import os import soundfile as sf import torch from torch.optim import SGD from tqdm import tqdm from InferenceInterfaces.Meta_FastSpeech2 import Meta_FastSpeech2 from Preprocessing.ArticulatoryCombinedTextFrontend import ArticulatoryCombinedTextFrontend from Preprocessing.AudioPreprocessor import AudioPreprocessor from TrainingInterfaces.Text_to_Spectrogram.AutoAligner.Aligner import Aligner from TrainingInterfaces.Text_to_Spectrogram.FastSpeech2.DurationCalculator import DurationCalculator from TrainingInterfaces.Text_to_Spectrogram.FastSpeech2.EnergyCalculator import EnergyCalculator from TrainingInterfaces.Text_to_Spectrogram.FastSpeech2.PitchCalculator import Dio class UtteranceCloner: def __init__(self, device): self.tts = Meta_FastSpeech2(device=device) self.device = device torch.hub._validate_not_a_forked_repo = lambda a, b, c: True # torch 1.9 has a bug in the hub loading, this is a workaround # careful: assumes 16kHz or 8kHz audio self.silero_model, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model='silero_vad', force_reload=False, onnx=False, verbose=False) (self.get_speech_timestamps, _, _, _, _) = utils torch.set_grad_enabled(True) # finding this issue was very infuriating: silero sets # this to false globally during model loading rather than using inference mode or no_grad self.silero_model = self.silero_model.to(self.device) def extract_prosody(self, transcript, ref_audio_path, lang="de", on_line_fine_tune=False): acoustic_model = Aligner() acoustic_checkpoint_path = os.path.join("Models", "Aligner", "aligner.pt") acoustic_model.load_state_dict(torch.load(acoustic_checkpoint_path, map_location='cpu')["asr_model"]) acoustic_model = acoustic_model.to(self.device) dio = Dio(reduction_factor=1, fs=16000) energy_calc = EnergyCalculator(reduction_factor=1, fs=16000) dc = DurationCalculator(reduction_factor=1) wave, sr = sf.read(ref_audio_path) tf = ArticulatoryCombinedTextFrontend(language=lang, use_word_boundaries=False) ap = AudioPreprocessor(input_sr=sr, output_sr=16000, melspec_buckets=80, hop_length=256, n_fft=1024, cut_silence=False) try: norm_wave = ap.audio_to_wave_tensor(normalize=True, audio=wave) except ValueError: print('Something went wrong, the reference wave might be too short.') raise RuntimeError with torch.inference_mode(): speech_timestamps = self.get_speech_timestamps(norm_wave, self.silero_model, sampling_rate=16000) norm_wave = norm_wave[speech_timestamps[0]['start']:speech_timestamps[-1]['end']] norm_wave_length = torch.LongTensor([len(norm_wave)]) text = tf.string_to_tensor(transcript, handle_missing=False).squeeze(0) melspec = ap.audio_to_mel_spec_tensor(audio=norm_wave, normalize=False, explicit_sampling_rate=16000).transpose(0, 1) melspec_length = torch.LongTensor([len(melspec)]).numpy() if on_line_fine_tune: # we fine-tune the aligner for a couple steps using SGD. This makes cloning pretty slow, but the results are greatly improved. steps = 10 tokens = list() # we need an ID sequence for training rather than a sequence of phonological features for vector in text: for phone in tf.phone_to_vector: if vector.numpy().tolist() == tf.phone_to_vector[phone]: tokens.append(tf.phone_to_id[phone]) tokens = torch.LongTensor(tokens) tokens = tokens.squeeze().to(self.device) tokens_len = torch.LongTensor([len(tokens)]).to(self.device) mel = melspec.unsqueeze(0).to(self.device) mel.requires_grad = True mel_len = torch.LongTensor([len(mel[0])]).to(self.device) # actual fine-tuning starts here optim_asr = SGD(acoustic_model.parameters(), lr=0.1) acoustic_model.train() for _ in tqdm(list(range(steps))): pred = acoustic_model(mel) loss = acoustic_model.ctc_loss(pred.transpose(0, 1).log_softmax(2), tokens, mel_len, tokens_len) optim_asr.zero_grad() loss.backward() torch.nn.utils.clip_grad_norm_(acoustic_model.parameters(), 1.0) optim_asr.step() acoustic_model.eval() alignment_path = acoustic_model.inference(mel=melspec.to(self.device), tokens=text.to(self.device), return_ctc=False) duration = dc(torch.LongTensor(alignment_path), vis=None).cpu() energy = energy_calc(input_waves=norm_wave.unsqueeze(0), input_waves_lengths=norm_wave_length, feats_lengths=melspec_length, durations=duration.unsqueeze(0), durations_lengths=torch.LongTensor([len(duration)]))[0].squeeze(0).cpu() pitch = dio(input_waves=norm_wave.unsqueeze(0), input_waves_lengths=norm_wave_length, feats_lengths=melspec_length, durations=duration.unsqueeze(0), durations_lengths=torch.LongTensor([len(duration)]))[0].squeeze(0).cpu() return duration, pitch, energy, speech_timestamps[0]['start'], speech_timestamps[-1]['end'] def clone_utterance(self, path_to_reference_audio, reference_transcription, clone_speaker_identity=True, lang="en"): if clone_speaker_identity: self.tts.set_utterance_embedding(path_to_reference_audio=path_to_reference_audio) duration, pitch, energy, silence_frames_start, silence_frames_end = self.extract_prosody(reference_transcription, path_to_reference_audio, lang=lang) self.tts.set_language(lang) start_sil = torch.zeros([silence_frames_start]).to(self.device) end_sil = torch.zeros([silence_frames_end]).to(self.device) cloned_speech = self.tts(reference_transcription, view=False, durations=duration, pitch=pitch, energy=energy) cloned_utt = torch.cat((start_sil, cloned_speech, end_sil), dim=0) return cloned_utt.cpu()