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munch import munchify +from tqdm import tqdm +from transformers import GPT2TokenizerFast + +from data.audio.unsupervised_audio_dataset import load_audio, UnsupervisedAudioDataset +from data.text.hf_datasets_wrapper import HfDataset +from data.util import find_files_of_type, is_audio_file +from models.tacotron2.taco_utils import load_filepaths_and_text +from models.tacotron2.text import text_to_sequence +from utils.util import opt_get + + +def build_paired_voice_dataset(args): + from data.audio.paired_voice_audio_dataset import TextWavLoader as D + from models.tacotron2.hparams import create_hparams + default_params = create_hparams() + default_params.update(args) + dataset_opt = munchify(default_params) + return D(dataset_opt) + + +def clamp(x, minimum, maximum): + return max(minimum, min(x, maximum)) + + +class GrandConjoinedDataset(torch.utils.data.Dataset): + """ + A joint text & speech dataset that joins three separate datasets into a single batch: + 1. Unpaired text + 2. Unpaired speech + 3. Paired speech & text + + Supports situations where the underlying data sources for these three elements are differently sized, e.g. you can + have a massive text corpus of 1B elements, a smaller unpaired speech corpus, and a small paired speech<->text corpus. + + Performs tokenization at this level, ignoring any tokenization performed by upstream datasets. + """ + def __init__(self, opt): + paired_dataset_args = opt['paired_dataset_args'] + unsupervised_audio_args = opt['unsupervised_audio_args'] + text_corpus_args = opt['text_corpus_args'] + sample_rate = 22050 + + self.max_paired_audio_length = opt['max_paired_audio_length'] + self.max_paired_text_length = opt['max_paired_text_length'] + self.max_solo_audio_length = opt['max_solo_audio_length'] + self.max_solo_text_length = opt['max_solo_text_length'] + self.sample_rate = sample_rate + + # Set some sane arguments for all three datasets. + paired_dataset_args['needs_collate'] = False + paired_dataset_args['load_conditioning'] = False + paired_dataset_args['sample_rate'] = sample_rate + paired_dataset_args['max_wav_length'] = self.max_paired_audio_length + paired_dataset_args['max_text_length'] = self.max_paired_text_length + unsupervised_audio_args['sampling_rate'] = sample_rate + unsupervised_audio_args['do_augmentation'] = False + unsupervised_audio_args['resample_clip'] = False + unsupervised_audio_args['pad_to_samples'] = self.max_solo_audio_length + + self.speech_and_text = build_paired_voice_dataset(paired_dataset_args) + self.speech = UnsupervisedAudioDataset(unsupervised_audio_args) + self.text = HfDataset(**text_corpus_args) + + def fetch_text_at(self, i): + try: + txt = self.text[i % len(self.text)]['text'] + tok = self.speech_and_text.get_text(txt) + padding_required = self.max_solo_text_length - tok.shape[0] + if padding_required < 0: + # Just truncate since there is no conditioning requried. + tok = tok[:self.max_solo_text_length] + elif padding_required > 0: + tok = F.pad(tok, (0, padding_required)) + return txt, tok + except: + # This is fully expected: there are a lot of text strings we intentionally do not + # handle (e.g. ones with emojis, or other languages). Just return another one. + return self.fetch_text_at((i+1) % len(self.text)) + + def __getitem__(self, i): + snt = self.speech_and_text[i % len(self.speech_and_text)] + sp = self.speech[i % len(self.speech)] + txt, txt_tok = self.fetch_text_at(i % len(self.text)) + + return { + 'paired_audio': snt['wav'], + 'paired_audio_lengths': snt['wav_lengths'], + 'paired_text': snt['real_text'], + 'paired_text_tokens': snt['padded_text'], + 'paired_file': snt['filenames'], + 'speech_audio': sp['clip'], + 'speech_lengths': clamp(sp['clip_lengths'], 0, self.max_solo_audio_length), + 'speech_file': sp['path'], + 'text_text': txt, + 'text_tokens': txt_tok, + } + + def __len__(self): + return max(len(self.speech), len(self.speech_and_text), len(self.text)) + + +if __name__ == '__main__': + batch_sz = 8 + params = { + 'mode': 'grand_conjoined_voice', + 'phase': 'train', + 'n_workers': 0, + 'batch_size': batch_sz, + + 'max_paired_audio_length': 255995, + 'max_paired_text_length': 80, + 'max_solo_text_length': 330, + 'max_solo_audio_length': 300000, + 'paired_dataset_args': { + 'path': ['Z:\\bigasr_dataset\\libritts\\test-clean_list.txt'], + 'fetcher_mode': ['libritts'], + }, + 'unsupervised_audio_args': { + 'path': ['Z:\\bigasr_dataset\\librispeech\\test_clean'], + 'cache_path': 'test_cache_delete_me.pth', + }, + 'text_corpus_args': { + 'corpi': [['bookcorpus', '']], + 'cache_path': 'Z:\\huggingface_datasets\\cache', + }, + } + from data import create_dataset, create_dataloader + + ds = create_dataset(params) + dl = create_dataloader(ds, params) + + def save(b, i, ib, key): + torchaudio.save(f'{i}_clip_{ib}_{key}.wav', b[key][ib], 22050) + + def decode(b, ib, key): + return ds.speech_and_text.tokenizer.decode(b[key][ib].cpu().numpy()) + + i = 0 + m = None + for i, b in tqdm(enumerate(dl)): + for ib in range(batch_sz): + #save(b, i, ib, 'paired_audio') + print(f'Paired text: {b["paired_text"][ib]}') + print(f'Paired text decoded: {decode(b, ib, "paired_text_tokens")}') + #save(b, i, ib, 'speech_audio') + print(f'Text: {b["text_text"][ib]}') + print(f'Text decoded: {decode(b, ib, "text_tokens")}') + diff --git a/codes/data/audio/paired_voice_audio_dataset.py b/codes/data/audio/paired_voice_audio_dataset.py index 9118f5f4..dd7367cb 100644 --- a/codes/data/audio/paired_voice_audio_dataset.py +++ b/codes/data/audio/paired_voice_audio_dataset.py @@ -85,7 +85,7 @@ class TextWavLoader(torch.utils.data.Dataset): self.needs_collate = opt_get(hparams, ['needs_collate'], True) if not self.needs_collate: assert self.max_wav_len is not None and self.max_text_len is not None - self.tokenizer = Tokenizer.from_file(opt_get(hparams, ['tokenizer_vocab'], '../experiments/custom_lowercase_gptvoice_tokenizer.json')) + self.tokenizer = Tokenizer.from_file(opt_get(hparams, ['tokenizer_vocab'], '../experiments/custom_lowercase_gptvoice_tokenizer_r2.json')) def get_wav_text_pair(self, audiopath_and_text): # separate filename and text diff --git a/codes/data/audio/unsupervised_audio_dataset.py b/codes/data/audio/unsupervised_audio_dataset.py index 4f9b75ac..5e74dfef 100644 --- a/codes/data/audio/unsupervised_audio_dataset.py +++ b/codes/data/audio/unsupervised_audio_dataset.py @@ -143,6 +143,7 @@ class UnsupervisedAudioDataset(torch.utils.data.Dataset): output = { 'clip': clips[0], + 'clip_lengths': audio_norm.shape[-1], 'path': filename, } if self.should_resample_clip: diff --git a/codes/data/audio/voice_tokenizer_builder.py b/codes/data/audio/voice_tokenizer_builder.py index d1982ac0..c2dd3edc 100644 --- a/codes/data/audio/voice_tokenizer_builder.py +++ b/codes/data/audio/voice_tokenizer_builder.py @@ -4,6 +4,7 @@ import datasets from tokenizers import Tokenizer from tokenizers.models import BPE from tokenizers.pre_tokenizers import Whitespace +from tokenizers.processors import ByteLevel from tokenizers.trainers import BpeTrainer from data.audio.paired_voice_audio_dataset import load_mozilla_cv, load_voxpopuli, load_tsv @@ -33,9 +34,8 @@ def train(): with open('all_texts.txt', 'r', encoding='utf-8') as at: ttsd = at.readlines() bcd = datasets.load_dataset('bookcorpus', cache_dir='Z:\\huggingface_datasets\\cache')['train'] - wkd = datasets.load_dataset('wikipedia', '20200501.en', cache_dir='Z:\\huggingface_datasets\\cache')['train'] - allowed_characters_re = re.compile(r'^[0-9a-z!@#%_=:;"/, \-\$\^&\*\(\)\+\{\[\]\}\\\.\'\?—ʼ]+$') + allowed_characters_re = re.compile(r'^[0-9a-z!@#%_=:;"/, \-\$\^&\*\(\)\+\{\[\]\}\\\.\'\?—–ʼ]+$') def preprocess_word(word, report=False): word = word.strip().lower() if not bool(allowed_characters_re.match(word)): @@ -53,14 +53,13 @@ def train(): for i in range(0, len(bcd), batch_size): yield [preprocess_word(t) for t in bcd[i:i+batch_size]['text']] - print("Processing wikipedia.") - for i in range(0, len(wkd), batch_size): - yield [preprocess_word(t) for t in wkd[i:i+batch_size]['text']] - - trainer = BpeTrainer(special_tokens=['[STOP]', '[UNK]'], vocab_size=9999) + trainer = BpeTrainer(special_tokens=['[STOP]', '[UNK]'], vocab_size=9999, continuing_subword_prefix='$$$') tokenizer = Tokenizer(BPE(unk_token="[UNK]")) tokenizer.pre_tokenizer = Whitespace() - tokenizer.train_from_iterator(batch_iterator(), trainer, length=len(ttsd)+len(bcd)+len(wkd)) + tokenizer.train_from_iterator(batch_iterator(), trainer, length=len(ttsd)+len(bcd)) + + print(tokenizer.decode(tokenizer.encode("i was traveling throughhadslfghds the woods in 1235375t137{{}}").ids)) + tokenizer.save('gpt_tts_tokenizer.json') @@ -74,4 +73,4 @@ if __name__ == '__main__': ('Y:\\clips\\books2-transcribed.tsv', 'tsv'), ('Y:\\clips\\podcasts-0-transcribed.tsv', 'tsv')], 'all_texts.txt') ''' - train() \ No newline at end of file + train() diff --git a/codes/data/text/hf_datasets_wrapper.py b/codes/data/text/hf_datasets_wrapper.py new file mode 100644 index 00000000..03e7c165 --- /dev/null +++ b/codes/data/text/hf_datasets_wrapper.py @@ -0,0 +1,36 @@ +from torch.utils.data import Dataset +import datasets + + +class HfDataset(Dataset): + """ + Simple wrapper for a HuggingFace dataset that can re-map keys if desired. + """ + def __init__(self, corpi, cache_path=None, key_maps=None, dataset_spec_key='train'): + self.hfd = [] + for corpus in corpi: + dataset_name, config = corpus + if config == '': + config = None + self.hfd.append(datasets.load_dataset(dataset_name, config, cache_dir=cache_path)[dataset_spec_key]) + self.key_maps = key_maps + + def __getitem__(self, item): + for dataset in self.hfd: + if item < len(dataset): + val = dataset[item] + if self.key_maps is None: + return val + else: + return {k: val[v] for k, v in self.key_maps.items()} + else: + item -= len(dataset) + raise IndexError() + + def __len__(self): + return sum([len(h) for h in self.hfd]) + + +if __name__ == '__main__': + d = HfDataset([['wikipedia', '20200501.en'], ['bookcorpus', '']], dataset_spec_key='train', cache_path='Z:\\huggingface_datasets\\cache') + print(d[5]) diff --git a/codes/models/gpt_voice/unified_voice.py b/codes/models/gpt_voice/unified_voice.py new file mode 100644 index 00000000..54bf6ffe --- /dev/null +++ b/codes/models/gpt_voice/unified_voice.py @@ -0,0 +1,165 @@ +import random +from time import time + +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import GPT2Model, GPT2Config, GPT2LMHeadModel, GPT2PreTrainedModel +from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions +from transformers.utils.model_parallel_utils import get_device_map, assert_device_map + +from models.arch_util import AttentionBlock +from models.gpt_voice.gpt_asr_hf import GPT2InferenceModel +from models.gpt_voice.mini_encoder import AudioMiniEncoder +from models.tacotron2.text import symbols +from trainer.networks import register_model +from utils.util import opt_get + + +class ConditioningEncoder(nn.Module): + def __init__(self, + spec_dim, + embedding_dim, + attn_blocks=6, + num_attn_heads=4, + do_checkpointing=False): + super().__init__() + attn = [] + self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1) + for a in range(attn_blocks): + attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing)) + self.attn = nn.Sequential(*attn) + self.dim = embedding_dim + self.do_checkpointing = do_checkpointing + + def forward(self, x): + h = self.init(x) + h = self.attn(h) + return h[:, :, 0] + + +class GptTtsHf(nn.Module): + NUMBER_TEXT_TOKENS = 10000 # The number of tokens produced by our bespoke BPE tokenizer. + START_TEXT_TOKEN = 9999 + STOP_TEXT_TOKEN = 0 + NUMBER_MEL_CODES = 8194 + START_MEL_TOKEN = 8192 + STOP_MEL_TOKEN = 8193 + + def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=80, max_mel_tokens=250, max_conditioning_inputs=3, + checkpointing=True, mel_length_compression=1024, max_conditioning_length=60): + super().__init__() + + + self.max_mel_tokens = max_mel_tokens + self.max_symbols_per_phrase = max_symbols_per_phrase + self.model_dim = model_dim + self.max_conditioning_inputs = max_conditioning_inputs + self.mel_length_compression = mel_length_compression + self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads) + self.text_embedding = nn.Embedding(self.NUMBER_TEXT_TOKENS, model_dim) + seq_length = 2+self.max_symbols_per_phrase+self.max_conditioning_inputs+self.max_mel_tokens + self.gpt_config = GPT2Config(vocab_size=self.NUMBER_MEL_CODES, + n_positions=seq_length, + n_ctx=seq_length, + n_embd=model_dim, + n_layer=layers, + n_head=heads, + gradient_checkpointing=checkpointing, + use_cache=not checkpointing) + self.gpt = GPT2Model(self.gpt_config) + self.final_norm = nn.LayerNorm(model_dim) + self.text_head = nn.Linear(model_dim, self.NUMBER_TEXT_TOKENS) + self.mel_head = nn.Linear(model_dim, self.NUMBER_MEL_CODES) + self.max_conditioning_length = max_conditioning_length + + + def build_aligned_inputs_and_targets(self, input, start_token, stop_token): + inp = F.pad(input, (1,0), value=start_token) + tar = F.pad(input, (0,1), value=stop_token) + return inp, tar + + def get_logits(self, text_inputs, cond_input, mel_inputs, get_attns=False): + text_emb = self.text_embedding(text_inputs) + cond = self.conditioning_encoder(cond_input).unsqueeze(1) + mel_emb = self.gpt.get_input_embeddings()(mel_inputs) + + emb = torch.cat([text_emb, cond, mel_emb], dim=1) + gpt_out = self.gpt(inputs_embeds=emb, return_dict=True, output_attentions=get_attns) + if get_attns: + return gpt_out.attentions + enc = gpt_out.last_hidden_state + + text_logits = self.final_norm(enc[:, :text_emb.shape[1]]) + text_logits = self.text_head(text_logits) + text_logits = text_logits.permute(0,2,1) + mel_logits = self.final_norm(enc[:, -mel_emb.shape[1]:]) + mel_logits = self.mel_head(mel_logits) + mel_logits = mel_logits.permute(0,2,1) + + return text_logits, mel_logits + + def forward(self, text_inputs, cond_input, mel_targets, wav_lengths, return_attentions=False): + """ + Forward pass + text_inputs: long tensor, (b,t) + cond_inputs: MEL float tensor, (b,c,80,s) + mel_targets: long tensor, (b,m) + mel_lengths: long tensor, (b,) + """ + # Set padding areas within MEL (currently it is coded with the MEL code for ). + mel_lengths = wav_lengths // self.mel_length_compression + for b in range(len(mel_lengths)): + if mel_lengths[b] < mel_targets.shape[-1]: + mel_targets[b, mel_lengths[b]:] = self.STOP_MEL_TOKEN + + # Randomly permute the conditioning spectrogram, to destroy any structure present. + cond_input = cond_input[:,:,torch.randperm(cond_input.shape[-1])] + if cond_input.shape[-1] > self.max_conditioning_length: + cond_input = cond_input[:,:,:self.max_conditioning_length] + + text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.START_TEXT_TOKEN, self.STOP_TEXT_TOKEN) + mel_inputs, mel_targets = self.build_aligned_inputs_and_targets(mel_targets, self.START_MEL_TOKEN, self.STOP_MEL_TOKEN) + text_logits, mel_logits = self.get_logits(text_inputs, cond_input, mel_inputs, get_attns=return_attentions) + if return_attentions: + return mel_logits + loss_text = F.cross_entropy(text_logits, text_targets.long()) + loss_mel = F.cross_entropy(mel_logits, mel_targets.long()) + return loss_text.mean(), loss_mel.mean(), mel_logits + + def inference(self, text_inputs, cond_input, **hf_generate_kwargs): + if not hasattr(self, 'inference_model'): + self.inference_model = GPT2InferenceModel(self.gpt_config, self.gpt, None, self.final_norm, self.mel_head) + + text_inputs = F.pad(text_inputs, (0, self.max_symbols_per_phrase - text_inputs.shape[1]), value=self.STOP_TEXT_TOKEN) + text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.START_TEXT_TOKEN, self.STOP_TEXT_TOKEN) + text_emb = self.text_embedding(text_inputs) + + # Randomly permute the conditioning spectrogram, to destroy any structure present. + cond_input = cond_input[:,:,torch.randperm(cond_input.shape[-1])] + if cond_input.shape[-1] > self.max_conditioning_length: + cond_input = cond_input[:,:,:self.max_conditioning_length] + cond = self.conditioning_encoder(cond_input).unsqueeze(1) + + emb = torch.cat([text_emb, cond], dim=1) + self.inference_model.store_mel_emb(emb) + + fake_inputs = torch.full((emb.shape[0],emb.shape[1]+1,), fill_value=1, dtype=torch.long, device=text_inputs.device) + fake_inputs[:,-1] = self.START_MEL_TOKEN + + gen = self.inference_model.generate(fake_inputs, bos_token_id=self.START_MEL_TOKEN, pad_token_id=self.STOP_MEL_TOKEN, eos_token_id=self.STOP_MEL_TOKEN, + max_length=emb.shape[1]+self.max_mel_tokens, **hf_generate_kwargs) + return gen[:, fake_inputs.shape[1]:] + + +@register_model +def register_gpt_tts_hf(opt_net, opt): + return GptTtsHf(**opt_get(opt_net, ['kwargs'], {})) + + +if __name__ == '__main__': + gpt = GptTtsHf(model_dim=1024, heads=16) + l = gpt(torch.randint(high=len(symbols), size=(2,200)), + torch.arange(0, 80, 1, dtype=torch.float).view(1,80,1).repeat(2,1,800), + torch.randint(high=8192, size=(2,250)), + torch.tensor([150*256,195*256]))