leave = 'Thank you for testing our features. see you soon.'
			log(leave)
			generate_fast(synth, leave)
			sleep(2)
			break 

def main():
    args = docopt(__doc__)
    print("Command line args:\n", args)
    checkpoint_dir = args["--checkpoint-dir"]
    data_root = args["--data-root"]
    dataset_name = args["--dataset"]
    assert dataset_name in ["blizzard2012", "ljspeech"]
    corpus = importlib.import_module("datasets." + dataset_name)
    corpus_instance = corpus.instantiate(in_dir="", out_dir=data_root)

    hparams.parse(args["--hparams"])
    print(hparams_debug_string())

    tf.logging.set_verbosity(tf.logging.INFO)
    train_and_evaluate(hparams,
                       checkpoint_dir,
                       corpus_instance.training_target_files,
                       corpus_instance.validation_target_files) 

def main():
    args = docopt(__doc__)
    print("Command line args:\n", args)
    checkpoint_dir = args["--checkpoint-dir"]
    postnet_checkpoint_dir = args["--postnet-checkpoint-dir"]
    data_root = args["--data-root"]
    dataset_name = args["--dataset"]
    assert dataset_name in ["blizzard2012"]
    corpus = importlib.import_module("datasets." + dataset_name)
    corpus_instance = corpus.instantiate(in_dir="", out_dir=data_root)

    hparams.parse(args["--hparams"])
    print(hparams_debug_string())

    tf.logging.set_verbosity(tf.logging.INFO)
    predict(hparams,
            checkpoint_dir,
            postnet_checkpoint_dir,
            corpus_instance.test_source_files,
            corpus_instance.test_target_files, ) 

def run_live(args, checkpoint_path, hparams):
	#Log to Terminal without keeping any records in files
	log(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path, hparams)

	#Generate fast greeting message
	greetings = 'Hello, Welcome to the Live testing tool. Please type a message and I will try to read it!'
	log(greetings)
	generate_fast(synth, greetings)

	#Interaction loop
	while True:
		try:
			text = input()
			generate_fast(synth, text)

		except KeyboardInterrupt:
			leave = 'Thank you for testing our features. see you soon.'
			log(leave)
			generate_fast(synth, leave)
			sleep(2)
			break 

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)
	os.makedirs(os.path.join(log_dir, 'wavs'), exist_ok=True)
	os.makedirs(os.path.join(log_dir, 'plots'), exist_ok=True)

	log(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path, hparams)

	delta_size = hparams.tacotron_synthesis_batch_size if hparams.tacotron_synthesis_batch_size < len(sentences) else len(sentences)
	batch_sentences = [sentences[i: i+hparams.tacotron_synthesis_batch_size] for i in range(0, len(sentences), delta_size)]
	start = time.time()
	for i, batch in enumerate(tqdm(batch_sentences)):
		audio.save_wav(synth.eval(batch), os.path.join(log_dir, 'wavs', 'eval_batch_{:03}.wav'.format(i)), hparams)
	log('\nGenerated total batch of {} in {:.3f} sec'.format(delta_size, time.time() - start))	

	return eval_dir 

def run_eval(args, checkpoint_path, output_dir):
	print(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path)
	eval_dir = os.path.join(output_dir, 'eval')
	log_dir = os.path.join(output_dir, 'logs-eval')
	wav = load_wav(args.reference_audio)
	reference_mel = melspectrogram(wav).transpose()
	#Create output path if it doesn't exist
	os.makedirs(eval_dir, exist_ok=True)
	os.makedirs(log_dir, exist_ok=True)
	os.makedirs(os.path.join(log_dir, 'wavs'), exist_ok=True)
	os.makedirs(os.path.join(log_dir, 'plots'), exist_ok=True)

	with open(os.path.join(eval_dir, 'map.txt'), 'w') as file:
		for i, text in enumerate(tqdm(hparams.sentences)):
			start = time.time()
			mel_filename = synth.synthesize(text, i+1, eval_dir, log_dir, None, reference_mel)

			file.write('{}|{}\n'.format(text, mel_filename))
	print('synthesized mel spectrograms at {}'.format(eval_dir)) 

def run_eval(args):
  print(hparams_debug_string())
  synth = Synthesizer()
  synth.load(args.checkpoint)
  base_path = get_output_base_path(args.checkpoint)
  for i, text in enumerate(sentences):
    path = '%s-%d.wav' % (base_path, i)
    print('Synthesizing: %s' % path)
    with open(path, 'wb') as f:
      f.write(synth.synthesize(text)) 

def main():
    args = docopt(__doc__)
    print("Command line args:\n", args)
    checkpoint_dir = args["--checkpoint-dir"]
    checkpoint_path = args["--checkpoint"]
    source_data_root = args["--source-data-root"]
    target_data_root = args["--target-data-root"]
    selected_list_dir = args["--selected-list-dir"]
    output_dir = args["--output-dir"]
    selected_list_filename = args["--selected-list-filename"] or "test.csv"

    tf.logging.set_verbosity(tf.logging.INFO)

    if args["--hparam-json-file"]:
        with open(args["--hparam-json-file"]) as f:
            json = "".join(f.readlines())
            hparams.parse_json(json)

    hparams.parse(args["--hparams"])
    tf.logging.info(hparams_debug_string())

    tf.logging.info(f"A selected list file to use: {os.path.join(selected_list_dir, selected_list_filename)}")

    test_list = list(load_key_list(selected_list_filename, selected_list_dir))

    test_source_files = [os.path.join(source_data_root, f"{key}.{hparams.source_file_extension}") for key in
                         test_list]
    test_target_files = [os.path.join(target_data_root, f"{key}.{hparams.target_file_extension}") for key in
                         test_list]

    predict(hparams,
            checkpoint_dir,
            checkpoint_path,
            output_dir,
            test_source_files,
            test_target_files) 

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)

	#Create output path if it doesn't exist
	os.makedirs(eval_dir, exist_ok=True)
	os.makedirs(log_dir, exist_ok=True)
	os.makedirs(os.path.join(log_dir, 'wavs'), exist_ok=True)
	os.makedirs(os.path.join(log_dir, 'plots'), exist_ok=True)

	log(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path, hparams)

	#Set inputs batch wise
	sentences = [sentences[i: i+hparams.tacotron_synthesis_batch_size] for i in range(0, len(sentences), hparams.tacotron_synthesis_batch_size)]

	log('Starting Synthesis')
	with open(os.path.join(eval_dir, 'map.txt'), 'w') as file:
		for i, texts in enumerate(tqdm(sentences)):
			start = time.time()
			basenames = ['batch_{}_sentence_{}'.format(i, j) for j in range(len(texts))]
			mel_filenames, speaker_ids = synth.synthesize(texts, basenames, eval_dir, log_dir, None)

			for elems in zip(texts, mel_filenames, speaker_ids):
				file.write('|'.join([str(x) for x in elems]) + '\n')
	log('synthesized mel spectrograms at {}'.format(eval_dir))
	return eval_dir 

def run_synthesis(args, checkpoint_path, output_dir):
	metadata_filename = os.path.join(args.input_dir, 'train.txt')
	print(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path, gta=args.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)
		print('Loaded metadata for {} examples ({:.2f} hours)'.format(len(metadata), hours))

	if args.GTA==True:
		synth_dir = os.path.join(output_dir, 'gta')
	else:
		synth_dir = os.path.join(output_dir, 'natural')

	#Create output path if it doesn't exist
	os.makedirs(synth_dir, exist_ok=True)

	print('starting synthesis')
	mel_dir = os.path.join(args.input_dir, 'mels')
	wav_dir = os.path.join(args.input_dir, 'audio')
	with open(os.path.join(synth_dir, 'map.txt'), 'w') as file:
		for i, meta in enumerate(tqdm(metadata)):
			text = meta[5]
			mel_filename = os.path.join(mel_dir, meta[1])
			wav_filename = os.path.join(wav_dir, meta[0])
			mel_output_filename = synth.synthesize(text, None, i+1, synth_dir, None, mel_filename)

			file.write('{}|{}|{}|{}\n'.format(text, mel_filename, mel_output_filename, wav_filename))
	print('synthesized mel spectrograms at {}'.format(synth_dir)) 

def run_eval(args):
  print(hparams_debug_string())
  synth = Synthesizer()
  synth.load(args.checkpoint)
  base_path = get_output_base_path(args.checkpoint)
  wav = load_wav(args.reference_audio)
  mel = melspectrogram(wav).transpose()
  for i, text in enumerate(tests):
    path = '%s-%d.wav' % (base_path, i)
    print('Synthesizing: %s' % path)
    with open(path, 'wb') as f:
      f.write(synth.synthesize(text, mel)) 

def run_synthesis(args, checkpoint_path, output_dir, hparams):
	GTA = (args.GTA == 'True')
	if GTA:
		synth_dir = os.path.join(output_dir, 'gta')

		#Create output path if it doesn't exist
		os.makedirs(synth_dir, exist_ok=True)
	else:
		synth_dir = os.path.join(output_dir, 'natural')

		#Create output path if it doesn't exist
		os.makedirs(synth_dir, exist_ok=True)


	metadata_filename = os.path.join(args.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)]

	log('starting synthesis')
	mel_dir = os.path.join(args.input_dir, 'mels')
	wav_dir = os.path.join(args.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]
			mel_filenames = [os.path.join(mel_dir, m[1]) for m in meta]
			wav_filenames = [os.path.join(wav_dir, m[0]) for m in meta]
			basenames = [os.path.basename(m).replace('.npy', '').replace('mel-', '') for m in mel_filenames]
			mel_output_filenames, speaker_ids = synth.synthesize(texts, basenames, synth_dir, None, mel_filenames)

			for elems in zip(wav_filenames, mel_filenames, mel_output_filenames, speaker_ids, texts):
				file.write('|'.join([str(x) for x in elems]) + '\n')
	log('synthesized mel spectrograms at {}'.format(synth_dir))
	return os.path.join(synth_dir, 'map.txt') 

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)

	#Create output path if it doesn't exist
	os.makedirs(eval_dir, exist_ok=True)
	os.makedirs(log_dir, exist_ok=True)
	os.makedirs(os.path.join(log_dir, 'wavs'), exist_ok=True)
	os.makedirs(os.path.join(log_dir, 'plots'), exist_ok=True)

	log(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path, hparams)

	#Set inputs batch wise
	sentences = [sentences[i: i+hparams.tacotron_synthesis_batch_size] for i in range(0, len(sentences), hparams.tacotron_synthesis_batch_size)]

	log('Starting Synthesis')
	with open(os.path.join(eval_dir, 'map.txt'), 'w') as file:
		for i, texts in enumerate(tqdm(sentences)):
			start = time.time()
			basenames = ['batch_{}_sentence_{}'.format(i, j) for j in range(len(texts))]
			mel_filenames, speaker_ids = synth.synthesize(texts, basenames, eval_dir, log_dir, None)

			for elems in zip(texts, mel_filenames, speaker_ids):
				file.write('|'.join([str(x) for x in elems]) + '\n')
	log('synthesized mel spectrograms at {}'.format(eval_dir))
	return eval_dir 

def run_eval(args):
  print(hparams_debug_string())
  synth = Synthesizer()
  synth.load(args.checkpoint)
  base_path = get_output_base_path(args.checkpoint)
  for i, text in enumerate(sentences):
    path = '%s-%d.wav' % (base_path, i)
    print('Synthesizing: %s' % path)
    with open(path, 'wb') as f:
      f.write(synth.synthesize(text)) 

def run_eval(args):
  print(hparams_debug_string())
  synth = Synthesizer()
  synth.load(args.checkpoint)
  base_path = get_output_base_path(args.checkpoint)
  for i, text in enumerate(sentences):
    path = '%s-%d.wav' % (base_path, i)
    print('Synthesizing: %s' % path)
    with open(path, 'wb') as f:
      f.write(synth.synthesize(text)) 

def run_synthesis(args, checkpoint_path, output_dir):
	metadata_filename = os.path.join(args.input_dir, 'train.txt')
	print(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path, gta=args.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)
		print('Loaded metadata for {} examples ({:.2f} hours)'.format(len(metadata), hours))

	if args.GTA==True:
		synth_dir = os.path.join(output_dir, 'gta')
	else:
		synth_dir = os.path.join(output_dir, 'natural')

	#Create output path if it doesn't exist
	os.makedirs(synth_dir, exist_ok=True)

	print('starting synthesis')
	mel_dir = os.path.join(args.input_dir, 'mels')
	wav_dir = os.path.join(args.input_dir, 'audio')
	with open(os.path.join(synth_dir, 'map.txt'), 'w') as file:
		for i, meta in enumerate(tqdm(metadata)):
			text = meta[5]
			mel_filename = os.path.join(mel_dir, meta[1])
			wav_filename = os.path.join(wav_dir, meta[0])
			mel_output_filename = synth.synthesize(text, None, i+1, synth_dir, None, mel_filename)

			file.write('{}|{}|{}|{}\n'.format(text, mel_filename, mel_output_filename, wav_filename))
	print('synthesized mel spectrograms at {}'.format(synth_dir)) 

def run_synthesis(args, checkpoint_path, output_dir, hparams):
	GTA = (args.GTA == 'True')
	if GTA:
		synth_dir = os.path.join(output_dir, 'gta')

		#Create output path if it doesn't exist
		os.makedirs(synth_dir, exist_ok=True)
	else:
		synth_dir = os.path.join(output_dir, 'natural')

		#Create output path if it doesn't exist
		os.makedirs(synth_dir, exist_ok=True)


	metadata_filename = os.path.join(args.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))

	#Set inputs batch wise
	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')
	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]
			mel_filenames = [os.path.join(mel_dir, m[1]) for m in meta]
			wav_filenames = [os.path.join(wav_dir, m[0]) for m in meta]
			basenames = [os.path.basename(m).replace('.npy', '').replace('mel-', '') for m in mel_filenames]
			mel_output_filenames, speaker_ids = synth.synthesize(texts, basenames, synth_dir, None, mel_filenames)

			for elems in zip(wav_filenames, mel_filenames, mel_output_filenames, speaker_ids, texts):
				file.write('|'.join([str(x) for x in elems]) + '\n')
	log('synthesized mel spectrograms at {}'.format(synth_dir))
	return os.path.join(synth_dir, 'map.txt') 

def run_synthesis(args, checkpoint_path, output_dir, hparams):
	log_dir = os.path.join(output_dir, 'plots')
	wav_dir = os.path.join(output_dir, 'wavs')

	#We suppose user will provide correct folder depending on training method
	log(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path, 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')
		with open(metadata_filename, encoding='utf-8') as f:
			metadata = np.array([line.strip().split('|') for line in f])

		speaker_ids = metadata[:, 2]
		mel_files = metadata[:, 1]
		texts = metadata[:, 0]

		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  = sorted([os.path.join(args.mels_dir, f) for f in os.listdir(args.mels_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:
			assert len(speaker_ids) == len(mel_files)

		texts = None

	log('Starting synthesis! (this will take a while..)')
	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)]

	with open(os.path.join(wav_dir, 'map.txt'), 'w') as file:
		for i, mel_batch in enumerate(tqdm(mel_files)):
			mel_spectros = [np.load(mel_file) for mel_file in mel_batch]

			basenames = [os.path.basename(mel_file).replace('.npy', '') for mel_file in mel_batch]
			speaker_id_batch = None if speaker_ids is None else speaker_ids[i]
			audio_files = synth.synthesize(mel_spectros, speaker_id_batch, basenames, wav_dir, log_dir)

			speaker_logs = ['<no_g>'] * len(mel_batch) if speaker_id_batch is None else speaker_id_batch

			for j, mel_file in enumerate(mel_batch):
				if texts is None:
					file.write('{}|{}\n'.format(mel_file, audio_files[j], speaker_logs[j]))
				else:
					file.write('{}|{}|{}\n'.format(texts[i][j], mel_file, audio_files[j], speaker_logs[j]))

	log('synthesized audio waveforms at {}'.format(wav_dir)) 

def run_synthesis(args, checkpoint_path, output_dir, hparams):
	log_dir = os.path.join(output_dir, 'plots')
	wav_dir = os.path.join(output_dir, 'wavs')

	#We suppose user will provide correct folder depending on training method
	log(hparams_debug_string())
	synth = Synthesizer()
	synth.load(checkpoint_path, 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')
		with open(metadata_filename, encoding='utf-8') as f:
			metadata = np.array([line.strip().split('|') for line in f])

		speaker_ids = metadata[:, 2]
		mel_files = metadata[:, 1]
		texts = metadata[:, 0]

		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  = sorted([os.path.join(args.mels_dir, f) for f in os.listdir(args.mels_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:
			assert len(speaker_ids) == len(mel_files)

		texts = None

	log('Starting synthesis! (this will take a while..)')
	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)]

	with open(os.path.join(wav_dir, 'map.txt'), 'w') as file:
		for i, mel_batch in enumerate(tqdm(mel_files)):
			mel_spectros = [np.load(mel_file) for mel_file in mel_batch]

			basenames = [os.path.basename(mel_file).replace('.npy', '') for mel_file in mel_batch]
			speaker_id_batch = None if speaker_ids is None else speaker_ids[i]
			audio_files = synth.synthesize(mel_spectros, speaker_id_batch, basenames, wav_dir, log_dir)

			speaker_logs = ['<no_g>'] * len(mel_batch) if speaker_id_batch is None else speaker_id_batch

			for j, mel_file in enumerate(mel_batch):
				if texts is None:
					file.write('{}|{}\n'.format(mel_file, audio_files[j], speaker_logs[j]))
				else:
					file.write('{}|{}|{}\n'.format(texts[i][j], mel_file, audio_files[j], speaker_logs[j]))

	log('synthesized audio waveforms at {}'.format(wav_dir)) 

def run_synthesis(args, checkpoint_path, output_dir, hparams):
	GTA = (args.GTA == 'True')
	if GTA:
		synth_dir = os.path.join(output_dir, 'gta')

		#Create output path if it doesn't exist
		os.makedirs(synth_dir, exist_ok=True)
	else:
		synth_dir = os.path.join(output_dir, 'natural')

		#Create output path if it doesn't exist
		os.makedirs(synth_dir, exist_ok=True)


	metadata_filename = os.path.join(args.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))

	#Set inputs batch wise
	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')
	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]
			mel_filenames = [os.path.join(mel_dir, m[1]) for m in meta]
			wav_filenames = [os.path.join(wav_dir, m[0]) for m in meta]
			basenames = [os.path.basename(m).replace('.npy', '').replace('mel-', '') for m in mel_filenames]
			mel_output_filenames, speaker_ids = synth.synthesize(texts, basenames, synth_dir, None, mel_filenames)

			for elems in zip(wav_filenames, mel_filenames, mel_output_filenames, speaker_ids, texts):
				file.write('|'.join([str(x) for x in elems]) + '\n')
	log('synthesized mel spectrograms at {}'.format(synth_dir))
	return os.path.join(synth_dir, 'map.txt') 

def main():
    args = docopt(__doc__)
    print("Command line args:\n", args)
    checkpoint_dir = args["--checkpoint-dir"]
    source_data_root = args["--source-data-root"]
    target_data_root = args["--target-data-root"]
    selected_list_dir = args["--selected-list-dir"]
    use_multi_gpu = args["--multi-gpus"]

    if args["--hparam-json-file"]:
        with open(args["--hparam-json-file"]) as f:
            json = "".join(f.readlines())
            hparams.parse_json(json)

    hparams.parse(args["--hparams"])

    training_list = list(load_key_list("train.csv", selected_list_dir))
    validation_list = list(load_key_list("validation.csv", selected_list_dir))

    training_source_files = [os.path.join(source_data_root, f"{key}.{hparams.source_file_extension}") for key in
                             training_list]
    training_target_files = [os.path.join(target_data_root, f"{key}.{hparams.target_file_extension}") for key in
                             training_list]
    validation_source_files = [os.path.join(source_data_root, f"{key}.{hparams.source_file_extension}") for key in
                               validation_list]
    validation_target_files = [os.path.join(target_data_root, f"{key}.{hparams.target_file_extension}") for key in
                               validation_list]

    log = logging.getLogger("tensorflow")
    log.setLevel(logging.INFO)
    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    fh = logging.FileHandler(hparams.logfile)
    fh.setLevel(logging.INFO)
    fh.setFormatter(formatter)
    log.addHandler(fh)
    tf.logging.set_verbosity(tf.logging.INFO)

    tf.logging.info(hparams_debug_string())

    train_and_evaluate(hparams,
                       checkpoint_dir,
                       training_source_files,
                       training_target_files,
                       validation_source_files,
                       validation_target_files,
                       use_multi_gpu)