forked from mrq/DL-Art-School
Revert paired back
This commit is contained in:
James Betker
parent
ad3e7df086
commit
2b36ca5f8e
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@ -15,39 +15,49 @@ from models.tacotron2.text import text_to_sequence, sequence_to_text
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from utils.util import opt_get
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def parse_libri(line, base_path, split="|"):
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fpt = line.strip().split(split)
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fpt[0] = os.path.join(base_path, fpt[0])
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return fpt
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def load_tsv(filename):
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with open(filename, encoding='utf-8') as f:
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components = [line.strip().split('\t') for line in f]
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base = os.path.dirname(filename)
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filepaths_and_text = [[os.path.join(base, f'{component[1]}'), component[0]] for component in components]
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return filepaths_and_text
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def parse_tsv(line, base_path):
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fpt = line.strip().split('\t')
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return os.path.join(base_path, f'{fpt[1]}'), fpt[0]
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def load_tsv_aligned_codes(filename):
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with open(filename, encoding='utf-8') as f:
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components = [line.strip().split('\t') for line in f]
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base = os.path.dirname(filename)
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def convert_string_list_to_tensor(strlist):
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if strlist.startswith('['):
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strlist = strlist[1:]
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if strlist.endswith(']'):
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strlist = strlist[:-1]
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as_ints = [int(s) for s in strlist.split(', ')]
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return torch.tensor(as_ints)
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filepaths_and_text = [[os.path.join(base, f'{component[1]}'), component[0], convert_string_list_to_tensor(component[2])] for component in components]
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return filepaths_and_text
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def parse_tsv_aligned_codes(line, base_path):
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fpt = line.strip().split('\t')
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def convert_string_list_to_tensor(strlist):
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if strlist.startswith('['):
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strlist = strlist[1:]
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if strlist.endswith(']'):
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strlist = strlist[:-1]
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as_ints = [int(s) for s in strlist.split(', ')]
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return torch.tensor(as_ints)
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return os.path.join(base_path, f'{fpt[1]}'), fpt[0], convert_string_list_to_tensor(fpt[2])
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def load_mozilla_cv(filename):
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with open(filename, encoding='utf-8') as f:
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components = [line.strip().split('\t') for line in f][1:] # First line is the header
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base = os.path.dirname(filename)
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filepaths_and_text = [[os.path.join(base, f'clips/{component[1]}'), component[2]] for component in components]
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return filepaths_and_text
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def parse_mozilla_cv(line, base_path):
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components = line.strip().split('\t')
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return os.path.join(base_path, f'clips/{components[1]}'), components[2]
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def parse_voxpopuli(line, base_path):
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line = line.strip().split('\t')
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file, raw_text, norm_text, speaker_id, split, gender = line
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year = file[:4]
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return os.path.join(base_path, year, f'{file}.ogg.wav'), raw_text
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def load_voxpopuli(filename):
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with open(filename, encoding='utf-8') as f:
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lines = [line.strip().split('\t') for line in f][1:] # First line is the header
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base = os.path.dirname(filename)
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filepaths_and_text = []
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for line in lines:
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if len(line) == 0:
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continue
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file, raw_text, norm_text, speaker_id, split, gender = line
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year = file[:4]
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filepaths_and_text.append([os.path.join(base, year, f'{file}.ogg.wav'), raw_text])
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return filepaths_and_text
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class CharacterTokenizer:
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@ -60,16 +70,14 @@ class CharacterTokenizer:
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class TextWavLoader(torch.utils.data.Dataset):
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def __init__(self, hparams):
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self.paths = hparams['path']
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if not isinstance(self.paths, list):
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self.paths = [self.paths]
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self.paths_size_bytes = [os.path.getsize(p) for p in self.paths]
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self.total_size_bytes = sum(self.paths_size_bytes)
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self.path = hparams['path']
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if not isinstance(self.path, list):
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self.path = [self.path]
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self.fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj')
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if not isinstance(self.fetcher_mode, list):
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self.fetcher_mode = [self.fetcher_mode]
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assert len(self.paths) == len(self.fetcher_mode)
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fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj')
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if not isinstance(fetcher_mode, list):
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fetcher_mode = [fetcher_mode]
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assert len(self.path) == len(fetcher_mode)
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self.load_conditioning = opt_get(hparams, ['load_conditioning'], False)
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self.conditioning_candidates = opt_get(hparams, ['num_conditioning_candidates'], 1)
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@ -77,8 +85,25 @@ class TextWavLoader(torch.utils.data.Dataset):
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self.debug_failures = opt_get(hparams, ['debug_loading_failures'], False)
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self.load_aligned_codes = opt_get(hparams, ['load_aligned_codes'], False)
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self.aligned_codes_to_audio_ratio = opt_get(hparams, ['aligned_codes_ratio'], 443)
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self.audiopaths_and_text = []
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for p, fm in zip(self.path, fetcher_mode):
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if fm == 'lj' or fm == 'libritts':
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fetcher_fn = load_filepaths_and_text
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elif fm == 'tsv':
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fetcher_fn = load_tsv_aligned_codes if self.load_aligned_codes else load_tsv
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elif fm == 'mozilla_cv':
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assert not self.load_conditioning # Conditioning inputs are incompatible with mozilla_cv
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fetcher_fn = load_mozilla_cv
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elif fm == 'voxpopuli':
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assert not self.load_conditioning # Conditioning inputs are incompatible with voxpopuli
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fetcher_fn = load_voxpopuli
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else:
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raise NotImplementedError()
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self.audiopaths_and_text.extend(fetcher_fn(p))
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self.text_cleaners = hparams.text_cleaners
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self.sample_rate = hparams.sample_rate
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random.seed(hparams.seed)
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random.shuffle(self.audiopaths_and_text)
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self.max_wav_len = opt_get(hparams, ['max_wav_length'], None)
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if self.max_wav_len is not None:
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self.max_aligned_codes = self.max_wav_len // self.aligned_codes_to_audio_ratio
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@ -109,64 +134,23 @@ class TextWavLoader(torch.utils.data.Dataset):
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assert not torch.any(tokens == 0)
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return tokens
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def load_random_line(self, depth=0):
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assert depth < 10
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rand_offset = random.randint(0, self.total_size_bytes)
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for i in range(len(self.paths)):
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if rand_offset < self.paths_size_bytes[i]:
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break
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else:
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rand_offset -= self.paths_size_bytes[i]
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path = self.paths[i]
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fm = self.fetcher_mode[i]
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with open(path, 'r', encoding='utf-8') as f:
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f.seek(rand_offset)
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# Read the rest of the line we seeked to, then the line after that.
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try: # This can fail when seeking to a UTF-8 escape byte.
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f.readline()
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except:
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return self.load_random_line(depth=depth + 1) # On failure, just recurse and try again.
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l2 = f.readline()
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if l2:
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try:
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base_path = os.path.dirname(path)
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if fm == 'lj' or fm == 'libritts':
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return parse_libri(l2, base_path)
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elif fm == 'tsv':
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return parse_tsv_aligned_codes(l2, base_path) if self.load_aligned_codes else parse_tsv(l2, base_path)
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elif fm == 'mozilla_cv':
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assert not self.load_conditioning # Conditioning inputs are incompatible with mozilla_cv
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return parse_mozilla_cv(l2, base_path)
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elif fm == 'voxpopuli':
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assert not self.load_conditioning # Conditioning inputs are incompatible with voxpopuli
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return parse_voxpopuli(l2, base_path)
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else:
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raise NotImplementedError()
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except:
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print(f"error parsing random offset: {sys.exc_info()}")
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return self.load_random_line(depth=depth+1) # On failure, just recurse and try again.
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def __getitem__(self, index):
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self.skipped_items += 1
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apt = self.load_random_line()
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try:
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tseq, wav, text, path = self.get_wav_text_pair(apt)
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tseq, wav, text, path = self.get_wav_text_pair(self.audiopaths_and_text[index])
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if text is None or len(text.strip()) == 0:
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raise ValueError
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cond, cond_is_self = load_similar_clips(apt[0], self.conditioning_length, self.sample_rate,
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cond, cond_is_self = load_similar_clips(self.audiopaths_and_text[index][0], self.conditioning_length, self.sample_rate,
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n=self.conditioning_candidates) if self.load_conditioning else (None, False)
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except:
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if self.skipped_items > 100:
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raise # Rethrow if we have nested too far.
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if self.debug_failures:
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print(f"error loading {apt[0]} {sys.exc_info()}")
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print(f"error loading {self.audiopaths_and_text[index][0]} {sys.exc_info()}")
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return self[(index+1) % len(self)]
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if self.load_aligned_codes:
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aligned_codes = apt[2]
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aligned_codes = self.audiopaths_and_text[index][2]
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actually_skipped_items = self.skipped_items
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self.skipped_items = 0
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@ -205,7 +189,7 @@ class TextWavLoader(torch.utils.data.Dataset):
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return res
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def __len__(self):
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return self.total_size_bytes // 1000 # 1000 cuts down a TSV file to the actual length pretty well, but doesn't work with the other formats.
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return len(self.audiopaths_and_text)
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class PairedVoiceDebugger:
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@ -240,23 +224,22 @@ class PairedVoiceDebugger:
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if __name__ == '__main__':
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batch_sz = 16
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batch_sz = 8
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params = {
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'mode': 'paired_voice_audio',
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#'path': ['Y:\\clips\\books1\\transcribed-w2v.tsv'],
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'path': ['Y:\\bigasr_dataset\\mozcv\\en\\train.tsv'],
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'fetcher_mode': ['mozilla_cv'],
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'path': ['Y:\\clips\\books1\\transcribed-w2v.tsv'],
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'fetcher_mode': ['tsv'],
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'phase': 'train',
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'n_workers': 0,
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'batch_size': batch_sz,
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'max_wav_length': 255995,
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'max_text_length': 200,
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'sample_rate': 22050,
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'load_conditioning': False,
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'load_conditioning': True,
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'num_conditioning_candidates': 2,
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'conditioning_length': 44000,
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'use_bpe_tokenizer': True,
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'load_aligned_codes': False,
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'load_aligned_codes': True,
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}
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from data import create_dataset, create_dataloader
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@ -273,7 +256,7 @@ if __name__ == '__main__':
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for i, b in tqdm(enumerate(dl)):
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for ib in range(batch_sz):
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print(f'{i} {ib} {b["real_text"][ib]}')
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#save(b, i, ib, 'wav')
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#if i > 5:
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# break
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save(b, i, ib, 'wav')
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if i > 5:
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break
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@ -300,7 +300,7 @@ class Trainer:
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../experiments/train_gpt_asr_mass_hf2_audio_only_fp32/train_gpt_asr_mass_hf2.yml')
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parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_diffusion_tts.yml')
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parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
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parser.add_argument('--local_rank', type=int, default=0)
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args = parser.parse_args()
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