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: 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)