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T4
| import os | |
| import numpy | |
| import soundfile as sf | |
| import torch | |
| from InferenceInterfaces.ToucanTTSInterface import ToucanTTSInterface | |
| from Modules.Aligner.Aligner import Aligner | |
| from Modules.ToucanTTS.DurationCalculator import DurationCalculator | |
| from Modules.ToucanTTS.EnergyCalculator import EnergyCalculator | |
| from Modules.ToucanTTS.PitchCalculator import Parselmouth | |
| from Preprocessing.AudioPreprocessor import AudioPreprocessor | |
| from Preprocessing.TextFrontend import ArticulatoryCombinedTextFrontend | |
| from Preprocessing.articulatory_features import get_feature_to_index_lookup | |
| from Utility.storage_config import MODELS_DIR | |
| from Utility.utils import float2pcm | |
| class UtteranceCloner: | |
| """ | |
| Clone the prosody of an utterance, but exchange the speaker (or don't) | |
| Useful for Privacy Applications | |
| """ | |
| def __init__(self, model_id, device, language="eng"): | |
| self.tts = ToucanTTSInterface(device=device, tts_model_path=model_id) | |
| self.ap = AudioPreprocessor(input_sr=100, output_sr=16000, cut_silence=False) | |
| self.tf = ArticulatoryCombinedTextFrontend(language=language, device=device) | |
| self.device = device | |
| acoustic_checkpoint_path = os.path.join(MODELS_DIR, "Aligner", "aligner.pt") | |
| self.aligner_weights = torch.load(acoustic_checkpoint_path, map_location=device)["asr_model"] | |
| 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.acoustic_model = Aligner() | |
| self.acoustic_model = self.acoustic_model.to(self.device) | |
| self.acoustic_model.load_state_dict(self.aligner_weights) | |
| self.acoustic_model.eval() | |
| self.parsel = Parselmouth(reduction_factor=1, fs=16000) | |
| self.energy_calc = EnergyCalculator(reduction_factor=1, fs=16000) | |
| self.dc = DurationCalculator(reduction_factor=1) | |
| def extract_prosody(self, transcript, ref_audio_path, lang="eng", on_line_fine_tune=True): | |
| if on_line_fine_tune: | |
| self.acoustic_model.load_state_dict(self.aligner_weights) | |
| self.acoustic_model.eval() | |
| wave, sr = sf.read(ref_audio_path) | |
| if self.tf.language != lang: | |
| self.tf = ArticulatoryCombinedTextFrontend(language=lang, device=self.device) | |
| if self.ap.input_sr != sr: | |
| self.ap = AudioPreprocessor(input_sr=sr, output_sr=16000, cut_silence=False) | |
| try: | |
| norm_wave = self.ap.normalize_audio(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) | |
| if len(speech_timestamps) == 0: | |
| speech_timestamps = [{'start': 0, 'end': len(norm_wave)}] | |
| start_silence = speech_timestamps[0]['start'] | |
| end_silence = len(norm_wave) - speech_timestamps[-1]['end'] | |
| norm_wave = norm_wave[speech_timestamps[0]['start']:speech_timestamps[-1]['end']] | |
| norm_wave_length = torch.LongTensor([len(norm_wave)]) | |
| text = self.tf.string_to_tensor(transcript, handle_missing=False).squeeze(0) | |
| features = self.ap.audio_to_mel_spec_tensor(audio=norm_wave, explicit_sampling_rate=16000).transpose(0, 1) | |
| feature_length = torch.LongTensor([len(features)]).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 = 4 | |
| tokens = self.tf.text_vectors_to_id_sequence(text_vector=text) # we need an ID sequence for training rather than a sequence of phonological features | |
| tokens = torch.LongTensor(tokens).squeeze().to(self.device) | |
| tokens_len = torch.LongTensor([len(tokens)]).to(self.device) | |
| mel = features.unsqueeze(0).to(self.device) | |
| mel_len = torch.LongTensor([len(mel[0])]).to(self.device) | |
| # actual fine-tuning starts here | |
| optim_asr = torch.optim.Adam(self.acoustic_model.parameters(), lr=0.00001) | |
| self.acoustic_model.train() | |
| for _ in range(steps): | |
| pred = self.acoustic_model(mel.clone()) | |
| loss = self.acoustic_model.ctc_loss(pred.transpose(0, 1).log_softmax(2), tokens, mel_len, tokens_len) | |
| print(loss.item()) | |
| optim_asr.zero_grad() | |
| loss.backward() | |
| torch.nn.utils.clip_grad_norm_(self.acoustic_model.parameters(), 1.0) | |
| optim_asr.step() | |
| self.acoustic_model.eval() | |
| # We deal with the word boundaries by having 2 versions of text: with and without word boundaries. | |
| # We note the index of word boundaries and insert durations of 0 afterwards | |
| text_without_word_boundaries = list() | |
| indexes_of_word_boundaries = list() | |
| for phoneme_index, vector in enumerate(text): | |
| if vector[get_feature_to_index_lookup()["word-boundary"]] == 0: | |
| text_without_word_boundaries.append(vector.numpy().tolist()) | |
| else: | |
| indexes_of_word_boundaries.append(phoneme_index) | |
| matrix_without_word_boundaries = torch.Tensor(text_without_word_boundaries) | |
| alignment_path = self.acoustic_model.inference(features=features.to(self.device), | |
| tokens=matrix_without_word_boundaries.to(self.device), | |
| return_ctc=False) | |
| duration = self.dc(torch.LongTensor(alignment_path), vis=None).cpu() | |
| for index_of_word_boundary in indexes_of_word_boundaries: | |
| duration = torch.cat([duration[:index_of_word_boundary], | |
| torch.LongTensor([0]), # insert a 0 duration wherever there is a word boundary | |
| duration[index_of_word_boundary:]]) | |
| energy = self.energy_calc(input_waves=norm_wave.unsqueeze(0), | |
| input_waves_lengths=norm_wave_length, | |
| feats_lengths=feature_length, | |
| text=text, | |
| durations=duration.unsqueeze(0), | |
| durations_lengths=torch.LongTensor([len(duration)]))[0].squeeze(0).cpu() | |
| pitch = self.parsel(input_waves=norm_wave.unsqueeze(0), | |
| input_waves_lengths=norm_wave_length, | |
| feats_lengths=feature_length, | |
| text=text, | |
| durations=duration.unsqueeze(0), | |
| durations_lengths=torch.LongTensor([len(duration)]))[0].squeeze(0).cpu() | |
| return duration, pitch, energy, start_silence, end_silence | |
| def clone_utterance(self, | |
| path_to_reference_audio_for_intonation, | |
| path_to_reference_audio_for_voice, | |
| transcription_of_intonation_reference, | |
| filename_of_result=None, | |
| lang="eng"): | |
| """ | |
| What is said in path_to_reference_audio_for_intonation has to match the text in the reference_transcription exactly! | |
| """ | |
| self.tts.set_utterance_embedding(path_to_reference_audio=path_to_reference_audio_for_voice) | |
| duration, pitch, energy, silence_frames_start, silence_frames_end = self.extract_prosody(transcription_of_intonation_reference, | |
| path_to_reference_audio_for_intonation, | |
| lang=lang) | |
| self.tts.set_language(lang) | |
| start_sil = numpy.zeros([int(silence_frames_start * 1.5)]) # timestamps are from 16kHz, but now we're using 24000Hz, so upsampling required | |
| end_sil = numpy.zeros([int(silence_frames_end * 1.5)]) # timestamps are from 16kHz, but now we're using 24000Hz, so upsampling required | |
| cloned_speech, sr = self.tts(transcription_of_intonation_reference, view=False, durations=duration, pitch=pitch, energy=energy) | |
| cloned_utt = numpy.concatenate([start_sil, cloned_speech, end_sil], axis=0) | |
| if filename_of_result is not None: | |
| sf.write(file=filename_of_result, data=float2pcm(cloned_utt), samplerate=sr, subtype="PCM_16") | |
| return cloned_utt, sr | |