import torch from cached_path import cached_path import nltk # nltk.download('punkt') import random random.seed(0) import numpy as np np.random.seed(0) import time import random import yaml import torch.nn.functional as F import copy import torchaudio import librosa from models import * from scipy.io.wavfile import write from munch import Munch from torch import nn from nltk.tokenize import word_tokenize from monotonic_align import mask_from_lens from monotonic_align.core import maximum_path_c torch.manual_seed(0) torch.backends.cudnn.benchmark = False torch.backends.cudnn.deterministic = True # IPA Phonemizer: https://github.com/bootphon/phonemizer _pad = "$" _punctuation = ';:,.!?¡¿—…"«»“” ' _letters = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz' _letters_ipa = "ɑɐɒæɓʙβɔɕçɗɖðʤəɘɚɛɜɝɞɟʄɡɠɢʛɦɧħɥʜɨɪʝɭɬɫɮʟɱɯɰŋɳɲɴøɵɸθœɶʘɹɺɾɻʀʁɽʂʃʈʧʉʊʋⱱʌɣɤʍχʎʏʑʐʒʔʡʕʢǀǁǂǃˈˌːˑʼʴʰʱʲʷˠˤ˞↓↑→↗↘'̩'ᵻ" # Export all symbols: symbols = [_pad] + list(_punctuation) + list(_letters) + list(_letters_ipa) dicts = {} for i in range(len((symbols))): dicts[symbols[i]] = i class TextCleaner: def __init__(self, dummy=None): self.word_index_dictionary = dicts print(len(dicts)) def __call__(self, text): indexes = [] for char in text: try: indexes.append(self.word_index_dictionary[char]) except KeyError: print('CLEAN', text) return indexes textclenaer = TextCleaner() to_mel = torchaudio.transforms.MelSpectrogram( n_mels=80, n_fft=2048, win_length=1200, hop_length=300) mean, std = -4, 4 # START UTIL def recursive_munch(d): if isinstance(d, dict): return Munch((k, recursive_munch(v)) for k, v in d.items()) elif isinstance(d, list): return [recursive_munch(v) for v in d] else: return d # ======== UTILS ABOVE def length_to_mask(lengths): mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths) mask = torch.gt(mask+1, lengths.unsqueeze(1)) return mask def preprocess(wave): wave_tensor = torch.from_numpy(wave).float() mel_tensor = to_mel(wave_tensor) mel_tensor = (torch.log(1e-5 + mel_tensor.unsqueeze(0)) - mean) / std return mel_tensor def compute_style(path): wave, sr = librosa.load(path, sr=24000) audio, index = librosa.effects.trim(wave, top_db=30) if sr != 24000: audio = librosa.resample(audio, sr, 24000) mel_tensor = preprocess(audio).to(device) with torch.no_grad(): ref_s = model.style_encoder(mel_tensor.unsqueeze(1)) ref_p = model.predictor_encoder(mel_tensor.unsqueeze(1)) return torch.cat([ref_s, ref_p], dim=1) device = 'cpu' if torch.cuda.is_available(): device = 'cuda' elif torch.backends.mps.is_available(): # print("MPS would be available but cannot be used rn") pass # device = 'mps' import phonemizer global_phonemizer = phonemizer.backend.EspeakBackend(language='en-us', preserve_punctuation=True, with_stress=True) # phonemizer = Phonemizer.from_checkpoint(str(cached_path('https://public-asai-dl-models.s3.eu-central-1.amazonaws.com/DeepPhonemizer/en_us_cmudict_ipa_forward.pt'))) config = yaml.safe_load(open(str('Utils/config.yml'))) # load pretrained ASR model ASR_config = config.get('ASR_config', False) ASR_path = config.get('ASR_path', False) text_aligner = load_ASR_models(ASR_path, ASR_config) # load pretrained F0 model F0_path = config.get('F0_path', False) pitch_extractor = load_F0_models(F0_path) # load BERT model from Utils.PLBERT.util import load_plbert BERT_path = config.get('PLBERT_dir', False) plbert = load_plbert(BERT_path) model_params = recursive_munch(config['model_params']) model = build_model(model_params, text_aligner, pitch_extractor, plbert) _ = [model[key].eval() for key in model] _ = [model[key].to(device) for key in model] # params_whole = torch.load("Models/LibriTTS/epochs_2nd_00020.pth", map_location='cpu') params_whole = torch.load(str(cached_path("hf://yl4579/StyleTTS2-LibriTTS/Models/LibriTTS/epochs_2nd_00020.pth")), map_location='cpu') params = params_whole['net'] for key in model: if key in params: print('%s loaded' % key) try: model[key].load_state_dict(params[key]) except: from collections import OrderedDict state_dict = params[key] new_state_dict = OrderedDict() for k, v in state_dict.items(): name = k[7:] # remove `module.` new_state_dict[name] = v # load params model[key].load_state_dict(new_state_dict, strict=False) # except: # _load(params[key], model[key]) _ = [model[key].eval() for key in model] from Modules.diffusion.sampler import DiffusionSampler, ADPM2Sampler, KarrasSchedule sampler = DiffusionSampler( model.diffusion.diffusion, sampler=ADPM2Sampler(), sigma_schedule=KarrasSchedule(sigma_min=0.0001, sigma_max=3.0, rho=9.0), # empirical parameters clamp=False ) def inference(text, ref_s, alpha = 0.3, beta = 0.7, diffusion_steps=5, embedding_scale=1, use_gruut=False): text = text.strip() ps = global_phonemizer.phonemize([text]) # print(f'PHONEMIZER: {ps=}\n\n') #PHONEMIZER: ps=['ɐbˈɛbæbləm '] ps = word_tokenize(ps[0]) # print(f'TOKENIZER: {ps=}\n\n') #OKENIZER: ps=['ɐbˈɛbæbləm'] ps = ' '.join(ps) tokens = textclenaer(ps) # print(f'TEXTCLEAN: {ps=}\n\n') #TEXTCLEAN: ps='ɐbˈɛbæbləm' tokens.insert(0, 0) tokens = torch.LongTensor(tokens).to(device).unsqueeze(0) # print(f'TOKENSFINAL: {ps=}\n\n') with torch.no_grad(): input_lengths = torch.LongTensor([tokens.shape[-1]]).to(device) text_mask = length_to_mask(input_lengths).to(device) # ----------------------- # WHO TRANSLATES these tokens to sylla # print(text_mask.shape, '\n__\n', tokens, '\n__\n', text_mask.min(), text_mask.max()) # text_mask=is binary # tokes = tensor([[ 0, 55, 157, 86, 125, 83, 55, 156, 57, 158, 123, 48, 83, 61, # 157, 102, 61, 16, 138, 64, 16, 53, 156, 138, 54, 62, 131, 85, # 123, 83, 54, 16, 50, 156, 86, 123, 102, 125, 102, 46, 147, 16, # 62, 135, 16, 76, 158, 92, 55, 156, 86, 56, 62, 177, 46, 16, # 50, 157, 43, 102, 58, 85, 55, 156, 51, 158, 46, 51, 158, 83, # 16, 48, 76, 158, 123, 16, 72, 53, 61, 157, 86, 61, 83, 44, # 156, 102, 54, 177, 125, 51, 16, 72, 56, 46, 16, 102, 112, 53, # 54, 156, 63, 158, 147, 83, 56, 16, 4]], device='cuda:0') t_en = model.text_encoder(tokens, input_lengths, text_mask) bert_dur = model.bert(tokens, attention_mask=(~text_mask).int()) d_en = model.bert_encoder(bert_dur).transpose(-1, -2) # print('BERTdu', bert_dur.shape, tokens.shape, '\n') # bert what is the 768 per token -> IS USED in sampler # BERTdu torch.Size([1, 11, 768]) torch.Size([1, 11]) s_pred = sampler(noise = torch.randn((1, 256)).unsqueeze(1).to(device), embedding=bert_dur, embedding_scale=embedding_scale, features=ref_s, # reference from the same speaker as the embedding num_steps=diffusion_steps).squeeze(1) s = s_pred[:, 128:] ref = s_pred[:, :128] ref = alpha * ref + (1 - alpha) * ref_s[:, :128] s = beta * s + (1 - beta) * ref_s[:, 128:] d = model.predictor.text_encoder(d_en, s, input_lengths, text_mask) x, _ = model.predictor.lstm(d) duration = model.predictor.duration_proj(x) duration = torch.sigmoid(duration).sum(axis=-1) pred_dur = torch.round(duration.squeeze()).clamp(min=1) pred_aln_trg = torch.zeros(input_lengths, int(pred_dur.sum().data)) c_frame = 0 for i in range(pred_aln_trg.size(0)): pred_aln_trg[i, c_frame:c_frame + int(pred_dur[i].data)] = 1 c_frame += int(pred_dur[i].data) # encode prosody en = (d.transpose(-1, -2) @ pred_aln_trg.unsqueeze(0).to(device)) if model_params.decoder.type == "hifigan": asr_new = torch.zeros_like(en) asr_new[:, :, 0] = en[:, :, 0] asr_new[:, :, 1:] = en[:, :, 0:-1] en = asr_new F0_pred, N_pred = model.predictor.F0Ntrain(en, s) asr = (t_en @ pred_aln_trg.unsqueeze(0).to(device)) if model_params.decoder.type == "hifigan": asr_new = torch.zeros_like(asr) asr_new[:, :, 0] = asr[:, :, 0] asr_new[:, :, 1:] = asr[:, :, 0:-1] asr = asr_new out = model.decoder(asr, F0_pred, N_pred, ref.squeeze().unsqueeze(0)) return out.squeeze().cpu().numpy()[..., :-50] # weird pulse at the end of the model, need to be fixed later