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256-bpe tokenizer

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This commit is contained in:
James Betker 2021-12-25 08:52:08 -07:00
parent 8e26400ce2
commit 52410fd9d9
3 changed files with 12 additions and 18 deletions
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2
codes/data/audio/paired_voice_audio_dataset.py
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@ -86,7 +86,7 @@ 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_r2.json'))
self.tokenizer = Tokenizer.from_file(opt_get(hparams, ['tokenizer_vocab'], '../experiments/bpe_lowercase_asr_256.json'))
def get_wav_text_pair(self, audiopath_and_text):
# separate filename and text

12
codes/data/audio/voice_tokenizer_builder.py
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@ -33,7 +33,7 @@ def build_text_file_from_priors(priors, output):
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']
#bcd = datasets.load_dataset('bookcorpus', cache_dir='Z:\\huggingface_datasets\\cache')['train']
allowed_characters_re = re.compile(r'^[0-9a-z!@#%_=:;"/, \-\$\^&\*\(\)\+\{\[\]\}\\\.\'\?—–ʼ]+$')
def preprocess_word(word, report=False):
@ -49,14 +49,14 @@ def train():
for i in range(0, len(ttsd), batch_size):
yield [preprocess_word(t, True) for t in ttsd[i:i+batch_size]]
print("Processing bookcorpus.")
for i in range(0, len(bcd), batch_size):
yield [preprocess_word(t) for t in bcd[i:i+batch_size]['text']]
#print("Processing bookcorpus.")
#for i in range(0, len(bcd), batch_size):
# yield [preprocess_word(t) for t in bcd[i:i+batch_size]['text']]
trainer = BpeTrainer(special_tokens=['[STOP]', '[UNK]'], vocab_size=9999, continuing_subword_prefix='$$$')
trainer = BpeTrainer(special_tokens=['[STOP]', '[UNK]'], vocab_size=511, continuing_subword_prefix='$$$')
tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
tokenizer.pre_tokenizer = Whitespace()
tokenizer.train_from_iterator(batch_iterator(), trainer, length=len(ttsd)+len(bcd))
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))

16
codes/models/gpt_voice/unified_voice.py
View File

@ -1,16 +1,10 @@
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 transformers import GPT2Model, GPT2Config
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
@ -47,14 +41,14 @@ class UnifiedGptVoice(nn.Module):
- Voice conditioned on text
"""
NUMBER_TEXT_TOKENS = 10000 # The number of tokens produced by our bespoke BPE tokenizer.
START_TEXT_TOKEN = 9999
NUMBER_TEXT_TOKENS = 256 # The number of tokens produced by our bespoke BPE tokenizer.
START_TEXT_TOKEN = 255
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,
def __init__(self, layers=8, model_dim=512, heads=8, max_symbols_per_phrase=120, max_mel_tokens=250, max_conditioning_inputs=3,
checkpointing=True, mel_length_compression=1024, max_conditioning_length=60):
super().__init__()
@ -222,7 +216,7 @@ def register_unified_gpt_voice(opt_net, opt):
if __name__ == '__main__':
gpt = UnifiedGptVoice(model_dim=256, heads=4)
l = gpt(torch.randn(2, 80, 800),
l = gpt(torch.randn(2, 120, 800),
torch.randint(high=len(symbols), size=(2,80)),
torch.randint(high=8192, size=(2,250)),
torch.tensor([150*256,195*256]))