from toolbox.ui import UI from encoder import inference as encoder from synthesizer.inference import Synthesizer from vocoder import inference as vocoder from pathlib import Path from time import perf_counter as timer from toolbox.utterance import Utterance import numpy as np import traceback import sys import torch import librosa import re from audioread.exceptions import NoBackendError # 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", ] #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, 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.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) 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 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") 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) 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_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_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()