from control.toolbox.ui import UI from models.encoder import inference as encoder from models.synthesizer.inference import Synthesizer from models.vocoder.wavernn import inference as rnn_vocoder from models.vocoder.hifigan import inference as gan_vocoder from models.vocoder.fregan import inference as fgan_vocoder from pathlib import Path from time import perf_counter as timer from control.toolbox.utterance import Utterance 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/test", "aidatatang_200zh/corpus/train", "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 # Import necessary dependency of Voice Conversion from utils.f0_utils import compute_f0, f02lf0, compute_mean_std, get_converted_lf0uv 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() import models.ppg_extractor as extractor 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 self.ui.log("Loading the convertor %s... " % model_fpath) self.ui.set_loading(1) start = timer() import models.ppg2mel as convertor self.convertor = convertor.load_model( 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 is not None and model_fpath.name.find("hifigan") > -1: 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 if len(model_config_fpaths) > 0: model_config_fpath = model_config_fpaths[0] elif model_fpath.name is not None and model_fpath.name.find("fregan") > -1: vocoder = fgan_vocoder self.ui.log("set fregan 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 if len(model_config_fpaths) > 0: 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()