class Toolbox:
def __init__(self, datasets_root, enc_models_dir, syn_models_dir,
voc_models_dir, low_mem):
sys.excepthook = self.excepthook
self.datasets_root = datasets_root
self.low_mem = low_mem
self.utterances = set()
self.current_generated = (None, None, None, None
) # speaker_name, spec, breaks, wav
self.synthesizer = None # type: Synthesizer
# Initialize the events and the interface
self.ui = UI()
self.reset_ui(enc_models_dir, syn_models_dir, voc_models_dir)
self.setup_events()
self.ui.start()
def excepthook(self, exc_type, exc_value, exc_tb):
traceback.print_exception(exc_type, exc_value, exc_tb)
self.ui.log("Exception: %s" % exc_value)
def setup_events(self):
# Dataset, speaker and utterance selection
self.ui.browser_load_button.clicked.connect(
lambda: self.load_from_browser())
random_func = lambda level: lambda: self.ui.populate_browser(
self.datasets_root, recognized_datasets, level)
self.ui.random_dataset_button.clicked.connect(random_func(0))
self.ui.random_speaker_button.clicked.connect(random_func(1))
self.ui.random_utterance_button.clicked.connect(random_func(2))
self.ui.dataset_box.currentIndexChanged.connect(random_func(1))
self.ui.speaker_box.currentIndexChanged.connect(random_func(2))
# Model selection
self.ui.encoder_box.currentIndexChanged.connect(self.init_encoder)
def func():
self.synthesizer = None
self.ui.synthesizer_box.currentIndexChanged.connect(func)
self.ui.vocoder_box.currentIndexChanged.connect(self.init_vocoder)
# Utterance selection
func = lambda: self.load_from_browser(self.ui.browse_file())
self.ui.browser_browse_button.clicked.connect(func)
func = lambda: self.ui.draw_utterance(self.ui.selected_utterance,
"current")
self.ui.utterance_history.currentIndexChanged.connect(func)
func = lambda: self.ui.play(self.ui.selected_utterance.wav, Synthesizer
.sample_rate)
self.ui.play_button.clicked.connect(func)
self.ui.stop_button.clicked.connect(self.ui.stop)
self.ui.record_button.clicked.connect(self.record)
# Generation
func = lambda: self.synthesize() or self.vocode()
self.ui.generate_button.clicked.connect(func)
self.ui.synthesize_button.clicked.connect(self.synthesize)
self.ui.vocode_button.clicked.connect(self.vocode)
# UMAP legend
self.ui.clear_button.clicked.connect(self.clear_utterances)
def reset_ui(self, encoder_models_dir, synthesizer_models_dir,
vocoder_models_dir):
self.ui.populate_browser(self.datasets_root, recognized_datasets, 0,
True)
self.ui.populate_models(encoder_models_dir, synthesizer_models_dir,
vocoder_models_dir)
def load_from_browser(self, fpath=None):
if fpath is None:
fpath = Path(self.datasets_root, self.ui.current_dataset_name,
self.ui.current_speaker_name,
self.ui.current_utterance_name)
name = str(fpath.relative_to(self.datasets_root))
speaker_name = self.ui.current_dataset_name + '_' + self.ui.current_speaker_name
# Select the next utterance
if self.ui.auto_next_checkbox.isChecked():
self.ui.browser_select_next()
elif fpath == "":
return
else:
name = fpath.name
speaker_name = fpath.parent.name
# Get the wav from the disk. We take the wav with the vocoder/synthesizer format for
# playback, so as to have a fair comparison with the generated audio
wav = Synthesizer.load_preprocess_wav(fpath)
self.ui.log("Loaded %s" % name)
self.add_real_utterance(wav, name, speaker_name)
def record(self):
wav = self.ui.record_one(encoder.sampling_rate, 5)
if wav is None:
return
self.ui.play(wav, encoder.sampling_rate)
speaker_name = "user01"
name = speaker_name + "_rec_%05d" % np.random.randint(100000)
self.add_real_utterance(wav, name, speaker_name)
def add_real_utterance(self, wav, name, speaker_name):
# Compute the mel spectrogram
spec = Synthesizer.make_spectrogram(wav)
self.ui.draw_spec(spec, "current")
# Compute the embedding
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.load_preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(
encoder_wav, return_partials=True)
# Add the utterance
utterance = Utterance(name, speaker_name, wav, spec, embed,
partial_embeds, False)
self.utterances.add(utterance)
self.ui.register_utterance(utterance)
# Plot it
self.ui.draw_embed(embed, name, "current")
self.ui.draw_umap_projections(self.utterances)
def clear_utterances(self):
self.utterances.clear()
self.ui.draw_umap_projections(self.utterances)
def synthesize(self):
self.ui.log("Generating the mel spectrogram...")
self.ui.set_loading(1)
# Synthesize the spectrogram
if self.synthesizer is None:
model_dir = self.ui.current_synthesizer_model_dir
checkpoints_dir = model_dir.joinpath("taco_pretrained")
self.synthesizer = Synthesizer(checkpoints_dir,
low_mem=self.low_mem)
if not self.synthesizer.is_loaded():
self.ui.log("Loading the synthesizer %s" %
self.synthesizer.checkpoint_fpath)
texts = self.ui.text_prompt.toPlainText().split("\n")
embed = self.ui.selected_utterance.embed
embeds = np.stack([embed] * len(texts))
specs = self.synthesizer.synthesize_spectrograms(texts, embeds)
breaks = [spec.shape[1] for spec in specs]
spec = np.concatenate(specs, axis=1)
self.ui.draw_spec(spec, "generated")
self.current_generated = (self.ui.selected_utterance.speaker_name,
spec, breaks, None)
self.ui.set_loading(0)
def vocode(self):
speaker_name, spec, breaks, _ = self.current_generated
assert spec is not None
# Synthesize the waveform
if not vocoder.is_loaded():
self.init_vocoder()
def vocoder_progress(i, seq_len, b_size, gen_rate):
real_time_factor = (gen_rate / Synthesizer.sample_rate) * 1000
line = "Waveform generation: %d/%d (batch size: %d, rate: %.1fkHz - %.2fx real time)" \
% (i * b_size, seq_len * b_size, b_size, gen_rate, real_time_factor)
self.ui.log(line, "overwrite")
self.ui.set_loading(i, seq_len)
if self.ui.current_vocoder_fpath is not None:
self.ui.log("")
wav = vocoder.infer_waveform(spec,
progress_callback=vocoder_progress)
else:
self.ui.log("Waveform generation with Griffin-Lim... ")
wav = Synthesizer.griffin_lim(spec)
self.ui.set_loading(0)
self.ui.log(" Done!", "append")
# Add breaks
b_ends = np.cumsum(np.array(breaks) * Synthesizer.hparams.hop_size)
b_starts = np.concatenate(([0], b_ends[:-1]))
wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)]
breaks = [np.zeros(int(0.15 * Synthesizer.sample_rate))] * len(breaks)
wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])
# Play it
wav = wav / np.abs(wav).max() * 0.97
self.ui.play(wav, Synthesizer.sample_rate)
# Compute the embedding
# TODO: this is problematic with different sampling rates, gotta fix it
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.load_preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(
encoder_wav, return_partials=True)
# Add the utterance
name = speaker_name + "_gen_%05d" % np.random.randint(100000)
utterance = Utterance(name, speaker_name, wav, spec, embed,
partial_embeds, True)
self.utterances.add(utterance)
# Plot it
self.ui.draw_embed(embed, name, "generated")
self.ui.draw_umap_projections(self.utterances)
def init_encoder(self):
model_fpath = self.ui.current_encoder_fpath
self.ui.log("Loading the encoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
encoder.load_model(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
def init_vocoder(self):
model_fpath = self.ui.current_vocoder_fpath
# Case of Griffin-lim
if model_fpath is None:
return
self.ui.log("Loading the vocoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
vocoder.load_model(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
class Toolbox:
def __init__(self, datasets_root, enc_models_dir, syn_models_dir, voc_models_dir, low_mem, seed, no_mp3_support):
if not no_mp3_support:
try:
librosa.load("samples/6829_00000.mp3")
except NoBackendError:
print("Librosa will be unable to open mp3 files if additional software is not installed.\n"
"Please install ffmpeg or add the '--no_mp3_support' option to proceed without support for mp3 files.")
exit(-1)
self.no_mp3_support = no_mp3_support
sys.excepthook = self.excepthook
self.datasets_root = datasets_root
self.low_mem = low_mem
self.utterances = set()
self.current_generated = (None, None, None, None) # speaker_name, spec, breaks, wav
self.synthesizer = None # type: Synthesizer
self.current_wav = None
self.waves_list = []
self.waves_count = 0
self.waves_namelist = []
# Check for webrtcvad (enables removal of silences in vocoder output)
try:
import webrtcvad
self.trim_silences = True
except:
self.trim_silences = False
# Initialize the events and the interface
self.ui = UI()
self.reset_ui(enc_models_dir, syn_models_dir, voc_models_dir, seed)
self.setup_events()
self.ui.start()
def excepthook(self, exc_type, exc_value, exc_tb):
traceback.print_exception(exc_type, exc_value, exc_tb)
self.ui.log("Exception: %s" % exc_value)
def setup_events(self):
# Dataset, speaker and utterance selection
self.ui.browser_load_button.clicked.connect(lambda: self.load_from_browser())
random_func = lambda level: lambda: self.ui.populate_browser(self.datasets_root,
recognized_datasets,
level)
self.ui.random_dataset_button.clicked.connect(random_func(0))
self.ui.random_speaker_button.clicked.connect(random_func(1))
self.ui.random_utterance_button.clicked.connect(random_func(2))
self.ui.dataset_box.currentIndexChanged.connect(random_func(1))
self.ui.speaker_box.currentIndexChanged.connect(random_func(2))
# Model selection
self.ui.encoder_box.currentIndexChanged.connect(self.init_encoder)
def func():
self.synthesizer = None
self.ui.synthesizer_box.currentIndexChanged.connect(func)
self.ui.vocoder_box.currentIndexChanged.connect(self.init_vocoder)
# Utterance selection
func = lambda: self.load_from_browser(self.ui.browse_file())
self.ui.browser_browse_button.clicked.connect(func)
#Audio book
func = lambda: self.load_book(self.ui.browse_book())
self.ui.load_book.clicked.connect(func)
func = lambda: self.synthBook()
self.ui.synth_book.clicked.connect(func)
#
func = lambda: self.ui.draw_utterance(self.ui.selected_utterance, "current")
self.ui.utterance_history.currentIndexChanged.connect(func)
func = lambda: self.ui.play(self.ui.selected_utterance.wav, Synthesizer.sample_rate)
self.ui.play_button.clicked.connect(func)
self.ui.stop_button.clicked.connect(self.ui.stop)
self.ui.record_button.clicked.connect(self.record)
#Audio
self.ui.setup_audio_devices(Synthesizer.sample_rate)
#Wav playback & save
func = lambda: self.replay_last_wav()
self.ui.replay_wav_button.clicked.connect(func)
func = lambda: self.export_current_wave()
self.ui.export_wav_button.clicked.connect(func)
self.ui.waves_cb.currentIndexChanged.connect(self.set_current_wav)
# Generation
func = lambda: self.synthesize() or self.vocode()
self.ui.generate_button.clicked.connect(func)
self.ui.synthesize_button.clicked.connect(self.synthesize)
self.ui.vocode_button.clicked.connect(self.vocode)
self.ui.random_seed_checkbox.clicked.connect(self.update_seed_textbox)
# UMAP legend
self.ui.clear_button.clicked.connect(self.clear_utterances)
def set_current_wav(self, index):
self.current_wav = self.waves_list[index]
def export_current_wave(self):
self.ui.save_audio_file(self.current_wav, Synthesizer.sample_rate)
def replay_last_wav(self):
self.ui.play(self.current_wav, Synthesizer.sample_rate)
def reset_ui(self, encoder_models_dir, synthesizer_models_dir, vocoder_models_dir, seed):
self.ui.populate_browser(self.datasets_root, recognized_datasets, 0, True)
self.ui.populate_models(encoder_models_dir, synthesizer_models_dir, vocoder_models_dir)
self.ui.populate_gen_options(seed, self.trim_silences)
def load_from_browser(self, fpath=None):
if fpath is None:
fpath = Path(self.datasets_root,
self.ui.current_dataset_name,
self.ui.current_speaker_name,
self.ui.current_utterance_name)
name = str(fpath.relative_to(self.datasets_root))
speaker_name = self.ui.current_dataset_name + '_' + self.ui.current_speaker_name
# Select the next utterance
if self.ui.auto_next_checkbox.isChecked():
self.ui.browser_select_next()
elif fpath == "":
return
else:
name = fpath.name
speaker_name = fpath.parent.name
if fpath.suffix.lower() == ".mp3" and self.no_mp3_support:
self.ui.log("Error: No mp3 file argument was passed but an mp3 file was used")
return
# Get the wav from the disk. We take the wav with the vocoder/synthesizer format for
# playback, so as to have a fair comparison with the generated audio
wav = Synthesizer.load_preprocess_wav(fpath)
self.ui.log("Loaded %s" % name)
self.add_real_utterance(wav, name, speaker_name)
def load_book(self, fpath=None):
if(fpath is None or fpath == ""):
return
if(fpath.suffix.lower() == ".txt"):
self.ui.text_prompt.clear()
file = open(fpath.absolute().as_posix(),mode='r')
text = file.read()
text = text.replace(".",".\n")
text = [line for line in text.split('\n') if line.strip() != '']
sep = '\n'
text = sep.join(text)
#text = text.replace(",",",\n")
#text = text.replace(":",":\n")
#text = text.replace("-","-\n")
#text = text.replace("!","!\n")
#text = text.replace("?","?\n)
file.close()
self.ui.text_prompt.appendPlainText(text)
else:
self.ui.log("Format is not supported")
return
def synthBook(self):
k = 0
self.ui.log('Book synthesis start')
texts = self.ui.text_prompt.toPlainText().split("\n")
n = 2
for i in range(0, len(texts), n):
newList = texts[i:i + n]
sep = '\n'
newText = sep.join(newList)
#self.ui.log(newText)
self.ui.text_prompt.clear()
self.ui.text_prompt.appendPlainText(newText)
self.synthesize()
self.vocode()
percent = str(((k+1)/(len(texts)/n)*100))
self.ui.log("Done for " + percent +"%")
sf.write("output/{}.wav".format(k), self.current_wav, Synthesizer.sample_rate)
k+=1
def record(self):
wav = self.ui.record_one(encoder.sampling_rate, 5)
if wav is None:
return
self.ui.play(wav, encoder.sampling_rate)
speaker_name = "user01"
name = speaker_name + "_rec_%05d" % np.random.randint(100000)
self.add_real_utterance(wav, name, speaker_name)
def add_real_utterance(self, wav, name, speaker_name):
# Compute the mel spectrogram
spec = Synthesizer.make_spectrogram(wav)
self.ui.draw_spec(spec, "current")
# Compute the embedding
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
# Add the utterance
utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, False)
self.utterances.add(utterance)
self.ui.register_utterance(utterance)
# Plot it
self.ui.draw_embed(embed, name, "current")
self.ui.draw_umap_projections(self.utterances)
def clear_utterances(self):
self.utterances.clear()
self.ui.draw_umap_projections(self.utterances)
def synthesize(self):
self.ui.log("Generating the mel spectrogram...")
self.ui.set_loading(1)
# Synthesize the spectrogram
if self.synthesizer is None:
model_dir = self.ui.current_synthesizer_model_dir
checkpoints_dir = model_dir.joinpath("taco_pretrained")
self.synthesizer = Synthesizer(checkpoints_dir, low_mem=self.low_mem)
if not self.synthesizer.is_loaded():
self.ui.log("Loading the synthesizer %s" % self.synthesizer.checkpoint_fpath)
# Update the synthesizer random seed
if self.ui.random_seed_checkbox.isChecked():
seed = self.synthesizer.set_seed(int(self.ui.seed_textbox.text()))
self.ui.populate_gen_options(seed, self.trim_silences)
else:
seed = self.synthesizer.set_seed(None)
texts = self.ui.text_prompt.toPlainText().split("\n")
embed = self.ui.selected_utterance.embed
embeds = np.stack([embed] * len(texts))
specs = self.synthesizer.synthesize_spectrograms(texts, embeds)
breaks = [spec.shape[1] for spec in specs]
spec = np.concatenate(specs, axis=1)
self.ui.draw_spec(spec, "generated")
self.current_generated = (self.ui.selected_utterance.speaker_name, spec, breaks, None)
self.ui.set_loading(0)
def vocode(self):
speaker_name, spec, breaks, _ = self.current_generated
assert spec is not None
# Initialize the vocoder model and make it determinstic, if user provides a seed
if self.ui.random_seed_checkbox.isChecked():
seed = self.synthesizer.set_seed(int(self.ui.seed_textbox.text()))
self.ui.populate_gen_options(seed, self.trim_silences)
else:
seed = None
if seed is not None:
torch.manual_seed(seed)
# Synthesize the waveform
if not vocoder.is_loaded() or seed is not None:
self.init_vocoder()
def vocoder_progress(i, seq_len, b_size, gen_rate):
real_time_factor = (gen_rate / Synthesizer.sample_rate) * 1000
line = "Waveform generation: %d/%d (batch size: %d, rate: %.1fkHz - %.2fx real time)" \
% (i * b_size, seq_len * b_size, b_size, gen_rate, real_time_factor)
self.ui.log(line, "overwrite")
self.ui.set_loading(i, seq_len)
if self.ui.current_vocoder_fpath is not None:
self.ui.log("")
wav = vocoder.infer_waveform(spec, progress_callback=vocoder_progress)
else:
self.ui.log("Waveform generation with Griffin-Lim... ")
wav = Synthesizer.griffin_lim(spec)
self.ui.set_loading(0)
self.ui.log(" Done!", "append")
# Add breaks
b_ends = np.cumsum(np.array(breaks) * Synthesizer.hparams.hop_size)
b_starts = np.concatenate(([0], b_ends[:-1]))
wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)]
breaks = [np.zeros(int(0.15 * Synthesizer.sample_rate))] * len(breaks)
wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])
# Trim excessive silences
if self.ui.trim_silences_checkbox.isChecked():
wav = encoder.preprocess_wav(wav)
# Play it
wav = wav / np.abs(wav).max() * 0.97
self.ui.play(wav, Synthesizer.sample_rate)
# Name it (history displayed in combobox)
# TODO better naming for the combobox items?
wav_name = str(self.waves_count + 1)
#Update waves combobox
self.waves_count += 1
if self.waves_count > MAX_WAVES:
self.waves_list.pop()
self.waves_namelist.pop()
self.waves_list.insert(0, wav)
self.waves_namelist.insert(0, wav_name)
self.ui.waves_cb.disconnect()
self.ui.waves_cb_model.setStringList(self.waves_namelist)
self.ui.waves_cb.setCurrentIndex(0)
self.ui.waves_cb.currentIndexChanged.connect(self.set_current_wav)
# Update current wav
self.set_current_wav(0)
#Enable replay and save buttons:
self.ui.replay_wav_button.setDisabled(False)
self.ui.export_wav_button.setDisabled(False)
# Compute the embedding
# TODO: this is problematic with different sampling rates, gotta fix it
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
# Add the utterance
name = speaker_name + "_gen_%05d" % np.random.randint(100000)
utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, True)
self.utterances.add(utterance)
# Plot it
self.ui.draw_embed(embed, name, "generated")
self.ui.draw_umap_projections(self.utterances)
def init_encoder(self):
model_fpath = self.ui.current_encoder_fpath
self.ui.log("Loading the encoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
encoder.load_model(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
def init_vocoder(self):
model_fpath = self.ui.current_vocoder_fpath
# Case of Griffin-lim
if model_fpath is None:
return
self.ui.log("Loading the vocoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
vocoder.load_model(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
def update_seed_textbox(self):
self.ui.update_seed_textbox()
class Toolbox:
def __init__(self,
datasets_root: Path,
models_dir: Path,
seed: int = None):
sys.excepthook = self.excepthook
self.datasets_root = datasets_root
self.utterances = set()
self.current_generated = (None, None, None, None
) # speaker_name, spec, breaks, wav
self.synthesizer = None # type: Synthesizer
self.current_wav = None
self.waves_list = []
self.waves_count = 0
self.waves_namelist = []
# Check for webrtcvad (enables removal of silences in vocoder output)
try:
import webrtcvad
self.trim_silences = True
except:
self.trim_silences = False
# Initialize the events and the interface
self.ui = UI()
self.reset_ui(models_dir, seed)
self.setup_events()
self.ui.start()
def excepthook(self, exc_type, exc_value, exc_tb):
traceback.print_exception(exc_type, exc_value, exc_tb)
self.ui.log("Exception: %s" % exc_value)
def setup_events(self):
# Dataset, speaker and utterance selection
self.ui.browser_load_button.clicked.connect(
lambda: self.load_from_browser())
random_func = lambda level: lambda: self.ui.populate_browser(
self.datasets_root, recognized_datasets, level)
self.ui.random_dataset_button.clicked.connect(random_func(0))
self.ui.random_speaker_button.clicked.connect(random_func(1))
self.ui.random_utterance_button.clicked.connect(random_func(2))
self.ui.dataset_box.currentIndexChanged.connect(random_func(1))
self.ui.speaker_box.currentIndexChanged.connect(random_func(2))
# Model selection
self.ui.encoder_box.currentIndexChanged.connect(self.init_encoder)
def func():
self.synthesizer = None
self.ui.synthesizer_box.currentIndexChanged.connect(func)
self.ui.vocoder_box.currentIndexChanged.connect(self.init_vocoder)
# Utterance selection
func = lambda: self.load_from_browser(self.ui.browse_file())
self.ui.browser_browse_button.clicked.connect(func)
func = lambda: self.ui.draw_utterance(self.ui.selected_utterance,
"current")
self.ui.utterance_history.currentIndexChanged.connect(func)
func = lambda: self.ui.play(self.ui.selected_utterance.wav, Synthesizer
.sample_rate)
self.ui.play_button.clicked.connect(func)
self.ui.stop_button.clicked.connect(self.ui.stop)
self.ui.record_button.clicked.connect(self.record)
#Audio
self.ui.setup_audio_devices(Synthesizer.sample_rate)
#Wav playback & save
func = lambda: self.replay_last_wav()
self.ui.replay_wav_button.clicked.connect(func)
func = lambda: self.export_current_wave()
self.ui.export_wav_button.clicked.connect(func)
self.ui.waves_cb.currentIndexChanged.connect(self.set_current_wav)
# Generation
func = lambda: self.synthesize() or self.vocode()
self.ui.generate_button.clicked.connect(func)
self.ui.synthesize_button.clicked.connect(self.synthesize)
self.ui.vocode_button.clicked.connect(self.vocode)
self.ui.random_seed_checkbox.clicked.connect(self.update_seed_textbox)
# UMAP legend
self.ui.clear_button.clicked.connect(self.clear_utterances)
def set_current_wav(self, index):
self.current_wav = self.waves_list[index]
def export_current_wave(self):
self.ui.save_audio_file(self.current_wav, Synthesizer.sample_rate)
def replay_last_wav(self):
self.ui.play(self.current_wav, Synthesizer.sample_rate)
def reset_ui(self, models_dir: Path, seed: int = None):
self.ui.populate_browser(self.datasets_root, recognized_datasets, 0,
True)
self.ui.populate_models(models_dir)
self.ui.populate_gen_options(seed, self.trim_silences)
def load_from_browser(self, fpath=None):
if fpath is None:
fpath = Path(self.datasets_root, self.ui.current_dataset_name,
self.ui.current_speaker_name,
self.ui.current_utterance_name)
name = str(fpath.relative_to(self.datasets_root))
speaker_name = self.ui.current_dataset_name + '_' + self.ui.current_speaker_name
# Select the next utterance
if self.ui.auto_next_checkbox.isChecked():
self.ui.browser_select_next()
elif fpath == "":
return
else:
name = fpath.name
speaker_name = fpath.parent.name
# Get the wav from the disk. We take the wav with the vocoder/synthesizer format for
# playback, so as to have a fair comparison with the generated audio
wav = Synthesizer.load_preprocess_wav(fpath)
self.ui.log("Loaded %s" % name)
self.add_real_utterance(wav, name, speaker_name)
def record(self):
wav = self.ui.record_one(encoder.sampling_rate, 5)
if wav is None:
return
self.ui.play(wav, encoder.sampling_rate)
speaker_name = "user01"
name = speaker_name + "_rec_%05d" % np.random.randint(100000)
self.add_real_utterance(wav, name, speaker_name)
def add_real_utterance(self, wav, name, speaker_name):
# Compute the mel spectrogram
spec = Synthesizer.make_spectrogram(wav)
self.ui.draw_spec(spec, "current")
# Compute the embedding
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(
encoder_wav, return_partials=True)
# Add the utterance
utterance = Utterance(name, speaker_name, wav, spec, embed,
partial_embeds, False)
self.utterances.add(utterance)
self.ui.register_utterance(utterance)
# Plot it
self.ui.draw_embed(embed, name, "current")
self.ui.draw_umap_projections(self.utterances)
def clear_utterances(self):
self.utterances.clear()
self.ui.draw_umap_projections(self.utterances)
def synthesize(self):
self.ui.log("Generating the mel spectrogram...")
self.ui.set_loading(1)
# Update the synthesizer random seed
if self.ui.random_seed_checkbox.isChecked():
seed = int(self.ui.seed_textbox.text())
self.ui.populate_gen_options(seed, self.trim_silences)
else:
seed = None
if seed is not None:
torch.manual_seed(seed)
# Synthesize the spectrogram
if self.synthesizer is None or seed is not None:
self.init_synthesizer()
texts = self.ui.text_prompt.toPlainText().split("\n")
embed = self.ui.selected_utterance.embed
embeds = [embed] * len(texts)
specs = self.synthesizer.synthesize_spectrograms(texts, embeds)
breaks = [spec.shape[1] for spec in specs]
spec = np.concatenate(specs, axis=1)
self.ui.draw_spec(spec, "generated")
self.current_generated = (self.ui.selected_utterance.speaker_name,
spec, breaks, None)
self.ui.set_loading(0)
def vocode(self):
speaker_name, spec, breaks, _ = self.current_generated
assert spec is not None
# Initialize the vocoder model and make it determinstic, if user provides a seed
if self.ui.random_seed_checkbox.isChecked():
seed = int(self.ui.seed_textbox.text())
self.ui.populate_gen_options(seed, self.trim_silences)
else:
seed = None
if seed is not None:
torch.manual_seed(seed)
# Synthesize the waveform
if not vocoder.is_loaded() or seed is not None:
self.init_vocoder()
def vocoder_progress(i, seq_len, b_size, gen_rate):
real_time_factor = (gen_rate / Synthesizer.sample_rate) * 1000
line = "Waveform generation: %d/%d (batch size: %d, rate: %.1fkHz - %.2fx real time)" \
% (i * b_size, seq_len * b_size, b_size, gen_rate, real_time_factor)
self.ui.log(line, "overwrite")
self.ui.set_loading(i, seq_len)
if self.ui.current_vocoder_fpath is not None:
self.ui.log("")
wav = vocoder.infer_waveform(spec,
progress_callback=vocoder_progress)
else:
self.ui.log("Waveform generation with Griffin-Lim... ")
wav = Synthesizer.griffin_lim(spec)
self.ui.set_loading(0)
self.ui.log(" Done!", "append")
# Add breaks
b_ends = np.cumsum(np.array(breaks) * Synthesizer.hparams.hop_size)
b_starts = np.concatenate(([0], b_ends[:-1]))
wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)]
breaks = [np.zeros(int(0.15 * Synthesizer.sample_rate))] * len(breaks)
wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])
# Trim excessive silences
if self.ui.trim_silences_checkbox.isChecked():
wav = encoder.preprocess_wav(wav)
# Play it
wav = wav / np.abs(wav).max() * 0.97
self.ui.play(wav, Synthesizer.sample_rate)
# Name it (history displayed in combobox)
# TODO better naming for the combobox items?
wav_name = str(self.waves_count + 1)
#Update waves combobox
self.waves_count += 1
if self.waves_count > MAX_WAVS:
self.waves_list.pop()
self.waves_namelist.pop()
self.waves_list.insert(0, wav)
self.waves_namelist.insert(0, wav_name)
self.ui.waves_cb.disconnect()
self.ui.waves_cb_model.setStringList(self.waves_namelist)
self.ui.waves_cb.setCurrentIndex(0)
self.ui.waves_cb.currentIndexChanged.connect(self.set_current_wav)
# Update current wav
self.set_current_wav(0)
#Enable replay and save buttons:
self.ui.replay_wav_button.setDisabled(False)
self.ui.export_wav_button.setDisabled(False)
# Compute the embedding
# TODO: this is problematic with different sampling rates, gotta fix it
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(
encoder_wav, return_partials=True)
# Add the utterance
name = speaker_name + "_gen_%05d" % np.random.randint(100000)
utterance = Utterance(name, speaker_name, wav, spec, embed,
partial_embeds, True)
self.utterances.add(utterance)
# Plot it
self.ui.draw_embed(embed, name, "generated")
self.ui.draw_umap_projections(self.utterances)
def init_encoder(self):
model_fpath = self.ui.current_encoder_fpath
self.ui.log("Loading the encoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
encoder.load_model(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
def init_synthesizer(self):
model_fpath = self.ui.current_synthesizer_fpath
self.ui.log("Loading the synthesizer %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
self.synthesizer = Synthesizer(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
def init_vocoder(self):
model_fpath = self.ui.current_vocoder_fpath
# Case of Griffin-lim
if model_fpath is None:
return
self.ui.log("Loading the vocoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
vocoder.load_model(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
def update_seed_textbox(self):
self.ui.update_seed_textbox()
class Toolbox:
def __init__(self, datasets_root, enc_models_dir, syn_models_dir, voc_models_dir, toolbox_files_dir, low_mem):
sys.excepthook = self.excepthook
self._out_dir = Path(toolbox_files_dir)
self.make_out_dirs()
self.datasets_root = datasets_root
self.datasets = [p.name for p in Path(datasets_root).glob("*") if p.is_dir()]
metapath = Path(self.datasets_root).joinpath("metadata.csv")
if metapath.is_file():
itdt = {}
for line in open(metapath, encoding="utf8"):
idx, text = line.strip().split("\t")
itdt[idx] = text
self.itdt = itdt
else:
self.itdt = {}
self.low_mem = low_mem
self.utterances = set()
self.current_generated = (None, None, None, None) # speaker_name, spec, breaks, wav
self.synthesizer = None # type: Synthesizer
# Initialize the events and the interface
self.ui = UI()
self.reset_ui(enc_models_dir, syn_models_dir, voc_models_dir)
self.setup_events()
self.ui.start()
def make_out_dirs(self):
self._out_dir.mkdir(exist_ok=True)
self._out_mel_dir = self._out_dir.joinpath('mels')
self._out_mel_dir.mkdir(exist_ok=True)
self._out_wav_dir = self._out_dir.joinpath('wavs')
self._out_wav_dir.mkdir(exist_ok=True)
self._out_embed_dir = self._out_dir.joinpath('embeds')
self._out_embed_dir.mkdir(exist_ok=True)
self._out_record_dir = self._out_dir.joinpath('records')
self._out_record_dir.mkdir(exist_ok=True)
def excepthook(self, exc_type, exc_value, exc_tb):
traceback.print_exception(exc_type, exc_value, exc_tb)
self.ui.log("Exception: %s" % exc_value)
def setup_events(self):
# Dataset, speaker and utterance selection
self.ui.browser_load_button.clicked.connect(lambda: self.load_from_browser())
random_func = lambda level: lambda: self.ui.populate_browser(self.datasets_root,
self.datasets,
level)
text_func = lambda: self.ui.text_prompt.setPlainText(np.random.choice(total_texts))
self.ui.random_dataset_button.clicked.connect(text_func)
self.ui.random_speaker_button.clicked.connect(random_func(1))
self.ui.random_utterance_button.clicked.connect(random_func(2))
self.ui.dataset_box.currentIndexChanged.connect(random_func(1))
self.ui.speaker_box.currentIndexChanged.connect(random_func(2))
# Model selection
self.ui.encoder_box.currentIndexChanged.connect(self.init_encoder)
def func():
self.synthesizer = None
self.ui.synthesizer_box.currentIndexChanged.connect(func)
self.ui.vocoder_box.currentIndexChanged.connect(self.init_vocoder)
# Utterance selection
func = lambda: self.load_from_browser(self.ui.browse_file())
self.ui.browser_browse_button.clicked.connect(func)
func = lambda: self.ui.draw_utterance(self.ui.selected_utterance, "current")
self.ui.utterance_history.currentIndexChanged.connect(func)
func = lambda: self.ui.play(self.ui.selected_utterance.wav, Synthesizer.sample_rate)
self.ui.play_button.clicked.connect(func)
self.ui.stop_button.clicked.connect(self.ui.stop)
self.ui.record_button.clicked.connect(self.record)
self.ui.take_generated_button.clicked.connect(self.preprocess)
# Generation
func = lambda: self.synthesize() or self.vocode()
self.ui.generate_button.clicked.connect(func)
self.ui.compare_button.clicked.connect(self.compare)
self.ui.synthesize_button.clicked.connect(self.synthesize)
self.ui.vocode_button.clicked.connect(self.vocode)
# UMAP legend
self.ui.clear_button.clicked.connect(self.clear_utterances)
def reset_ui(self, encoder_models_dir, synthesizer_models_dir, vocoder_models_dir):
self.ui.populate_browser(self.datasets_root, self.datasets, 0, True)
self.ui.populate_models(encoder_models_dir, synthesizer_models_dir, vocoder_models_dir)
def load_from_browser(self, fpath=None):
if fpath is None:
fpath = Path(self.datasets_root,
self.ui.current_dataset_name,
self.ui.current_speaker_name,
self.ui.current_utterance_name)
# name = '/'.join(fpath.relative_to(self.datasets_root).parts)
dat = self.ui.current_dataset_name.replace("\\", "#").replace("/", "#")
spk = self.ui.current_speaker_name.replace("\\", "#").replace("/", "#")
aud = self.ui.current_utterance_name.replace("\\", "#").replace("/", "#")
speaker_name = "#".join((dat, spk))
name = "#".join((speaker_name, aud))
# name = '-'.join(fpath.relative_to(self.datasets_root.joinpath(self.ui.current_dataset_name)).parts)
# speaker_name = self.ui.current_speaker_name.replace("\\", "-").replace("/", "-")
# Select the next utterance
if self.ui.auto_next_checkbox.isChecked():
self.ui.browser_select_next()
elif fpath == "":
return
else:
name = fpath.name
speaker_name = fpath.parent.name
# Get the wav from the disk. We take the wav with the vocoder/synthesizer format for
# playback, so as to have a fair comparison with the generated audio
wav = Synthesizer.load_preprocess_wav(fpath)
self.ui.log("Loaded %s" % name)
self.add_real_utterance(wav, name, speaker_name)
def compare(self):
"""
1.判断参考音频是否有对应文本。
2.输入框更新为参考文本。
3.合成参考音频对应文本的语音。
4.展示embed,spectrogram,alignment。
:return:
"""
idx = self.ui.selected_utterance.name.replace("#", "/")
idx = re.sub(r"(_preprocessed)(\..*?)$", r"\2", idx)
if idx not in self.itdt:
print("Compare Failed! index: {}".format(idx))
return
self.ui.text_prompt.setPlainText(self.itdt[idx])
self.synthesize()
self.vocode()
def preprocess(self):
wav = self.ui.selected_utterance.wav
out = aukit.remove_noise(wav, sr=Synthesizer.sample_rate)
hp = aukit.Dict2Obj({})
hp["vad_window_length"] = 10 # milliseconds
hp["vad_moving_average_width"] = 2
hp["vad_max_silence_length"] = 2
hp["audio_norm_target_dBFS"] = -32
hp["sample_rate"] = 16000
hp["int16_max"] = (2 ** 15) - 1
out = trim_long_silences(out, hparams=hp)
spec = Synthesizer.make_spectrogram(out)
self.ui.draw_align(spec[::-1], "current")
name = filename_add_suffix(self.ui.selected_utterance.name, "_preprocessed")
speaker_name = self.ui.selected_utterance.speaker_name
self.add_real_utterance(out, name, speaker_name)
def record(self):
wav = self.ui.record_one(encoder.sampling_rate, 5)
if wav is None:
return
self.ui.play(wav, encoder.sampling_rate)
speaker_name = "user01"
name = speaker_name + "_rec_{}".format(time_formatter())
fpath = self._out_record_dir.joinpath(name + '.wav')
audio.save_wav(wav, fpath, encoder.sampling_rate) # save
wav = Synthesizer.load_preprocess_wav(fpath) # 保持一致的数据格式
self.add_real_utterance(wav, name, speaker_name)
def add_real_utterance(self, wav, name, speaker_name):
# Compute the mel spectrogram
spec = Synthesizer.make_spectrogram(wav)
self.ui.draw_spec(spec, "current")
# Compute the embedding
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
np.save(self._out_embed_dir.joinpath(name + '.npy'), embed, allow_pickle=False) # save
# Add the utterance
utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, False)
self.utterances.add(utterance)
self.ui.register_utterance(utterance)
# Plot it
self.ui.draw_embed(embed, name, "current")
self.ui.draw_umap_projections(self.utterances)
def clear_utterances(self):
self.utterances.clear()
self.ui.draw_umap_projections(self.utterances)
def synthesize(self):
self.ui.log("Generating the mel spectrogram...")
self.ui.set_loading(1)
# Synthesize the spectrogram
if self.synthesizer is None:
model_dir = Path(self.ui.current_synthesizer_model_dir)
checkpoints_dir = model_dir.joinpath("checkpoints")
hp_path = model_dir.joinpath("metas", "hparams.json") # load from trained models
if hp_path.exists():
hparams = aukit.Dict2Obj(json.load(open(hp_path, encoding="utf8")))
else:
hparams = None
self.synthesizer = Synthesizer(checkpoints_dir, low_mem=self.low_mem, hparams=hparams)
if not self.synthesizer.is_loaded():
self.ui.log("Loading the synthesizer %s" % self.synthesizer.checkpoint_fpath)
ptext = self.ui.text_prompt.toPlainText()
texts = ptext.split("\n")
embed = self.ui.selected_utterance.embed
embeds = np.stack([embed] * len(texts))
specs, aligns = self.synthesizer.synthesize_spectrograms(texts, embeds, return_alignments=True)
breaks = [spec.shape[1] for spec in specs]
spec = np.concatenate(specs, axis=1)
align = np.concatenate(aligns, axis=1)
fref = self.ui.selected_utterance.name
ftext = '。'.join(texts)
ftime = '{}'.format(time_formatter())
fname = filename_formatter('{}_{}_{}zi_{}.npy'.format(fref, ftime, len(ftext), ftext))
np.save(self._out_mel_dir.joinpath(fname), spec, allow_pickle=False) # save
self.ui.draw_spec(spec, "generated")
self.ui.draw_align(align, "generated")
self.current_generated = (self.ui.selected_utterance.speaker_name, spec, breaks, None)
self.ui.set_loading(0)
def vocode(self):
speaker_name, spec, breaks, _ = self.current_generated
assert spec is not None
# Synthesize the waveform
if not vocoder.is_loaded():
self.init_vocoder()
def vocoder_progress(i, seq_len, b_size, gen_rate):
real_time_factor = (gen_rate / Synthesizer.sample_rate) * 1000
line = "Waveform generation: %d/%d (batch size: %d, rate: %.1fkHz - %.2fx real time)" \
% (i * b_size, seq_len * b_size, b_size, gen_rate, real_time_factor)
self.ui.log(line, "overwrite")
self.ui.set_loading(i, seq_len)
wav = None
vocname = ""
if self.ui.current_vocoder_fpath is not None:
model_fpath = self.ui.current_vocoder_fpath
vocname = Path(model_fpath).parent.stem
if Path(model_fpath).parent.stem == "melgan":
self.ui.log("Waveform generation with MelGAN... ")
wav = vocoder_melgan.infer_waveform_melgan(spec, model_fpath)
elif Path(model_fpath).parent.stem == "wavernn":
self.ui.log("Waveform generation with WaveRNN... ")
wav = vocoder.infer_waveform(spec, progress_callback=vocoder_progress)
if wav is None:
vocname = "griffinlim"
self.ui.log("Waveform generation with Griffin-Lim... ")
wav = Synthesizer.griffin_lim(spec)
self.ui.set_loading(0)
self.ui.log(" Done!", "append")
# Play it
wav = wav / np.abs(wav).max() * 0.97
self.ui.play(wav, Synthesizer.sample_rate)
fref = self.ui.selected_utterance.name
ftime = '{}'.format(time_formatter())
ftext = self.ui.text_prompt.toPlainText()
fms = int(len(wav) * 1000 / Synthesizer.sample_rate)
fvoc = vocname
fname = filename_formatter('{}_{}_{}_{}ms_{}.wav'.format(fref, ftime, fvoc, fms, ftext))
audio.save_wav(wav, self._out_wav_dir.joinpath(fname), Synthesizer.sample_rate) # save
# Compute the embedding
# TODO: this is problematic with different sampling rates, gotta fix it
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
# Add the utterance
name = speaker_name + "_gen_{}".format(time_formatter())
utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, True)
np.save(self._out_embed_dir.joinpath(name + '.npy'), embed, allow_pickle=False) # save
self.utterances.add(utterance)
# Plot it
self.ui.draw_embed(embed, name, "generated")
self.ui.draw_umap_projections(self.utterances)
def init_encoder(self):
model_fpath = self.ui.current_encoder_fpath
self.ui.log("Loading the encoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
encoder.load_model(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
def init_vocoder(self):
model_fpath = self.ui.current_vocoder_fpath
# Case of Griffin-lim
if model_fpath is None:
return
else:
self.ui.log("Loading the vocoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
if Path(model_fpath).parent.stem == "melgan":
vocoder_melgan.load_vocoder_melgan(model_fpath)
elif Path(model_fpath).parent.stem == "wavernn":
vocoder.load_model(model_fpath)
else:
return
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
class Toolbox:
def __init__(self, datasets_root, enc_models_dir, syn_models_dir, voc_models_dir, toolbox_files_dir, low_mem):
sys.excepthook = self.excepthook
self._out_dir = Path(toolbox_files_dir)
self.make_out_dirs()
self.datasets_root = datasets_root
self.low_mem = low_mem
self.utterances = set()
self.current_generated = (None, None, None, None) # speaker_name, spec, breaks, wav
self.synthesizer = None # type: Synthesizer
# Initialize the events and the interface
self.ui = UI()
self.reset_ui(enc_models_dir, syn_models_dir, voc_models_dir)
self.setup_events()
self.ui.start()
def make_out_dirs(self):
self._out_dir.mkdir(exist_ok=True)
self._out_mel_dir = self._out_dir.joinpath('mels')
self._out_mel_dir.mkdir(exist_ok=True)
self._out_wav_dir = self._out_dir.joinpath('wavs')
self._out_wav_dir.mkdir(exist_ok=True)
self._out_embed_dir = self._out_dir.joinpath('embeds')
self._out_embed_dir.mkdir(exist_ok=True)
self._out_record_dir = self._out_dir.joinpath('records')
self._out_record_dir.mkdir(exist_ok=True)
def excepthook(self, exc_type, exc_value, exc_tb):
traceback.print_exception(exc_type, exc_value, exc_tb)
self.ui.log("Exception: %s" % exc_value)
def setup_events(self):
# Dataset, speaker and utterance selection
self.ui.browser_load_button.clicked.connect(lambda: self.load_from_browser())
random_func = lambda level: lambda: self.ui.populate_browser(self.datasets_root,
recognized_datasets,
level)
text_func = lambda: self.ui.text_prompt.setPlainText(np.random.choice(total_texts, 1)[0])
self.ui.random_dataset_button.clicked.connect(text_func)
self.ui.random_speaker_button.clicked.connect(random_func(1))
self.ui.random_utterance_button.clicked.connect(random_func(2))
self.ui.dataset_box.currentIndexChanged.connect(random_func(1))
self.ui.speaker_box.currentIndexChanged.connect(random_func(2))
# Model selection
self.ui.encoder_box.currentIndexChanged.connect(self.init_encoder)
def func():
self.synthesizer = None
self.ui.synthesizer_box.currentIndexChanged.connect(func)
self.ui.vocoder_box.currentIndexChanged.connect(self.init_vocoder)
# Utterance selection
func = lambda: self.load_from_browser(self.ui.browse_file())
self.ui.browser_browse_button.clicked.connect(func)
func = lambda: self.ui.draw_utterance(self.ui.selected_utterance, "current")
self.ui.utterance_history.currentIndexChanged.connect(func)
func = lambda: self.ui.play(self.ui.selected_utterance.wav, Synthesizer.sample_rate)
self.ui.play_button.clicked.connect(func)
self.ui.stop_button.clicked.connect(self.ui.stop)
self.ui.record_button.clicked.connect(self.record)
# Generation
func = lambda: self.synthesize() or self.vocode()
self.ui.generate_button.clicked.connect(func)
self.ui.synthesize_button.clicked.connect(self.synthesize)
self.ui.vocode_button.clicked.connect(self.vocode)
# UMAP legend
self.ui.clear_button.clicked.connect(self.clear_utterances)
def reset_ui(self, encoder_models_dir, synthesizer_models_dir, vocoder_models_dir):
self.ui.populate_browser(self.datasets_root, recognized_datasets, 0, True)
self.ui.populate_models(encoder_models_dir, synthesizer_models_dir, vocoder_models_dir)
def load_from_browser(self, fpath=None):
if fpath is None:
fpath = Path(self.datasets_root,
self.ui.current_dataset_name,
self.ui.current_speaker_name,
self.ui.current_utterance_name)
# name = '-'.join(fpath.relative_to(self.datasets_root).parts)
speaker_name = "-".join((self.ui.current_dataset_name.replace("\\", "_").replace("/", "_"),
self.ui.current_speaker_name.replace("\\", "_").replace("/", "_")))
name = "-".join((speaker_name, self.ui.current_utterance_name.replace("\\", "_").replace("/", "_")))
# name = '-'.join(fpath.relative_to(self.datasets_root.joinpath(self.ui.current_dataset_name)).parts)
# speaker_name = self.ui.current_speaker_name.replace("\\", "-").replace("/", "-")
# Select the next utterance
if self.ui.auto_next_checkbox.isChecked():
self.ui.browser_select_next()
elif fpath == "":
return
else:
name = fpath.name
speaker_name = fpath.parent.name
# Get the wav from the disk. We take the wav with the vocoder/synthesizer format for
# playback, so as to have a fair comparison with the generated audio
wav = Synthesizer.load_preprocess_wav(fpath)
self.ui.log("Loaded %s" % name)
self.add_real_utterance(wav, name, speaker_name)
def record(self):
wav = self.ui.record_one(encoder.sampling_rate, 5)
if wav is None:
return
self.ui.play(wav, encoder.sampling_rate)
speaker_name = "user01"
name = speaker_name + "_rec_%d" % int(time.time())
audio.save_wav(wav, self._out_record_dir.joinpath(name + '.wav'), encoder.sampling_rate) # save
self.add_real_utterance(wav, name, speaker_name)
def add_real_utterance(self, wav, name, speaker_name):
# Compute the mel spectrogram
spec = Synthesizer.make_spectrogram(wav)
self.ui.draw_spec(spec, "current")
# Compute the embedding
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
np.save(self._out_embed_dir.joinpath(name + '.npy'), embed, allow_pickle=False) # save
# Add the utterance
utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, False)
self.utterances.add(utterance)
self.ui.register_utterance(utterance)
# Plot it
self.ui.draw_embed(embed, name, "current")
self.ui.draw_umap_projections(self.utterances)
def clear_utterances(self):
self.utterances.clear()
self.ui.draw_umap_projections(self.utterances)
def synthesize(self):
self.ui.log("Generating the mel spectrogram...")
self.ui.set_loading(1)
# Synthesize the spectrogram
if self.synthesizer is None:
model_dir = self.ui.current_synthesizer_model_dir
checkpoints_dir = model_dir.joinpath("taco_pretrained")
self.synthesizer = Synthesizer(checkpoints_dir, low_mem=self.low_mem)
if not self.synthesizer.is_loaded():
self.ui.log("Loading the synthesizer %s" % self.synthesizer.checkpoint_fpath)
ptext = self.ui.text_prompt.toPlainText()
# if ptext.startswith("py"): # 适用于sync2，适应训练时候用pinyin+chinese_cleaners的bug
# ptext = get_pinyin(ptext[2:]) # 把chinese_cleaners的lowercase用起来，否则不能合成。
ptext = " ".join(text2pinyin(ptext))
texts = ptext.split("\n")
print(dict(texts=texts))
embed = self.ui.selected_utterance.embed
embeds = np.stack([embed] * len(texts))
specs = self.synthesizer.synthesize_spectrograms(texts, embeds)
# 去除前后安静或噪声部分
for num, spec in enumerate(specs):
tmp = spec.T
sidx, eidx = find_start_end_points(tmp)
specs[num] = tmp[sidx:eidx].T
# specs = [spec.T[:find_endpoint(spec.T)].T for spec in specs] # find endpoint
breaks = [spec.shape[1] for spec in specs]
spec = np.concatenate(specs, axis=1)
fref = '-'.join([self.ui.current_dataset_name, self.ui.current_speaker_name, self.ui.current_utterance_name])
ftext = '。'.join(texts)
ftime = '{}'.format(int(time.time()))
fname = filename_formatter('{}_{}_{}zi_{}.npy'.format(fref, ftime, len(ftext), ftext))
np.save(self._out_mel_dir.joinpath(fname), spec, allow_pickle=False) # save
self.ui.draw_spec(spec, "generated")
self.current_generated = (self.ui.selected_utterance.speaker_name, spec, breaks, None)
self.ui.set_loading(0)
def vocode(self):
speaker_name, spec, breaks, _ = self.current_generated
assert spec is not None
# Synthesize the waveform
if not vocoder.is_loaded():
self.init_vocoder()
def vocoder_progress(i, seq_len, b_size, gen_rate):
real_time_factor = (gen_rate / Synthesizer.sample_rate) * 1000
line = "Waveform generation: %d/%d (batch size: %d, rate: %.1fkHz - %.2fx real time)" \
% (i * b_size, seq_len * b_size, b_size, gen_rate, real_time_factor)
self.ui.log(line, "overwrite")
self.ui.set_loading(i, seq_len)
if self.ui.current_vocoder_fpath is not None:
self.ui.log("")
wav = vocoder.infer_waveform(spec, progress_callback=vocoder_progress)
else:
self.ui.log("Waveform generation with Griffin-Lim... ")
wav = Synthesizer.griffin_lim(spec)
self.ui.set_loading(0)
self.ui.log(" Done!", "append")
# Add breaks
b_ends = np.cumsum(np.array(breaks) * Synthesizer.hparams.hop_size)
b_starts = np.concatenate(([0], b_ends[:-1]))
wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)]
breaks = [np.zeros(int(0.15 * Synthesizer.sample_rate))] * len(breaks)
wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])
# Play it
wav = wav / np.abs(wav).max() * 0.97
self.ui.play(wav, Synthesizer.sample_rate)
fref = '-'.join([self.ui.current_dataset_name, self.ui.current_speaker_name, self.ui.current_utterance_name])
ftime = '{}'.format(int(time.time()))
ftext = self.ui.text_prompt.toPlainText()
fms = int(len(wav) * 1000 / Synthesizer.sample_rate)
fname = filename_formatter('{}_{}_{}ms_{}.wav'.format(fref, ftime, fms, ftext))
audio.save_wav(wav, self._out_wav_dir.joinpath(fname), Synthesizer.sample_rate) # save
# Compute the embedding
# TODO: this is problematic with different sampling rates, gotta fix it
if not encoder.is_loaded():
self.init_encoder()
encoder_wav = encoder.preprocess_wav(wav)
embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
# Add the utterance
name = speaker_name + "_gen_%05d" % int(time.time())
utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, True)
np.save(self._out_embed_dir.joinpath(name + '.npy'), embed, allow_pickle=False) # save
self.utterances.add(utterance)
# Plot it
self.ui.draw_embed(embed, name, "generated")
self.ui.draw_umap_projections(self.utterances)
def init_encoder(self):
model_fpath = self.ui.current_encoder_fpath
self.ui.log("Loading the encoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
encoder.load_model(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)
def init_vocoder(self):
model_fpath = self.ui.current_vocoder_fpath
# Case of Griffin-lim
if model_fpath is None:
return
self.ui.log("Loading the vocoder %s... " % model_fpath)
self.ui.set_loading(1)
start = timer()
vocoder.load_model(model_fpath)
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
self.ui.set_loading(0)