Spaces:
Sleeping
Sleeping
File size: 6,376 Bytes
d9f82df |
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 |
import re
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2FeatureExtractor, Wav2Vec2CTCTokenizer, Wav2Vec2Processor
from tortoise.utils.audio import load_audio
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='cuda' if not torch.backends.mps.is_available() else 'mps'):
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():
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
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)
|