From 2b36ca5f8e970e5fd5e65386db3a30deab938aef Mon Sep 17 00:00:00 2001
From: James Betker <jbetker@gmail.com>
Date: Sun, 16 Jan 2022 21:10:46 -0700
Subject: [PATCH] Revert paired back
---
.../data/audio/paired_voice_audio_dataset.py | 165 ++++++++----------
codes/train.py | 2 +-
2 files changed, 75 insertions(+), 92 deletions(-)
diff --git a/codes/data/audio/paired_voice_audio_dataset.py b/codes/data/audio/paired_voice_audio_dataset.py
index 9d50d1c4..d6123229 100644
--- a/codes/data/audio/paired_voice_audio_dataset.py
+++ b/codes/data/audio/paired_voice_audio_dataset.py
@@ -15,39 +15,49 @@ from models.tacotron2.text import text_to_sequence, sequence_to_text
from utils.util import opt_get
-def parse_libri(line, base_path, split="|"):
- fpt = line.strip().split(split)
- fpt[0] = os.path.join(base_path, fpt[0])
- return fpt
+def load_tsv(filename):
+ with open(filename, encoding='utf-8') as f:
+ components = [line.strip().split('\t') for line in f]
+ base = os.path.dirname(filename)
+ filepaths_and_text = [[os.path.join(base, f'{component[1]}'), component[0]] for component in components]
+ return filepaths_and_text
-def parse_tsv(line, base_path):
- fpt = line.strip().split('\t')
- return os.path.join(base_path, f'{fpt[1]}'), fpt[0]
+def load_tsv_aligned_codes(filename):
+ with open(filename, encoding='utf-8') as f:
+ components = [line.strip().split('\t') for line in f]
+ base = os.path.dirname(filename)
+ 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)
+ filepaths_and_text = [[os.path.join(base, f'{component[1]}'), component[0], convert_string_list_to_tensor(component[2])] for component in components]
+ return filepaths_and_text
-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])
+def load_mozilla_cv(filename):
+ with open(filename, encoding='utf-8') as f:
+ components = [line.strip().split('\t') for line in f][1:] # First line is the header
+ base = os.path.dirname(filename)
+ filepaths_and_text = [[os.path.join(base, f'clips/{component[1]}'), component[2]] for component in components]
+ return filepaths_and_text
-def parse_mozilla_cv(line, base_path):
- components = line.strip().split('\t')
- return os.path.join(base_path, f'clips/{components[1]}'), components[2]
-
-
-def parse_voxpopuli(line, base_path):
- line = line.strip().split('\t')
- file, raw_text, norm_text, speaker_id, split, gender = line
- year = file[:4]
- return os.path.join(base_path, year, f'{file}.ogg.wav'), raw_text
+def load_voxpopuli(filename):
+ with open(filename, encoding='utf-8') as f:
+ lines = [line.strip().split('\t') for line in f][1:] # First line is the header
+ base = os.path.dirname(filename)
+ filepaths_and_text = []
+ for line in lines:
+ if len(line) == 0:
+ continue
+ file, raw_text, norm_text, speaker_id, split, gender = line
+ year = file[:4]
+ filepaths_and_text.append([os.path.join(base, year, f'{file}.ogg.wav'), raw_text])
+ return filepaths_and_text
class CharacterTokenizer:
@@ -60,16 +70,14 @@ class CharacterTokenizer:
class TextWavLoader(torch.utils.data.Dataset):
def __init__(self, hparams):
- self.paths = hparams['path']
- if not isinstance(self.paths, list):
- self.paths = [self.paths]
- self.paths_size_bytes = [os.path.getsize(p) for p in self.paths]
- self.total_size_bytes = sum(self.paths_size_bytes)
+ self.path = hparams['path']
+ if not isinstance(self.path, list):
+ self.path = [self.path]
- self.fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj')
- if not isinstance(self.fetcher_mode, list):
- self.fetcher_mode = [self.fetcher_mode]
- assert len(self.paths) == len(self.fetcher_mode)
+ fetcher_mode = opt_get(hparams, ['fetcher_mode'], 'lj')
+ if not isinstance(fetcher_mode, list):
+ fetcher_mode = [fetcher_mode]
+ assert len(self.path) == len(fetcher_mode)
self.load_conditioning = opt_get(hparams, ['load_conditioning'], False)
self.conditioning_candidates = opt_get(hparams, ['num_conditioning_candidates'], 1)
@@ -77,8 +85,25 @@ class TextWavLoader(torch.utils.data.Dataset):
self.debug_failures = opt_get(hparams, ['debug_loading_failures'], False)
self.load_aligned_codes = opt_get(hparams, ['load_aligned_codes'], False)
self.aligned_codes_to_audio_ratio = opt_get(hparams, ['aligned_codes_ratio'], 443)
+ self.audiopaths_and_text = []
+ for p, fm in zip(self.path, fetcher_mode):
+ if fm == 'lj' or fm == 'libritts':
+ fetcher_fn = load_filepaths_and_text
+ elif fm == 'tsv':
+ fetcher_fn = load_tsv_aligned_codes if self.load_aligned_codes else load_tsv
+ elif fm == 'mozilla_cv':
+ assert not self.load_conditioning # Conditioning inputs are incompatible with mozilla_cv
+ fetcher_fn = load_mozilla_cv
+ elif fm == 'voxpopuli':
+ assert not self.load_conditioning # Conditioning inputs are incompatible with voxpopuli
+ fetcher_fn = load_voxpopuli
+ else:
+ raise NotImplementedError()
+ self.audiopaths_and_text.extend(fetcher_fn(p))
self.text_cleaners = hparams.text_cleaners
self.sample_rate = hparams.sample_rate
+ random.seed(hparams.seed)
+ random.shuffle(self.audiopaths_and_text)
self.max_wav_len = opt_get(hparams, ['max_wav_length'], None)
if self.max_wav_len is not None:
self.max_aligned_codes = self.max_wav_len // self.aligned_codes_to_audio_ratio
@@ -109,64 +134,23 @@ class TextWavLoader(torch.utils.data.Dataset):
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 = self.paths[i]
- fm = self.fetcher_mode[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)
- if fm == 'lj' or fm == 'libritts':
- return parse_libri(l2, base_path)
- elif fm == 'tsv':
- return parse_tsv_aligned_codes(l2, base_path) if self.load_aligned_codes else parse_tsv(l2, base_path)
- elif fm == 'mozilla_cv':
- assert not self.load_conditioning # Conditioning inputs are incompatible with mozilla_cv
- return parse_mozilla_cv(l2, base_path)
- elif fm == 'voxpopuli':
- assert not self.load_conditioning # Conditioning inputs are incompatible with voxpopuli
- return parse_voxpopuli(l2, base_path)
- else:
- raise NotImplementedError()
- 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 __getitem__(self, index):
self.skipped_items += 1
- apt = self.load_random_line()
try:
- tseq, wav, text, path = self.get_wav_text_pair(apt)
+ tseq, wav, text, path = self.get_wav_text_pair(self.audiopaths_and_text[index])
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,
+ cond, cond_is_self = load_similar_clips(self.audiopaths_and_text[index][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()}")
+ print(f"error loading {self.audiopaths_and_text[index][0]} {sys.exc_info()}")
return self[(index+1) % len(self)]
if self.load_aligned_codes:
- aligned_codes = apt[2]
+ aligned_codes = self.audiopaths_and_text[index][2]
actually_skipped_items = self.skipped_items
self.skipped_items = 0
@@ -205,7 +189,7 @@ class TextWavLoader(torch.utils.data.Dataset):
return res
def __len__(self):
- 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.
+ return len(self.audiopaths_and_text)
class PairedVoiceDebugger:
@@ -240,23 +224,22 @@ class PairedVoiceDebugger:
if __name__ == '__main__':
- batch_sz = 16
+ batch_sz = 8
params = {
'mode': 'paired_voice_audio',
- #'path': ['Y:\\clips\\books1\\transcribed-w2v.tsv'],
- 'path': ['Y:\\bigasr_dataset\\mozcv\\en\\train.tsv'],
- 'fetcher_mode': ['mozilla_cv'],
+ 'path': ['Y:\\clips\\books1\\transcribed-w2v.tsv'],
+ 'fetcher_mode': ['tsv'],
'phase': 'train',
'n_workers': 0,
'batch_size': batch_sz,
'max_wav_length': 255995,
'max_text_length': 200,
'sample_rate': 22050,
- 'load_conditioning': False,
+ 'load_conditioning': True,
'num_conditioning_candidates': 2,
'conditioning_length': 44000,
'use_bpe_tokenizer': True,
- 'load_aligned_codes': False,
+ 'load_aligned_codes': True,
}
from data import create_dataset, create_dataloader
@@ -273,7 +256,7 @@ if __name__ == '__main__':
for i, b in tqdm(enumerate(dl)):
for ib in range(batch_sz):
print(f'{i} {ib} {b["real_text"][ib]}')
- #save(b, i, ib, 'wav')
- #if i > 5:
- # break
+ save(b, i, ib, 'wav')
+ if i > 5:
+ break
diff --git a/codes/train.py b/codes/train.py
index 0e6e6699..d249b3cd 100644
--- a/codes/train.py
+++ b/codes/train.py
@@ -300,7 +300,7 @@ class Trainer:
if __name__ == '__main__':
parser = argparse.ArgumentParser()
- 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')
+ parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_diffusion_tts.yml')
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
parser.add_argument('--local_rank', type=int, default=0)
args = parser.parse_args()