def tts(model, text, p=0, speaker_id=0, fast=True, figures=True):
from synthesis import tts as _tts
waveform, alignment, spectrogram, mel = _tts(model, text, p, speaker_id,
fast)
if figures:
visualize(alignment, spectrogram)
IPython.display.display(Audio(waveform, rate=fs))
def tts(model, text, p=0, speaker_id=0, fast=True, figures=True):
from synthesis import tts as _tts
waveform, alignment, spectrogram, mel = _tts(model, text, p, speaker_id, fast)
if figures:
visualize(alignment, spectrogram)
# IPython.display.display(Audio(waveform, rate=fs))
librosa.output.write_wav('out/speaker' + str(speaker_id) + '.wav', waveform, fs)
def tts(model, text, p=0, speaker_id=None, fast=True, figures=True):
from synthesis import tts as _tts
waveform, alignment, spectrogram, mel = _tts(model, text, p, speaker_id,
fast)
if figures:
visualize(alignment, spectrogram)
sd.play(waveform, 22500)
sd.wait()
def tts(model, text, file_path, p=0, speaker_id=None, fast=True):
from synthesis import tts as _tts
import audio
waveform, alignment, spectrogram, mel = _tts(model, text, p, speaker_id,
fast)
# 22050, 353 kbps, 16 bit, mono
audio.save_wav(waveform, file_path)
def generate_cloned_samples(cloning_texts_location=None,
no_speakers=108,
fast=True,
p=0):
# Clone
name = "deepvoice3_pytorch"
# if not exists(name):
# print("Clone the repo!!")
# else:
# print("Exists!")
# Change working directory to the project dir
os.chdir(join(expanduser("."), name))
import hparams
import json
import synthesis
import train
from deepvoice3_pytorch import frontend
from train import build_model
from train import restore_parts, load_checkpoint
from synthesis import tts as _tts
# get_ipython().system(u' python3 -m nltk.downloader cmudict')
checkpoint_path = "20171222_deepvoice3_vctk108_checkpoint_step000300000.pth"
if not exists(checkpoint_path):
print("Dowload the Pre-Trained Network!!")
# !curl -O -L "https://www.dropbox.com/s/uzmtzgcedyu531k/20171222_deepvoice3_vctk108_checkpoint_step000300000.pth"
# Copy preset file (json) from master
# The preset file describes hyper parameters
# get_ipython().system(u' git checkout master --quiet')
preset = "./presets/deepvoice3_vctk.json"
# get_ipython().system(u' cp -v $preset .')
# preset = "./deepvoice3_vctk.json"
# And then git checkout to the working commit
# This is due to the model was trained a few months ago and it's not compatible
# with the current master.
# ! git checkout 0421749 --quiet
# ! pip install -q -e .
# print(hparams.hparams.get_model_structure())
# Newly added params. Need to inject dummy values
for dummy, v in [("fmin", 0), ("fmax", 0), ("rescaling", False),
("rescaling_max", 0.999),
("allow_clipping_in_normalization", False)]:
#if hparams.hparams.get(dummy) is None:
hparams.hparams.add_hparam(dummy, v)
# Load parameters from preset
with open(preset) as f:
hparams.hparams.parse_json(f.read())
# Tell we are using multi-speaker DeepVoice3
hparams.hparams.builder = "deepvoice3_multispeaker"
# Inject frontend text processor
synthesis._frontend = getattr(frontend, "en")
train._frontend = getattr(frontend, "en")
# alises
fs = hparams.hparams.sample_rate
hop_length = hparams.hparams.hop_size
model = build_model()
model = load_checkpoint(checkpoint_path, model, None, True)
# text = "here i am"
# speaker_id = 0
# fast = True
# p = 0
# waveform, alignment, spectrogram, mel = _tts(model, text, p, speaker_id, fast)
# print(waveform.shape)
# print(alignment.shape)
# print(spectrogram.shape)
# print(mel.shape)
# print(type(mel))
cloning_texts = ["this is the first", "this is the first"]
if (cloning_texts_location == None):
cloning_texts_location = "./Cloning_Audio/cloning_text.txt"
# cloning_texts = open("./Cloning_Audio/cloning_text.txt").splitlines()
# no_cloning_texts = len(cloning_texts)
all_speakers = []
for speaker_id in range(no_speakers):
speaker_cloning_mel = []
print("The Speaker being cloned speaker-{}".format(speaker_id))
for text in cloning_texts:
waveform, alignment, spectrogram, mel = _tts(
model, text, p, speaker_id, fast)
speaker_cloning_mel.append(mel)
#print(np.array(speaker_cloning_mel).shape)
all_speakers.append(speaker_cloning_mel)
with open("./Cloning_Audio/speakers_cloned_voices_mel.p",
"wb") as fp: #Pickling
pickle.dump(all_speakers, fp)
print("")
print(np.array(all_speakers).shape)
# print(all_speakers.shape)
# all speakers[speaker_id][cloned_audio_number]
# print(all_speakers[0][1].shape)
return all_speakers
def tts(model, text, p=0, speaker_id=5, fast=True, figures=True):
from synthesis import tts as _tts
waveform, alignment, spectrogram, mel = _tts(model, text, p, speaker_id,
fast)
return waveform
def tts(model, text, filename, p=0, speaker_id=None, fast=True, figures=True):
from synthesis import tts as _tts
waveform, alignment, _, mel = _tts(model, text, p, speaker_id, fast)
# waveform /= np.max(np.abs(waveform))
save_wav(waveform, filename)
