mirror of
https://github.com/babysor/MockingBird.git
synced 2024-03-22 13:11:31 +08:00
470 lines
18 KiB
Python
470 lines
18 KiB
Python
from toolbox.ui import UI
|
|
from encoder import inference as encoder
|
|
from synthesizer.inference import Synthesizer
|
|
from vocoder.wavernn import inference as rnn_vocoder
|
|
from vocoder.hifigan import inference as gan_vocoder
|
|
import ppg_extractor as extractor
|
|
import ppg2mel as convertor
|
|
from pathlib import Path
|
|
from time import perf_counter as timer
|
|
from toolbox.utterance import Utterance
|
|
from utils.f0_utils import compute_f0, f02lf0, compute_mean_std, get_converted_lf0uv
|
|
import numpy as np
|
|
import traceback
|
|
import sys
|
|
import torch
|
|
import re
|
|
|
|
# 默认使用wavernn
|
|
vocoder = rnn_vocoder
|
|
|
|
# Use this directory structure for your datasets, or modify it to fit your needs
|
|
recognized_datasets = [
|
|
"LibriSpeech/dev-clean",
|
|
"LibriSpeech/dev-other",
|
|
"LibriSpeech/test-clean",
|
|
"LibriSpeech/test-other",
|
|
"LibriSpeech/train-clean-100",
|
|
"LibriSpeech/train-clean-360",
|
|
"LibriSpeech/train-other-500",
|
|
"LibriTTS/dev-clean",
|
|
"LibriTTS/dev-other",
|
|
"LibriTTS/test-clean",
|
|
"LibriTTS/test-other",
|
|
"LibriTTS/train-clean-100",
|
|
"LibriTTS/train-clean-360",
|
|
"LibriTTS/train-other-500",
|
|
"LJSpeech-1.1",
|
|
"VoxCeleb1/wav",
|
|
"VoxCeleb1/test_wav",
|
|
"VoxCeleb2/dev/aac",
|
|
"VoxCeleb2/test/aac",
|
|
"VCTK-Corpus/wav48",
|
|
"aidatatang_200zh/corpus/dev",
|
|
"aidatatang_200zh/corpus/test",
|
|
"aishell3/test/wav",
|
|
"magicdata/train",
|
|
]
|
|
|
|
#Maximum of generated wavs to keep on memory
|
|
MAX_WAVES = 15
|
|
|
|
class Toolbox:
|
|
def __init__(self, datasets_root, enc_models_dir, syn_models_dir, voc_models_dir, extractor_models_dir, convertor_models_dir, seed, no_mp3_support, vc_mode):
|
|
self.no_mp3_support = no_mp3_support
|
|
self.vc_mode = vc_mode
|
|
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
|
|
|
|
# for ppg-based voice conversion
|
|
self.extractor = None
|
|
self.convertor = None # ppg2mel
|
|
|
|
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(vc_mode)
|
|
self.style_idx = 0
|
|
self.reset_ui(enc_models_dir, syn_models_dir, voc_models_dir, extractor_models_dir, convertor_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
|
|
if self.vc_mode:
|
|
self.ui.extractor_box.currentIndexChanged.connect(self.init_extractor)
|
|
else:
|
|
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)
|
|
|
|
# Source Utterance selection
|
|
if self.vc_mode:
|
|
func = lambda: self.load_soruce_button(self.ui.selected_utterance)
|
|
self.ui.load_soruce_button.clicked.connect(func)
|
|
|
|
#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
|
|
self.ui.vocode_button.clicked.connect(self.vocode)
|
|
self.ui.random_seed_checkbox.clicked.connect(self.update_seed_textbox)
|
|
|
|
if self.vc_mode:
|
|
func = lambda: self.convert() or self.vocode()
|
|
self.ui.convert_button.clicked.connect(func)
|
|
else:
|
|
func = lambda: self.synthesize() or self.vocode()
|
|
self.ui.generate_button.clicked.connect(func)
|
|
self.ui.synthesize_button.clicked.connect(self.synthesize)
|
|
|
|
# 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, extractor_models_dir, convertor_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, extractor_models_dir, convertor_models_dir, self.vc_mode)
|
|
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_soruce_button(self, utterance: Utterance):
|
|
self.selected_source_utterance = utterance
|
|
|
|
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, self.vc_mode)
|
|
|
|
# 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")
|
|
punctuation = '！，。、,' # punctuate and split/clean text
|
|
processed_texts = []
|
|
for text in texts:
|
|
for processed_text in re.sub(r'[{}]+'.format(punctuation), '\n', text).split('\n'):
|
|
if processed_text:
|
|
processed_texts.append(processed_text.strip())
|
|
texts = processed_texts
|
|
embed = self.ui.selected_utterance.embed
|
|
embeds = [embed] * len(texts)
|
|
min_token = int(self.ui.token_slider.value())
|
|
specs = self.synthesizer.synthesize_spectrograms(texts, embeds, style_idx=int(self.ui.style_slider.value()), min_stop_token=min_token, steps=int(self.ui.length_slider.value())*200)
|
|
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, sample_rate = 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 * 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, 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 convert(self):
|
|
self.ui.log("Extract PPG and Converting...")
|
|
self.ui.set_loading(1)
|
|
|
|
# Init
|
|
if self.convertor is None:
|
|
self.init_convertor()
|
|
if self.extractor is None:
|
|
self.init_extractor()
|
|
|
|
src_wav = self.selected_source_utterance.wav
|
|
|
|
# Compute the ppg
|
|
if not self.extractor is None:
|
|
ppg = self.extractor.extract_from_wav(src_wav)
|
|
|
|
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
|
ref_wav = self.ui.selected_utterance.wav
|
|
ref_lf0_mean, ref_lf0_std = compute_mean_std(f02lf0(compute_f0(ref_wav)))
|
|
lf0_uv = get_converted_lf0uv(src_wav, ref_lf0_mean, ref_lf0_std, convert=True)
|
|
min_len = min(ppg.shape[1], len(lf0_uv))
|
|
ppg = ppg[:, :min_len]
|
|
lf0_uv = lf0_uv[:min_len]
|
|
_, mel_pred, att_ws = self.convertor.inference(
|
|
ppg,
|
|
logf0_uv=torch.from_numpy(lf0_uv).unsqueeze(0).float().to(device),
|
|
spembs=torch.from_numpy(self.ui.selected_utterance.embed).unsqueeze(0).to(device),
|
|
)
|
|
mel_pred= mel_pred.transpose(0, 1)
|
|
breaks = [mel_pred.shape[1]]
|
|
mel_pred= mel_pred.detach().cpu().numpy()
|
|
self.ui.draw_spec(mel_pred, "generated")
|
|
self.current_generated = (self.ui.selected_utterance.speaker_name, mel_pred, breaks, None)
|
|
self.ui.set_loading(0)
|
|
|
|
def init_extractor(self):
|
|
if self.ui.current_extractor_fpath is None:
|
|
return
|
|
model_fpath = self.ui.current_extractor_fpath
|
|
self.ui.log("Loading the extractor %s... " % model_fpath)
|
|
self.ui.set_loading(1)
|
|
start = timer()
|
|
self.extractor = extractor.load_model(model_fpath)
|
|
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
|
|
self.ui.set_loading(0)
|
|
|
|
def init_convertor(self):
|
|
if self.ui.current_convertor_fpath is None:
|
|
return
|
|
model_fpath = self.ui.current_convertor_fpath
|
|
# search a config file
|
|
model_config_fpaths = list(model_fpath.parent.rglob("*.yaml"))
|
|
if self.ui.current_convertor_fpath is None:
|
|
return
|
|
model_config_fpath = model_config_fpaths[0]
|
|
self.ui.log("Loading the convertor %s... " % model_fpath)
|
|
self.ui.set_loading(1)
|
|
start = timer()
|
|
self.convertor = convertor.load_model(model_config_fpath, model_fpath)
|
|
self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
|
|
self.ui.set_loading(0)
|
|
|
|
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):
|
|
|
|
global vocoder
|
|
model_fpath = self.ui.current_vocoder_fpath
|
|
# Case of Griffin-lim
|
|
if model_fpath is None:
|
|
return
|
|
# Sekect vocoder based on model name
|
|
model_config_fpath = None
|
|
if model_fpath.name[0] == "g":
|
|
vocoder = gan_vocoder
|
|
self.ui.log("set hifigan as vocoder")
|
|
# search a config file
|
|
model_config_fpaths = list(model_fpath.parent.rglob("*.json"))
|
|
if self.vc_mode and self.ui.current_extractor_fpath is None:
|
|
return
|
|
model_config_fpath = model_config_fpaths[0]
|
|
else:
|
|
vocoder = rnn_vocoder
|
|
self.ui.log("set wavernn as vocoder")
|
|
|
|
self.ui.log("Loading the vocoder %s... " % model_fpath)
|
|
self.ui.set_loading(1)
|
|
start = timer()
|
|
vocoder.load_model(model_fpath, model_config_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()
|