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Running on Zero
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cc348e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | import os
import random
import warnings
import soundfile as sf
import torch
from numpy import trim_zeros
from speechbrain.pretrained import EncoderClassifier
from torch.multiprocessing import Manager
from torch.multiprocessing import Process
from torch.utils.data import Dataset
from tqdm import tqdm
from Preprocessing.AudioPreprocessor import AudioPreprocessor
from Preprocessing.TextFrontend import ArticulatoryCombinedTextFrontend
class AlignerDataset(Dataset):
def __init__(self,
path_to_transcript_dict,
cache_dir,
lang,
loading_processes=8, # careful with the amount of processes if you use silence removal, only as many processes as you have cores
min_len_in_seconds=1,
max_len_in_seconds=20,
cut_silences=True,
rebuild_cache=False,
verbose=False,
device="cpu",
phone_input=False):
os.makedirs(cache_dir, exist_ok=True)
if not os.path.exists(os.path.join(cache_dir, "aligner_train_cache.pt")) or rebuild_cache:
if cut_silences:
torch.set_num_threads(1)
torch.hub.load(repo_or_dir='snakers4/silero-vad',
model='silero_vad',
force_reload=False,
onnx=False,
verbose=False) # download and cache for it to be loaded and used later
torch.set_grad_enabled(True)
resource_manager = Manager()
self.path_to_transcript_dict = resource_manager.dict(path_to_transcript_dict)
key_list = list(self.path_to_transcript_dict.keys())
with open(os.path.join(cache_dir, "files_used.txt"), encoding='utf8', mode="w") as files_used_note:
files_used_note.write(str(key_list))
random.shuffle(key_list)
# build cache
print("... building dataset cache ...")
self.datapoints = resource_manager.list()
# make processes
key_splits = list()
process_list = list()
for i in range(loading_processes):
key_splits.append(key_list[i * len(key_list) // loading_processes:(i + 1) * len(key_list) // loading_processes])
for key_split in key_splits:
process_list.append(
Process(target=self.cache_builder_process,
args=(key_split,
lang,
min_len_in_seconds,
max_len_in_seconds,
cut_silences,
verbose,
"cpu",
phone_input),
daemon=True))
process_list[-1].start()
for process in process_list:
process.join()
self.datapoints = list(self.datapoints)
tensored_datapoints = list()
# we had to turn all of the tensors to numpy arrays to avoid shared memory
# issues. Now that the multi-processing is over, we can convert them back
# to tensors to save on conversions in the future.
print("Converting into convenient format...")
norm_waves = list()
filepaths = list()
for datapoint in tqdm(self.datapoints):
tensored_datapoints.append([torch.Tensor(datapoint[0]),
torch.LongTensor(datapoint[1]),
torch.Tensor(datapoint[2]),
torch.LongTensor(datapoint[3])])
norm_waves.append(torch.Tensor(datapoint[-2]))
filepaths.append(datapoint[-1])
self.datapoints = tensored_datapoints
# add speaker embeddings
self.speaker_embeddings = list()
speaker_embedding_func_ecapa = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb",
run_opts={"device": str(device)},
savedir="Models/SpeakerEmbedding/speechbrain_speaker_embedding_ecapa")
with torch.no_grad():
for wave in tqdm(norm_waves):
self.speaker_embeddings.append(speaker_embedding_func_ecapa.encode_batch(wavs=wave.to(device).unsqueeze(0)).squeeze().cpu())
# save to cache
torch.save((self.datapoints, norm_waves, self.speaker_embeddings, filepaths), os.path.join(cache_dir, "aligner_train_cache.pt"))
else:
# just load the datapoints from cache
self.datapoints = torch.load(os.path.join(cache_dir, "aligner_train_cache.pt"), map_location='cpu')
self.speaker_embeddings = self.datapoints[2]
self.datapoints = self.datapoints[0]
self.tf = ArticulatoryCombinedTextFrontend(language=lang)
print(f"Prepared an Aligner dataset with {len(self.datapoints)} datapoints in {cache_dir}.")
def cache_builder_process(self,
path_list,
lang,
min_len,
max_len,
cut_silences,
verbose,
device,
phone_input):
process_internal_dataset_chunk = list()
tf = ArticulatoryCombinedTextFrontend(language=lang)
_, sr = sf.read(path_list[0])
ap = AudioPreprocessor(input_sr=sr, output_sr=16000, melspec_buckets=80, hop_length=256, n_fft=1024, cut_silence=cut_silences, device=device)
for path in tqdm(path_list):
if self.path_to_transcript_dict[path].strip() == "":
continue
wave, sr = sf.read(path)
dur_in_seconds = len(wave) / sr
if not (min_len <= dur_in_seconds <= max_len):
if verbose:
print(f"Excluding {path} because of its duration of {round(dur_in_seconds, 2)} seconds.")
continue
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore") # otherwise we get tons of warnings about an RNN not being in contiguous chunks
norm_wave = ap.audio_to_wave_tensor(normalize=True, audio=wave)
except ValueError:
continue
dur_in_seconds = len(norm_wave) / 16000
if not (min_len <= dur_in_seconds <= max_len):
if verbose:
print(f"Excluding {path} because of its duration of {round(dur_in_seconds, 2)} seconds.")
continue
norm_wave = torch.tensor(trim_zeros(norm_wave.numpy()))
# raw audio preprocessing is done
transcript = self.path_to_transcript_dict[path]
try:
cached_text = tf.string_to_tensor(transcript, handle_missing=False, input_phonemes=phone_input, path_to_wavfile=path).squeeze(0).cpu().numpy()
except KeyError:
tf.string_to_tensor(transcript, handle_missing=True, input_phonemes=phone_input, path_to_wavfile=path).squeeze(0).cpu().numpy()
continue # we skip sentences with unknown symbols
cached_text_len = torch.LongTensor([len(cached_text)]).numpy()
cached_speech = ap.audio_to_mel_spec_tensor(audio=norm_wave, normalize=False, explicit_sampling_rate=16000).transpose(0, 1).cpu().numpy()
cached_speech_len = torch.LongTensor([len(cached_speech)]).numpy()
process_internal_dataset_chunk.append([cached_text,
cached_text_len,
cached_speech,
cached_speech_len,
norm_wave.cpu().detach().numpy(),
path])
self.datapoints += process_internal_dataset_chunk
def __getitem__(self, index):
text_vector = self.datapoints[index][0]
tokens = list()
for vector in text_vector:
if vector[19] == 0: # we don't include word boundaries when performing alignment, since they are not always present in audio.
for phone in self.tf.phone_to_vector:
if vector.numpy().tolist()[11:] == self.tf.phone_to_vector[phone][11:]:
# the first 10 dimensions are for modifiers, so we ignore those when trying to find the phoneme in the ID lookup
tokens.append(self.tf.phone_to_id[phone])
# this is terribly inefficient, but it's fine
break
tokens = torch.LongTensor(tokens)
return tokens, \
torch.LongTensor([len(tokens)]), \
self.datapoints[index][2], \
self.datapoints[index][3], \
self.speaker_embeddings[index]
def __len__(self):
return len(self.datapoints)
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