diff --git a/codes/data/__init__.py b/codes/data/__init__.py index a390d4ea..f296a53c 100644 --- a/codes/data/__init__.py +++ b/codes/data/__init__.py @@ -81,6 +81,12 @@ def create_dataset(dataset_opt, return_collate=False): default_params = create_hparams() default_params.update(dataset_opt) dataset_opt = munchify(default_params) + elif mode == 'fast_paired_voice_audio_with_phonemes': + from data.audio.fast_paired_dataset_with_phonemes import FastPairedVoiceDataset as D + from models.audio.tts.tacotron2 import create_hparams + default_params = create_hparams() + default_params.update(dataset_opt) + dataset_opt = munchify(default_params) elif mode == 'gpt_tts': from data.audio.gpt_tts_dataset import GptTtsDataset as D from data.audio.gpt_tts_dataset import GptTtsCollater as C diff --git a/codes/data/audio/fast_paired_dataset_with_phonemes.py b/codes/data/audio/fast_paired_dataset_with_phonemes.py new file mode 100644 index 00000000..95d1004e --- /dev/null +++ b/codes/data/audio/fast_paired_dataset_with_phonemes.py @@ -0,0 +1,320 @@ +import hashlib +import os +import random +import sys +import time +from itertools import groupby + +import torch +import torch.nn.functional as F +import torch.utils.data +import torchaudio +from tqdm import tqdm + +from data.audio.paired_voice_audio_dataset import CharacterTokenizer +from data.audio.unsupervised_audio_dataset import load_audio, load_similar_clips +from utils.util import opt_get + + +def parse_tsv_aligned_codes(line, base_path): + fpt = line.strip().split('\t') + def convert_string_list_to_tensor(strlist): + if strlist.startswith('['): + strlist = strlist[1:] + if strlist.endswith(']'): + strlist = strlist[:-1] + as_ints = [int(s) for s in strlist.split(', ')] + return torch.tensor(as_ints) + return os.path.join(base_path, f'{fpt[1]}'), fpt[0], convert_string_list_to_tensor(fpt[2]) + + +class FastPairedVoiceDataset(torch.utils.data.Dataset): + """ + This dataset is derived from paired_voice_audio, but it only supports loading from TSV files generated from the + ocotillo transcription engine, which includes alignment codes. To support the vastly larger TSV files, this dataset + uses an indexing mechanism which randomly selects offsets within the translation file to seek to. The data returned + is relative to these offsets. + + In practice, this means two things: + 1) Index {i} of this dataset means nothing: fetching from the same index will almost always return different data. + As a result, this dataset should not be used for validation or test runs. Use PairedVoiceAudio dataset instead. + 2) This dataset has a slight bias for items with longer text or longer filenames. + + The upshot is that this dataset loads extremely quickly and consumes almost no system memory. + """ + def __init__(self, hparams): + self.paths = hparams['path'] + phoneme_paths = hparams['phoneme_paths'] + self.paths = [(p, False) for p in self.paths] + [(p, True) for p in phoneme_paths] + + self.paths_size_bytes = [os.path.getsize(p) for p, _ in self.paths] + self.total_size_bytes = sum(self.paths_size_bytes) + self.types = opt_get(hparams, ['types'], [0 for _ in self.paths]) + + self.normal_text_end_token = hparams['normal_text_end_token'] + self.load_conditioning = opt_get(hparams, ['load_conditioning'], False) + self.conditioning_candidates = opt_get(hparams, ['num_conditioning_candidates'], 1) + self.conditioning_length = opt_get(hparams, ['conditioning_length'], 44100) + self.produce_ctc_metadata = opt_get(hparams, ['produce_ctc_metadata'], False) + self.debug_failures = opt_get(hparams, ['debug_loading_failures'], False) + self.text_cleaners = hparams.text_cleaners + self.sample_rate = hparams.sample_rate + self.aligned_codes_to_audio_ratio = 443 * self.sample_rate // 22050 + self.max_wav_len = opt_get(hparams, ['max_wav_length'], None) + self.load_aligned_codes = opt_get(hparams, ['load_aligned_codes'], False) + if self.max_wav_len is not None: + self.max_aligned_codes = self.max_wav_len // self.aligned_codes_to_audio_ratio + self.max_text_len = opt_get(hparams, ['max_text_length'], None) + assert self.max_wav_len is not None and self.max_text_len is not None + self.use_bpe_tokenizer = opt_get(hparams, ['use_bpe_tokenizer'], False) + if self.use_bpe_tokenizer: + from data.audio.voice_tokenizer import VoiceBpeTokenizer + self.tokenizer = VoiceBpeTokenizer(opt_get(hparams, ['tokenizer_vocab'], '../experiments/bpe_lowercase_asr_256.json')) + else: + self.tokenizer = CharacterTokenizer() + self.skipped_items = 0 # records how many items are skipped when accessing an index. + + self.load_times = torch.zeros((256,)) + self.load_ind = 0 + + def get_wav_text_pair(self, audiopath_and_text): + # separate filename and text + audiopath, text = audiopath_and_text[0], audiopath_and_text[1] + text_seq = self.get_text(text) + wav = load_audio(audiopath, self.sample_rate) + return (text_seq, wav, text, audiopath_and_text[0]) + + def get_text(self, text): + tokens = self.tokenizer.encode(text) + tokens = torch.IntTensor(tokens) + if self.use_bpe_tokenizer: + # Assert if any UNK,start tokens encountered. + assert not torch.any(tokens == 1) + # The stop token should always be sacred. + assert not torch.any(tokens == 0) + return tokens + + def load_random_line(self, depth=0): + assert depth < 10 + + rand_offset = random.randint(0, self.total_size_bytes) + for i in range(len(self.paths)): + if rand_offset < self.paths_size_bytes[i]: + break + else: + rand_offset -= self.paths_size_bytes[i] + path, is_phonetic = self.paths[i] + type = self.types[i] + with open(path, 'r', encoding='utf-8') as f: + f.seek(rand_offset) + # Read the rest of the line we seeked to, then the line after that. + try: # This can fail when seeking to a UTF-8 escape byte. + f.readline() + except: + return self.load_random_line(depth=depth + 1) # On failure, just recurse and try again. + l2 = f.readline() + + if l2: + try: + base_path = os.path.dirname(path) + return parse_tsv_aligned_codes(l2, base_path), type, is_phonetic + except: + print(f"error parsing random offset: {sys.exc_info()}") + return self.load_random_line(depth=depth+1) # On failure, just recurse and try again. + + def get_ctc_metadata(self, codes): + grouped = groupby(codes.tolist()) + rcodes, repeats, seps = [], [], [0] + for val, group in grouped: + if val == 0: + seps[-1] = len(list(group)) # This is a very important distinction! It means the padding belongs to the character proceeding it. + else: + rcodes.append(val) + repeats.append(len(list(group))) + seps.append(0) + + rcodes = torch.tensor(rcodes) + # These clip values are sane maximum values which I did not see in the datasets I have access to. + repeats = torch.clip(torch.tensor(repeats), min=1, max=30) + seps = torch.clip(torch.tensor(seps[:-1]), max=120) + + # Pad or clip the codes to get them to exactly self.max_text_len + orig_lens = rcodes.shape[0] + if rcodes.shape[0] < self.max_text_len: + gap = self.max_text_len - rcodes.shape[0] + rcodes = F.pad(rcodes, (0, gap)) + repeats = F.pad(repeats, (0, gap), value=1) # The minimum value for repeats is 1, hence this is the pad value too. + seps = F.pad(seps, (0, gap)) + elif rcodes.shape[0] > self.max_text_len: + rcodes = rcodes[:self.max_text_len] + repeats = rcodes[:self.max_text_len] + seps = seps[:self.max_text_len] + return { + 'ctc_raw_codes': rcodes, + 'ctc_separators': seps, + 'ctc_repeats': repeats, + 'ctc_raw_lengths': orig_lens, + } + + def __getitem__(self, index): + start = time.time() + self.skipped_items += 1 + apt, type, is_phonetic = self.load_random_line() + try: + tseq, wav, text, path = self.get_wav_text_pair(apt) + if text is None or len(text.strip()) == 0: + raise ValueError + cond, cond_is_self = load_similar_clips(apt[0], self.conditioning_length, self.sample_rate, + n=self.conditioning_candidates) if self.load_conditioning else (None, False) + except: + if self.skipped_items > 100: + raise # Rethrow if we have nested too far. + if self.debug_failures: + print(f"error loading {apt[0]} {sys.exc_info()}") + return self[(index+1) % len(self)] + raw_codes = apt[2] + aligned_codes = raw_codes + + actually_skipped_items = self.skipped_items + self.skipped_items = 0 + if wav is None or \ + (self.max_wav_len is not None and wav.shape[-1] > self.max_wav_len) or \ + (self.max_text_len is not None and tseq.shape[0] > self.max_text_len): + # Basically, this audio file is nonexistent or too long to be supported by the dataset. + # It's hard to handle this situation properly. Best bet is to return the a random valid token and skew the dataset somewhat as a result. + if self.debug_failures: + print(f"error loading {path}: ranges are out of bounds; {wav.shape[-1]}, {tseq.shape[0]}") + rv = random.randint(0,len(self)-1) + return self[rv] + + # Shift phonetic token and aligned_code tokens over. + if is_phonetic: + tseq = tseq + self.normal_text_end_token + # But keep the padding/stop tokens. + if self.load_aligned_codes: + aligned_codes = aligned_codes + self.normal_text_end_token + + orig_output = wav.shape[-1] + orig_text_len = tseq.shape[0] + orig_aligned_code_length = aligned_codes.shape[0] + if wav.shape[-1] != self.max_wav_len: + wav = F.pad(wav, (0, self.max_wav_len - wav.shape[-1])) + # These codes are aligned to audio inputs, so make sure to pad them as well. + aligned_codes = F.pad(aligned_codes, (0, self.max_aligned_codes-aligned_codes.shape[0])) + if tseq.shape[0] != self.max_text_len: + tseq = F.pad(tseq, (0, self.max_text_len - tseq.shape[0])) + + elapsed = time.time() - start + self.load_times[self.load_ind] = elapsed + self.load_ind = (self.load_ind + 1) % len(self.load_times) + + res = { + 'real_text': text, + 'padded_text': tseq, + 'text_lengths': torch.tensor(orig_text_len, dtype=torch.long), + 'wav': wav, + 'wav_lengths': torch.tensor(orig_output, dtype=torch.long), + 'filenames': path, + 'skipped_items': actually_skipped_items, + 'load_time': self.load_times.mean(), + 'type': type, + } + if self.load_conditioning: + res['conditioning'] = cond + res['conditioning_contains_self'] = cond_is_self + if self.load_aligned_codes: + res['aligned_codes']: aligned_codes + res['aligned_codes_lengths']: orig_aligned_code_length + if self.produce_ctc_metadata: + res.update(self.get_ctc_metadata(raw_codes)) + + return res + + def __len__(self): + return self.total_size_bytes // 1000 # 1000 cuts down a TSV file to the actual length pretty well. + + +class FastPairedVoiceDebugger: + def __init__(self): + self.total_items = 0 + self.loaded_items = 0 + self.self_conditioning_items = 0 + self.unique_files = set() + self.load_time = 0 + + def get_state(self): + return {'total_items': self.total_items, + 'loaded_items': self.loaded_items, + 'self_conditioning_items': self.self_conditioning_items} + + def load_state(self, state): + if isinstance(state, dict): + self.total_items = opt_get(state, ['total_items'], 0) + self.loaded_items = opt_get(state, ['loaded_items'], 0) + self.self_conditioning_items = opt_get(state, ['self_conditioning_items'], 0) + + def update(self, batch): + self.total_items += batch['wav'].shape[0] + self.loaded_items += batch['skipped_items'].sum().item() + self.load_time = batch['load_time'].mean().item() + for filename in batch['filenames']: + self.unique_files.add(hashlib.sha256(filename.encode('utf-8'))) + if 'conditioning' in batch.keys(): + self.self_conditioning_items += batch['conditioning_contains_self'].sum().item() + + def get_debugging_map(self): + return { + 'total_samples_loaded': self.total_items, + 'percent_skipped_samples': (self.loaded_items - self.total_items) / self.loaded_items, + 'percent_conditioning_is_self': self.self_conditioning_items / self.loaded_items, + 'unique_files_loaded': len(self.unique_files), + 'load_time': self.load_time, + } + + +if __name__ == '__main__': + batch_sz = 16 + params = { + 'mode': 'fast_paired_voice_audio_with_phonemes', + 'path': ['y:/libritts/train-clean-100/transcribed-oco.tsv',], + 'phoneme_paths': ['y:/libritts/train-other-500/transcribed-phoneme-oco.tsv'], + 'types': [0,0], + 'normal_text_end_token': 256, + 'phase': 'train', + 'n_workers': 0, + 'batch_size': batch_sz, + 'max_wav_length': 220500, + 'max_text_length': 500, + 'sample_rate': 22050, + 'load_conditioning': True, + 'num_conditioning_candidates': 2, + 'conditioning_length': 102400, + 'use_bpe_tokenizer': True, + 'load_aligned_codes': False, + } + from data import create_dataset, create_dataloader + + def save(b, i, ib, key, c=None): + if c is not None: + torchaudio.save(f'{i}_clip_{ib}_{key}_{c}.wav', b[key][ib][c], 22050) + else: + torchaudio.save(f'{i}_clip_{ib}_{key}.wav', b[key][ib], 22050) + + ds, c = create_dataset(params, return_collate=True) + dl = create_dataloader(ds, params, collate_fn=c) + i = 0 + m = None + max_pads, max_repeats = 0, 0 + for i, b in tqdm(enumerate(dl)): + for ib in range(batch_sz): + #max_pads = max(max_pads, b['ctc_pads'].max()) + #max_repeats = max(max_repeats, b['ctc_repeats'].max()) + print(f'{i} {ib} {b["real_text"][ib]}') + #save(b, i, ib, 'wav') + #save(b, i, ib, 'conditioning', 0) + #save(b, i, ib, 'conditioning', 1) + pass + if i > 15: + break + print(max_pads, max_repeats) +