model.seq2seq.decoder.max_decoder_steps = max_decoder_steps
os.makedirs(dst_dir, exist_ok=True)
with open(text_list_file_path, "rb") as f:
lines = f.readlines()
for idx, line in enumerate(lines):
text = line.decode("utf-8")[:-1]
words = nltk.word_tokenize(text)
waveform, alignment, _, _ = tts(
model, text, p=replace_pronunciation_prob, speaker_id=speaker_id, fast=True)
dst_wav_path = join(dst_dir, "{}_{}{}.wav".format(
idx, checkpoint_name, file_name_suffix))
dst_alignment_path = join(
dst_dir, "{}_{}{}_alignment.png".format(idx, checkpoint_name,
file_name_suffix))
plot_alignment(alignment.T, dst_alignment_path,
info="{}, {}".format(hparams.builder, basename(checkpoint_path)))
audio.save_wav(waveform, dst_wav_path)
from os.path import basename, splitext
name = splitext(basename(text_list_file_path))[0]
if output_html:
print("""
{}
({} chars, {} words)
<audio controls="controls" >
<source src="/audio/{}/{}/{}" autoplay/>
Your browser does not support the audio element.
</audio>
<div align="center"><img src="/audio/{}/{}/{}" /></div>
model.load_state_dict(checkpoint["state_dict"])
model.decoder.max_decoder_steps = max_decoder_steps
model.make_generation_fast_()
os.makedirs(dst_dir, exist_ok=True)
with open(text_list_file_path, "rb") as f:
lines = f.readlines()
for idx, line in enumerate(lines):
text = line.decode("utf-8")[:-1]
words = nltk.word_tokenize(text)
# print("{}: {} ({} chars, {} words)".format(idx, text, len(text), len(words)))
waveform, alignment, _, _ = tts(model, text)
dst_wav_path = join(dst_dir, "{}{}.wav".format(idx, file_name_suffix))
dst_alignment_path = join(dst_dir, "{}{}_alignment.png".format(idx, file_name_suffix))
plot_alignment(alignment.T, dst_alignment_path,
info="deepvoice3, {}".format(checkpoint_path))
audio.save_wav(waveform, dst_wav_path)
from os.path import basename, splitext
name = splitext(basename(text_list_file_path))[0]
print("""
{}
({} chars, {} words)
<audio controls="controls" >
<source src="/audio/deepvoice3/{}/{}{}.wav" autoplay/>
Your browser does not support the audio element.
</audio>
<div align="center"><img src="/audio/deepvoice3/{}/{}{}_alignment.png" /></div>
""".format(text, len(text), len(words),
def synthesis(checkpoint_path, preset, dst_dir, srt_path, face_path):
global _frontend
checkpoint_seq2seq_path = None
checkpoint_postnet_path = None
max_decoder_steps = 500
file_name_suffix = ""
replace_pronunciation_prob = float(0.0)
# Load preset if specified
if preset is not None:
with open(preset) as f:
hparams.parse_json(f.read())
# Override hyper parameters
hparams.parse("")
assert hparams.name == "deepvoice3"
_frontend = getattr(frontend, hparams.frontend)
print(_frontend)
import train
train._frontend = _frontend
from train import plot_alignment, build_model
# Model
model = build_model()
# Load checkpoints separately
if checkpoint_postnet_path is not None and checkpoint_seq2seq_path is not None:
checkpoint = _load(checkpoint_seq2seq_path)
model.seq2seq.load_state_dict(checkpoint["state_dict"])
checkpoint = _load(checkpoint_postnet_path)
model.postnet.load_state_dict(checkpoint["state_dict"])
checkpoint_name = splitext(basename(checkpoint_seq2seq_path))[0]
else:
checkpoint = _load(checkpoint_path)
model.load_state_dict(checkpoint["state_dict"])
checkpoint_name = splitext(basename(checkpoint_path))[0]
model.seq2seq.decoder.max_decoder_steps = max_decoder_steps
os.makedirs(dst_dir, exist_ok=True)
task = load_srt(srt_path, face_path)
idx = 0
for i in task:
speaker_id = i[3]
text = i[4]
words = nltk.word_tokenize(text)
file_name = "{} speaker_{} {}-{}".format(idx, speaker_id, i[1], i[2])
print(text)
waveform, alignment, _, _ = tts(model,
text,
p=replace_pronunciation_prob,
speaker_id=speaker_id,
fast=True)
dst_wav_path = join(dst_dir, "{}.wav".format(file_name))
dst_alignment_path = join(dst_dir,
"{}_alignment.png".format(file_name))
plot_alignment(alignment.T,
dst_alignment_path,
info="{}, {}".format(hparams.builder,
basename(checkpoint_path)))
audio.save_wav(waveform, dst_wav_path)
print(
idx, ": {}\n ({} chars, {} words)".format(text, len(text),
len(words)))
idx += 1
print(
"Finished! Check out {} for generated audio samples.".format(dst_dir))
model.seq2seq.decoder.max_decoder_steps = max_decoder_steps
os.makedirs(out_dir, exist_ok=True)
with open(text_file, "r") as file_reader:
lines = file_reader.readlines()
lines = [line.strip("\n") for line in lines]
for idx, line in enumerate(lines):
text = line[:-1]
waveform, alignment, _, _ = tts(model,
text,
speaker_id=speaker_id,
fast=True)
out_wav_path = join(out_dir,
f"{idx}_{checkpoint_name}_synthesized.wav")
out_alignment_path = join(
out_dir, f"{idx}_{checkpoint_name}_synthesized_alignment.png")
plot_alignment(alignment.T,
out_alignment_path,
info=f"{cfg.builder}, {basename(checkpoint_path)}")
audio.save_wav(waveform, out_wav_path)
print(f"Synthesis complete. Generated audio samples saved in {out_dir}")
sys.exit(0)
