160 lines
6.5 KiB
Python
Executable File
160 lines
6.5 KiB
Python
Executable File
import re
|
|
|
|
import torch
|
|
import torchaudio
|
|
from transformers import Wav2Vec2ForCTC, Wav2Vec2FeatureExtractor, Wav2Vec2CTCTokenizer, Wav2Vec2Processor
|
|
|
|
from tortoise.utils.audio import load_audio
|
|
from tortoise.utils.device import get_device
|
|
|
|
import tortoise.utils.torch_intermediary as ml
|
|
|
|
def max_alignment(s1, s2, skip_character='~', record=None):
|
|
"""
|
|
A clever function that aligns s1 to s2 as best it can. Wherever a character from s1 is not found in s2, a '~' is
|
|
used to replace that character.
|
|
|
|
Finally got to use my DP skills!
|
|
"""
|
|
if record is None:
|
|
record = {}
|
|
assert skip_character not in s1, f"Found the skip character {skip_character} in the provided string, {s1}"
|
|
if len(s1) == 0:
|
|
return ''
|
|
if len(s2) == 0:
|
|
return skip_character * len(s1)
|
|
if s1 == s2:
|
|
return s1
|
|
if s1[0] == s2[0]:
|
|
return s1[0] + max_alignment(s1[1:], s2[1:], skip_character, record)
|
|
|
|
take_s1_key = (len(s1), len(s2) - 1)
|
|
if take_s1_key in record:
|
|
take_s1, take_s1_score = record[take_s1_key]
|
|
else:
|
|
take_s1 = max_alignment(s1, s2[1:], skip_character, record)
|
|
take_s1_score = len(take_s1.replace(skip_character, ''))
|
|
record[take_s1_key] = (take_s1, take_s1_score)
|
|
|
|
take_s2_key = (len(s1) - 1, len(s2))
|
|
if take_s2_key in record:
|
|
take_s2, take_s2_score = record[take_s2_key]
|
|
else:
|
|
take_s2 = max_alignment(s1[1:], s2, skip_character, record)
|
|
take_s2_score = len(take_s2.replace(skip_character, ''))
|
|
record[take_s2_key] = (take_s2, take_s2_score)
|
|
|
|
return take_s1 if take_s1_score > take_s2_score else skip_character + take_s2
|
|
|
|
|
|
class Wav2VecAlignment:
|
|
"""
|
|
Uses wav2vec2 to perform audio<->text alignment.
|
|
"""
|
|
def __init__(self, device=None):
|
|
if device is None:
|
|
device = torch.device(get_device())
|
|
|
|
self.model = Wav2Vec2ForCTC.from_pretrained("jbetker/wav2vec2-large-robust-ft-libritts-voxpopuli").cpu()
|
|
self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(f"facebook/wav2vec2-large-960h")
|
|
self.tokenizer = Wav2Vec2CTCTokenizer.from_pretrained('jbetker/tacotron-symbols')
|
|
self.device = device
|
|
|
|
def align(self, audio, expected_text, audio_sample_rate=24000):
|
|
orig_len = audio.shape[-1]
|
|
|
|
with torch.no_grad():
|
|
if torch.cuda.is_available(): # This is unneccessary technically, but it's a placebo
|
|
self.model = self.model.to(self.device)
|
|
|
|
audio = audio.to(self.device)
|
|
audio = torchaudio.functional.resample(audio, audio_sample_rate, 16000)
|
|
clip_norm = (audio - audio.mean()) / torch.sqrt(audio.var() + 1e-7)
|
|
logits = self.model(clip_norm).logits
|
|
|
|
if torch.cuda.is_available():
|
|
self.model = self.model.cpu()
|
|
|
|
logits = logits[0]
|
|
pred_string = self.tokenizer.decode(logits.argmax(-1).tolist())
|
|
|
|
fixed_expectation = max_alignment(expected_text.lower(), pred_string)
|
|
w2v_compression = orig_len // logits.shape[0]
|
|
expected_tokens = self.tokenizer.encode(fixed_expectation)
|
|
expected_chars = list(fixed_expectation)
|
|
if len(expected_tokens) == 1:
|
|
return [0] # The alignment is simple; there is only one token.
|
|
expected_tokens.pop(0) # The first token is a given.
|
|
expected_chars.pop(0)
|
|
|
|
alignments = [0]
|
|
def pop_till_you_win():
|
|
if len(expected_tokens) == 0:
|
|
return None
|
|
popped = expected_tokens.pop(0)
|
|
popped_char = expected_chars.pop(0)
|
|
while popped_char == '~':
|
|
alignments.append(-1)
|
|
if len(expected_tokens) == 0:
|
|
return None
|
|
popped = expected_tokens.pop(0)
|
|
popped_char = expected_chars.pop(0)
|
|
return popped
|
|
|
|
next_expected_token = pop_till_you_win()
|
|
for i, logit in enumerate(logits):
|
|
top = logit.argmax()
|
|
if next_expected_token == top:
|
|
alignments.append(i * w2v_compression)
|
|
if len(expected_tokens) > 0:
|
|
next_expected_token = pop_till_you_win()
|
|
else:
|
|
break
|
|
|
|
pop_till_you_win()
|
|
if not (len(expected_tokens) == 0 and len(alignments) == len(expected_text)):
|
|
torch.save([audio, expected_text], 'alignment_debug.pth')
|
|
assert False, "Something went wrong with the alignment algorithm. I've dumped a file, 'alignment_debug.pth' to" \
|
|
"your current working directory. Please report this along with the file so it can get fixed."
|
|
|
|
# Now fix up alignments. Anything with -1 should be interpolated.
|
|
alignments.append(orig_len) # This'll get removed but makes the algorithm below more readable.
|
|
for i in range(len(alignments)):
|
|
if alignments[i] == -1:
|
|
for j in range(i+1, len(alignments)):
|
|
if alignments[j] != -1:
|
|
next_found_token = j
|
|
break
|
|
for j in range(i, next_found_token):
|
|
gap = alignments[next_found_token] - alignments[i-1]
|
|
alignments[j] = (j-i+1) * gap // (next_found_token-i+1) + alignments[i-1]
|
|
|
|
return alignments[:-1]
|
|
|
|
def redact(self, audio, expected_text, audio_sample_rate=24000):
|
|
if '[' not in expected_text:
|
|
return audio
|
|
splitted = expected_text.split('[')
|
|
fully_split = [splitted[0]]
|
|
for spl in splitted[1:]:
|
|
assert ']' in spl, 'Every "[" character must be paired with a "]" with no nesting.'
|
|
fully_split.extend(spl.split(']'))
|
|
|
|
# At this point, fully_split is a list of strings, with every other string being something that should be redacted.
|
|
non_redacted_intervals = []
|
|
last_point = 0
|
|
for i in range(len(fully_split)):
|
|
if i % 2 == 0 and fully_split[i] != "": # Check for empty string fixes index error
|
|
end_interval = max(0, last_point + len(fully_split[i]) - 1)
|
|
non_redacted_intervals.append((last_point, end_interval))
|
|
last_point += len(fully_split[i])
|
|
|
|
bare_text = ''.join(fully_split)
|
|
alignments = self.align(audio, bare_text, audio_sample_rate)
|
|
|
|
output_audio = []
|
|
for nri in non_redacted_intervals:
|
|
start, stop = nri
|
|
output_audio.append(audio[:, alignments[start]:alignments[stop]])
|
|
return torch.cat(output_audio, dim=-1)
|