コード例 #1
0
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)
コード例 #2
0
ファイル: __init__.py プロジェクト: Cerins/AIAudiobook
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() 
コード例 #3
0
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()
コード例 #4
0
ファイル: core.py プロジェクト: zxy2020/zhrtvc
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)
コード例 #5
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)