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477 lines
19 KiB
Python
477 lines
19 KiB
Python
from toolbox.ui import UI
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from encoder import inference as encoder
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from synthesizer.inference import Synthesizer
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from vocoder.wavernn import inference as rnn_vocoder
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from vocoder.hifigan import inference as gan_vocoder
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from vocoder.fregan import inference as fgan_vocoder
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from pathlib import Path
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from time import perf_counter as timer
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from toolbox.utterance import Utterance
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import numpy as np
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import traceback
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import sys
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import torch
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import re
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# 默认使用wavernn
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vocoder = rnn_vocoder
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# Use this directory structure for your datasets, or modify it to fit your needs
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recognized_datasets = [
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"LibriSpeech/dev-clean",
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"LibriSpeech/dev-other",
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"LibriSpeech/test-clean",
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"LibriSpeech/test-other",
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"LibriSpeech/train-clean-100",
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"LibriSpeech/train-clean-360",
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"LibriSpeech/train-other-500",
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"LibriTTS/dev-clean",
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"LibriTTS/dev-other",
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"LibriTTS/test-clean",
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"LibriTTS/test-other",
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"LibriTTS/train-clean-100",
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"LibriTTS/train-clean-360",
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"LibriTTS/train-other-500",
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"LJSpeech-1.1",
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"VoxCeleb1/wav",
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"VoxCeleb1/test_wav",
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"VoxCeleb2/dev/aac",
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"VoxCeleb2/test/aac",
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"VCTK-Corpus/wav48",
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"aidatatang_200zh/corpus/dev",
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"aidatatang_200zh/corpus/test",
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"aishell3/test/wav",
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"magicdata/train",
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]
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#Maximum of generated wavs to keep on memory
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MAX_WAVES = 15
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class Toolbox:
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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):
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self.no_mp3_support = no_mp3_support
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self.vc_mode = vc_mode
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sys.excepthook = self.excepthook
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self.datasets_root = datasets_root
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self.utterances = set()
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self.current_generated = (None, None, None, None) # speaker_name, spec, breaks, wav
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self.synthesizer = None # type: Synthesizer
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# for ppg-based voice conversion
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self.extractor = None
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self.convertor = None # ppg2mel
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self.current_wav = None
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self.waves_list = []
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self.waves_count = 0
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self.waves_namelist = []
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# Check for webrtcvad (enables removal of silences in vocoder output)
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try:
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import webrtcvad
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self.trim_silences = True
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except:
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self.trim_silences = False
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# Initialize the events and the interface
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self.ui = UI(vc_mode)
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self.style_idx = 0
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self.reset_ui(enc_models_dir, syn_models_dir, voc_models_dir, extractor_models_dir, convertor_models_dir, seed)
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self.setup_events()
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self.ui.start()
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def excepthook(self, exc_type, exc_value, exc_tb):
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traceback.print_exception(exc_type, exc_value, exc_tb)
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self.ui.log("Exception: %s" % exc_value)
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def setup_events(self):
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# Dataset, speaker and utterance selection
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self.ui.browser_load_button.clicked.connect(lambda: self.load_from_browser())
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random_func = lambda level: lambda: self.ui.populate_browser(self.datasets_root,
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recognized_datasets,
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level)
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self.ui.random_dataset_button.clicked.connect(random_func(0))
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self.ui.random_speaker_button.clicked.connect(random_func(1))
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self.ui.random_utterance_button.clicked.connect(random_func(2))
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self.ui.dataset_box.currentIndexChanged.connect(random_func(1))
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self.ui.speaker_box.currentIndexChanged.connect(random_func(2))
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# Model selection
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self.ui.encoder_box.currentIndexChanged.connect(self.init_encoder)
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def func():
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self.synthesizer = None
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if self.vc_mode:
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self.ui.extractor_box.currentIndexChanged.connect(self.init_extractor)
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else:
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self.ui.synthesizer_box.currentIndexChanged.connect(func)
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self.ui.vocoder_box.currentIndexChanged.connect(self.init_vocoder)
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# Utterance selection
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func = lambda: self.load_from_browser(self.ui.browse_file())
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self.ui.browser_browse_button.clicked.connect(func)
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func = lambda: self.ui.draw_utterance(self.ui.selected_utterance, "current")
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self.ui.utterance_history.currentIndexChanged.connect(func)
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func = lambda: self.ui.play(self.ui.selected_utterance.wav, Synthesizer.sample_rate)
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self.ui.play_button.clicked.connect(func)
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self.ui.stop_button.clicked.connect(self.ui.stop)
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self.ui.record_button.clicked.connect(self.record)
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# Source Utterance selection
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if self.vc_mode:
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func = lambda: self.load_soruce_button(self.ui.selected_utterance)
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self.ui.load_soruce_button.clicked.connect(func)
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#Audio
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self.ui.setup_audio_devices(Synthesizer.sample_rate)
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#Wav playback & save
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func = lambda: self.replay_last_wav()
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self.ui.replay_wav_button.clicked.connect(func)
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func = lambda: self.export_current_wave()
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self.ui.export_wav_button.clicked.connect(func)
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self.ui.waves_cb.currentIndexChanged.connect(self.set_current_wav)
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# Generation
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self.ui.vocode_button.clicked.connect(self.vocode)
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self.ui.random_seed_checkbox.clicked.connect(self.update_seed_textbox)
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if self.vc_mode:
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func = lambda: self.convert() or self.vocode()
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self.ui.convert_button.clicked.connect(func)
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else:
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func = lambda: self.synthesize() or self.vocode()
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self.ui.generate_button.clicked.connect(func)
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self.ui.synthesize_button.clicked.connect(self.synthesize)
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# UMAP legend
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self.ui.clear_button.clicked.connect(self.clear_utterances)
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def set_current_wav(self, index):
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self.current_wav = self.waves_list[index]
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def export_current_wave(self):
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self.ui.save_audio_file(self.current_wav, Synthesizer.sample_rate)
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def replay_last_wav(self):
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self.ui.play(self.current_wav, Synthesizer.sample_rate)
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def reset_ui(self, encoder_models_dir, synthesizer_models_dir, vocoder_models_dir, extractor_models_dir, convertor_models_dir, seed):
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self.ui.populate_browser(self.datasets_root, recognized_datasets, 0, True)
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self.ui.populate_models(encoder_models_dir, synthesizer_models_dir, vocoder_models_dir, extractor_models_dir, convertor_models_dir, self.vc_mode)
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self.ui.populate_gen_options(seed, self.trim_silences)
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def load_from_browser(self, fpath=None):
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if fpath is None:
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fpath = Path(self.datasets_root,
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self.ui.current_dataset_name,
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self.ui.current_speaker_name,
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self.ui.current_utterance_name)
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name = str(fpath.relative_to(self.datasets_root))
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speaker_name = self.ui.current_dataset_name + '_' + self.ui.current_speaker_name
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# Select the next utterance
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if self.ui.auto_next_checkbox.isChecked():
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self.ui.browser_select_next()
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elif fpath == "":
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return
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else:
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name = fpath.name
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speaker_name = fpath.parent.name
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if fpath.suffix.lower() == ".mp3" and self.no_mp3_support:
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self.ui.log("Error: No mp3 file argument was passed but an mp3 file was used")
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return
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# Get the wav from the disk. We take the wav with the vocoder/synthesizer format for
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# playback, so as to have a fair comparison with the generated audio
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wav = Synthesizer.load_preprocess_wav(fpath)
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self.ui.log("Loaded %s" % name)
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self.add_real_utterance(wav, name, speaker_name)
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def load_soruce_button(self, utterance: Utterance):
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self.selected_source_utterance = utterance
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def record(self):
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wav = self.ui.record_one(encoder.sampling_rate, 5)
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if wav is None:
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return
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self.ui.play(wav, encoder.sampling_rate)
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speaker_name = "user01"
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name = speaker_name + "_rec_%05d" % np.random.randint(100000)
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self.add_real_utterance(wav, name, speaker_name)
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def add_real_utterance(self, wav, name, speaker_name):
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# Compute the mel spectrogram
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spec = Synthesizer.make_spectrogram(wav)
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self.ui.draw_spec(spec, "current")
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# Compute the embedding
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if not encoder.is_loaded():
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self.init_encoder()
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encoder_wav = encoder.preprocess_wav(wav)
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embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
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# Add the utterance
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utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, False)
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self.utterances.add(utterance)
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self.ui.register_utterance(utterance, self.vc_mode)
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# Plot it
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self.ui.draw_embed(embed, name, "current")
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self.ui.draw_umap_projections(self.utterances)
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def clear_utterances(self):
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self.utterances.clear()
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self.ui.draw_umap_projections(self.utterances)
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def synthesize(self):
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self.ui.log("Generating the mel spectrogram...")
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self.ui.set_loading(1)
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# Update the synthesizer random seed
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if self.ui.random_seed_checkbox.isChecked():
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seed = int(self.ui.seed_textbox.text())
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self.ui.populate_gen_options(seed, self.trim_silences)
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else:
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seed = None
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if seed is not None:
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torch.manual_seed(seed)
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# Synthesize the spectrogram
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if self.synthesizer is None or seed is not None:
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self.init_synthesizer()
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texts = self.ui.text_prompt.toPlainText().split("\n")
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punctuation = '！，。、,' # punctuate and split/clean text
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processed_texts = []
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for text in texts:
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for processed_text in re.sub(r'[{}]+'.format(punctuation), '\n', text).split('\n'):
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if processed_text:
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processed_texts.append(processed_text.strip())
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texts = processed_texts
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embed = self.ui.selected_utterance.embed
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embeds = [embed] * len(texts)
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min_token = int(self.ui.token_slider.value())
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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)
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breaks = [spec.shape[1] for spec in specs]
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spec = np.concatenate(specs, axis=1)
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self.ui.draw_spec(spec, "generated")
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self.current_generated = (self.ui.selected_utterance.speaker_name, spec, breaks, None)
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self.ui.set_loading(0)
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def vocode(self):
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speaker_name, spec, breaks, _ = self.current_generated
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assert spec is not None
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# Initialize the vocoder model and make it determinstic, if user provides a seed
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if self.ui.random_seed_checkbox.isChecked():
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seed = int(self.ui.seed_textbox.text())
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self.ui.populate_gen_options(seed, self.trim_silences)
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else:
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seed = None
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if seed is not None:
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torch.manual_seed(seed)
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# Synthesize the waveform
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if not vocoder.is_loaded() or seed is not None:
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self.init_vocoder()
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def vocoder_progress(i, seq_len, b_size, gen_rate):
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real_time_factor = (gen_rate / Synthesizer.sample_rate) * 1000
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line = "Waveform generation: %d/%d (batch size: %d, rate: %.1fkHz - %.2fx real time)" \
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% (i * b_size, seq_len * b_size, b_size, gen_rate, real_time_factor)
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self.ui.log(line, "overwrite")
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self.ui.set_loading(i, seq_len)
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if self.ui.current_vocoder_fpath is not None:
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self.ui.log("")
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wav, sample_rate = vocoder.infer_waveform(spec, progress_callback=vocoder_progress)
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else:
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self.ui.log("Waveform generation with Griffin-Lim... ")
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wav = Synthesizer.griffin_lim(spec)
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self.ui.set_loading(0)
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self.ui.log(" Done!", "append")
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# Add breaks
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b_ends = np.cumsum(np.array(breaks) * Synthesizer.hparams.hop_size)
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b_starts = np.concatenate(([0], b_ends[:-1]))
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wavs = [wav[start:end] for start, end, in zip(b_starts, b_ends)]
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breaks = [np.zeros(int(0.15 * sample_rate))] * len(breaks)
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wav = np.concatenate([i for w, b in zip(wavs, breaks) for i in (w, b)])
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# Trim excessive silences
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if self.ui.trim_silences_checkbox.isChecked():
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wav = encoder.preprocess_wav(wav)
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# Play it
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wav = wav / np.abs(wav).max() * 0.97
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self.ui.play(wav, sample_rate)
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# Name it (history displayed in combobox)
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# TODO better naming for the combobox items?
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wav_name = str(self.waves_count + 1)
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#Update waves combobox
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self.waves_count += 1
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if self.waves_count > MAX_WAVES:
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self.waves_list.pop()
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self.waves_namelist.pop()
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self.waves_list.insert(0, wav)
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self.waves_namelist.insert(0, wav_name)
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self.ui.waves_cb.disconnect()
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self.ui.waves_cb_model.setStringList(self.waves_namelist)
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self.ui.waves_cb.setCurrentIndex(0)
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self.ui.waves_cb.currentIndexChanged.connect(self.set_current_wav)
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# Update current wav
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self.set_current_wav(0)
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#Enable replay and save buttons:
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self.ui.replay_wav_button.setDisabled(False)
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self.ui.export_wav_button.setDisabled(False)
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# Compute the embedding
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# TODO: this is problematic with different sampling rates, gotta fix it
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if not encoder.is_loaded():
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self.init_encoder()
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encoder_wav = encoder.preprocess_wav(wav)
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embed, partial_embeds, _ = encoder.embed_utterance(encoder_wav, return_partials=True)
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# Add the utterance
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name = speaker_name + "_gen_%05d" % np.random.randint(100000)
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utterance = Utterance(name, speaker_name, wav, spec, embed, partial_embeds, True)
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self.utterances.add(utterance)
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# Plot it
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self.ui.draw_embed(embed, name, "generated")
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self.ui.draw_umap_projections(self.utterances)
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def convert(self):
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self.ui.log("Extract PPG and Converting...")
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self.ui.set_loading(1)
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# Init
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if self.convertor is None:
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self.init_convertor()
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if self.extractor is None:
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self.init_extractor()
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src_wav = self.selected_source_utterance.wav
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# Compute the ppg
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if not self.extractor is None:
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ppg = self.extractor.extract_from_wav(src_wav)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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ref_wav = self.ui.selected_utterance.wav
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# Import necessary dependency of Voice Conversion
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from utils.f0_utils import compute_f0, f02lf0, compute_mean_std, get_converted_lf0uv
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ref_lf0_mean, ref_lf0_std = compute_mean_std(f02lf0(compute_f0(ref_wav)))
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lf0_uv = get_converted_lf0uv(src_wav, ref_lf0_mean, ref_lf0_std, convert=True)
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min_len = min(ppg.shape[1], len(lf0_uv))
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ppg = ppg[:, :min_len]
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lf0_uv = lf0_uv[:min_len]
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_, mel_pred, att_ws = self.convertor.inference(
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ppg,
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logf0_uv=torch.from_numpy(lf0_uv).unsqueeze(0).float().to(device),
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spembs=torch.from_numpy(self.ui.selected_utterance.embed).unsqueeze(0).to(device),
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)
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mel_pred= mel_pred.transpose(0, 1)
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breaks = [mel_pred.shape[1]]
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mel_pred= mel_pred.detach().cpu().numpy()
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self.ui.draw_spec(mel_pred, "generated")
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self.current_generated = (self.ui.selected_utterance.speaker_name, mel_pred, breaks, None)
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self.ui.set_loading(0)
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def init_extractor(self):
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if self.ui.current_extractor_fpath is None:
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return
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model_fpath = self.ui.current_extractor_fpath
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self.ui.log("Loading the extractor %s... " % model_fpath)
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self.ui.set_loading(1)
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start = timer()
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import ppg_extractor as extractor
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self.extractor = extractor.load_model(model_fpath)
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self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
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self.ui.set_loading(0)
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def init_convertor(self):
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if self.ui.current_convertor_fpath is None:
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return
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model_fpath = self.ui.current_convertor_fpath
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self.ui.log("Loading the convertor %s... " % model_fpath)
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self.ui.set_loading(1)
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start = timer()
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import ppg2mel as convertor
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self.convertor = convertor.load_model( model_fpath)
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self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
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self.ui.set_loading(0)
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def init_encoder(self):
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model_fpath = self.ui.current_encoder_fpath
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self.ui.log("Loading the encoder %s... " % model_fpath)
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self.ui.set_loading(1)
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start = timer()
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encoder.load_model(model_fpath)
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self.ui.log("Done (%dms)." % int(1000 * (timer() - start)), "append")
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self.ui.set_loading(0)
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def init_synthesizer(self):
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model_fpath = self.ui.current_synthesizer_fpath
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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()
|