from nemo.collections.asr.models import EncDecHybridRNNTCTCModel
from dataclasses import dataclass, field
from typing import List, Union
import torch
from nemo.utils import logging
from pathlib import Path
from viterbi_decoding import viterbi_decoding
from nemo.collections.asr.parts.submodules.ctc_decoding import CTCDecodingConfig
BLANK_TOKEN = "<b>"
SPACE_TOKEN = "<space>"
V_NEGATIVE_NUM = -3.4e38
@dataclass
class Token:
text: str = None
text_cased: str = None
s_start: int = None
s_end: int = None
t_start: float = None
t_end: float = None
@dataclass
class Word:
text: str = None
s_start: int = None
s_end: int = None
t_start: float = None
t_end: float = None
tokens: List[Token] = field(default_factory=list)
@dataclass
class Segment:
text: str = None
s_start: int = None
s_end: int = None
t_start: float = None
t_end: float = None
words_and_tokens: List[Union[Word, Token]] = field(default_factory=list)
@dataclass
class Utterance:
token_ids_with_blanks: List[int] = field(default_factory=list)
segments_and_tokens: List[Union[Segment, Token]] = field(default_factory=list)
text: str = None
pred_text: str = None
audio_filepath: str = None
utt_id: str = None
saved_output_files: dict = field(default_factory=dict)
def is_sub_or_superscript_pair(ref_text, text):
"""returns True if ref_text is a subscript or superscript version of text"""
sub_or_superscript_to_num = {
"⁰": "0",
"¹": "1",
"²": "2",
"³": "3",
"⁴": "4",
"⁵": "5",
"⁶": "6",
"⁷": "7",
"⁸": "8",
"⁹": "9",
"₀": "0",
"₁": "1",
"₂": "2",
"₃": "3",
"₄": "4",
"₅": "5",
"₆": "6",
"₇": "7",
"₈": "8",
"₉": "9",
}
if text in sub_or_superscript_to_num:
if sub_or_superscript_to_num[text] == ref_text:
return True
return False
def restore_token_case(word, word_tokens):
# remove repeated "▁" and "_" from word as that is what the tokenizer will do
while "▁▁" in word:
word = word.replace("▁▁", "▁")
while "__" in word:
word = word.replace("__", "_")
word_tokens_cased = []
word_char_pointer = 0
for token in word_tokens:
token_cased = ""
for token_char in token:
if token_char == word[word_char_pointer]:
token_cased += token_char
word_char_pointer += 1
else:
if token_char.upper() == word[word_char_pointer] or is_sub_or_superscript_pair(
token_char, word[word_char_pointer]
):
token_cased += token_char.upper()
word_char_pointer += 1
else:
if token_char == "▁" or token_char == "_":
if word[word_char_pointer] == "▁" or word[word_char_pointer] == "_":
token_cased += token_char
word_char_pointer += 1
elif word_char_pointer == 0:
token_cased += token_char
else:
raise RuntimeError(
f"Unexpected error - failed to recover capitalization of tokens for word {word}"
)
word_tokens_cased.append(token_cased)
return word_tokens_cased
def get_utt_obj(
text, model, separator, T, audio_filepath, utt_id,
):
"""
Function to create an Utterance object and add all necessary information to it except
for timings of the segments / words / tokens according to the alignment - that will
be done later in a different function, after the alignment is done.
The Utterance object has a list segments_and_tokens which contains Segment objects and
Token objects (for blank tokens in between segments).
Within the Segment objects, there is a list words_and_tokens which contains Word objects and
Token objects (for blank tokens in between words).
Within the Word objects, there is a list tokens tokens which contains Token objects for
blank and non-blank tokens.
We will be building up these lists in this function. This data structure will then be useful for
generating the various output files that we wish to save.
"""
if not separator: # if separator is not defined - treat the whole text as one segment
segments = [text]
else:
segments = text.split(separator)
# remove any spaces at start and end of segments
segments = [seg.strip() for seg in segments]
# remove any empty segments
segments = [seg for seg in segments if len(seg) > 0]
utt = Utterance(text=text, audio_filepath=audio_filepath, utt_id=utt_id,)
# build up lists: token_ids_with_blanks, segments_and_tokens.
# The code for these is different depending on whether we use char-based tokens or not
if hasattr(model, 'tokenizer'):
if hasattr(model, 'blank_id'):
BLANK_ID = model.blank_id
else:
BLANK_ID = len(model.tokenizer.vocab) # TODO: check
utt.token_ids_with_blanks = [BLANK_ID]
# check for text being 0 length
if len(text) == 0:
return utt
# check for # tokens + token repetitions being > T
all_tokens = model.tokenizer.text_to_ids(text)
n_token_repetitions = 0
for i_tok in range(1, len(all_tokens)):
if all_tokens[i_tok] == all_tokens[i_tok - 1]:
n_token_repetitions += 1
if len(all_tokens) + n_token_repetitions > T:
logging.info(
f"Utterance {utt_id} has too many tokens compared to the audio file duration."
" Will not generate output alignment files for this utterance."
)
return utt
# build up data structures containing segments/words/tokens
utt.segments_and_tokens.append(Token(text=BLANK_TOKEN, text_cased=BLANK_TOKEN, s_start=0, s_end=0,))
segment_s_pointer = 1 # first segment will start at s=1 because s=0 is a blank
word_s_pointer = 1 # first word will start at s=1 because s=0 is a blank
for segment in segments:
# add the segment to segment_info and increment the segment_s_pointer
segment_tokens = model.tokenizer.text_to_tokens(segment)
utt.segments_and_tokens.append(
Segment(
text=segment,
s_start=segment_s_pointer,
# segment_tokens do not contain blanks => need to muliply by 2
# s_end needs to be the index of the final token (including blanks) of the current segment:
# segment_s_pointer + len(segment_tokens) * 2 is the index of the first token of the next segment =>
# => need to subtract 2
s_end=segment_s_pointer + len(segment_tokens) * 2 - 2,
)
)
segment_s_pointer += (
len(segment_tokens) * 2
) # multiply by 2 to account for blanks (which are not present in segment_tokens)
words = segment.split(" ") # we define words to be space-separated sub-strings
for word_i, word in enumerate(words):
word_tokens = model.tokenizer.text_to_tokens(word)
word_token_ids = model.tokenizer.text_to_ids(word)
word_tokens_cased = restore_token_case(word, word_tokens)
# add the word to word_info and increment the word_s_pointer
utt.segments_and_tokens[-1].words_and_tokens.append(
# word_tokens do not contain blanks => need to muliply by 2
# s_end needs to be the index of the final token (including blanks) of the current word:
# word_s_pointer + len(word_tokens) * 2 is the index of the first token of the next word =>
# => need to subtract 2
Word(text=word, s_start=word_s_pointer, s_end=word_s_pointer + len(word_tokens) * 2 - 2)
)
word_s_pointer += (
len(word_tokens) * 2
) # multiply by 2 to account for blanks (which are not present in word_tokens)
for token_i, (token, token_id, token_cased) in enumerate(
zip(word_tokens, word_token_ids, word_tokens_cased)
):
# add the text tokens and the blanks in between them
# to our token-based variables
utt.token_ids_with_blanks.extend([token_id, BLANK_ID])
# adding Token object for non-blank token
utt.segments_and_tokens[-1].words_and_tokens[-1].tokens.append(
Token(
text=token,
text_cased=token_cased,
# utt.token_ids_with_blanks has the form [...., <this non-blank token>, <blank>] =>
# => if do len(utt.token_ids_with_blanks) - 1 you get the index of the final <blank>
# => we want to do len(utt.token_ids_with_blanks) - 2 to get the index of <this non-blank token>
s_start=len(utt.token_ids_with_blanks) - 2,
# s_end is same as s_start since the token only occupies one element in the list
s_end=len(utt.token_ids_with_blanks) - 2,
)
)
# adding Token object for blank tokens in between the tokens of the word
# (ie do not add another blank if you have reached the end)
if token_i < len(word_tokens) - 1:
utt.segments_and_tokens[-1].words_and_tokens[-1].tokens.append(
Token(
text=BLANK_TOKEN,
text_cased=BLANK_TOKEN,
# utt.token_ids_with_blanks has the form [...., <this blank token>] =>
# => if do len(utt.token_ids_with_blanks) -1 you get the index of this <blank>
s_start=len(utt.token_ids_with_blanks) - 1,
# s_end is same as s_start since the token only occupies one element in the list
s_end=len(utt.token_ids_with_blanks) - 1,
)
)
# add a Token object for blanks in between words in this segment
# (but only *in between* - do not add the token if it is after the final word)
if word_i < len(words) - 1:
utt.segments_and_tokens[-1].words_and_tokens.append(
Token(
text=BLANK_TOKEN,
text_cased=BLANK_TOKEN,
# utt.token_ids_with_blanks has the form [...., <this blank token>] =>
# => if do len(utt.token_ids_with_blanks) -1 you get the index of this <blank>
s_start=len(utt.token_ids_with_blanks) - 1,
# s_end is same as s_start since the token only occupies one element in the list
s_end=len(utt.token_ids_with_blanks) - 1,
)
)
# add the blank token in between segments/after the final segment
utt.segments_and_tokens.append(
Token(
text=BLANK_TOKEN,
text_cased=BLANK_TOKEN,
# utt.token_ids_with_blanks has the form [...., <this blank token>] =>
# => if do len(utt.token_ids_with_blanks) -1 you get the index of this <blank>
s_start=len(utt.token_ids_with_blanks) - 1,
# s_end is same as s_start since the token only occupies one element in the list
s_end=len(utt.token_ids_with_blanks) - 1,
)
)
return utt
def _get_utt_id(audio_filepath, audio_filepath_parts_in_utt_id):
fp_parts = Path(audio_filepath).parts[-audio_filepath_parts_in_utt_id:]
utt_id = Path("_".join(fp_parts)).stem
utt_id = utt_id.replace(" ", "-") # replace any spaces in the filepath with dashes
return utt_id
def add_t_start_end_to_utt_obj(utt_obj, alignment_utt, output_timestep_duration):
"""
Function to add t_start and t_end (representing time in seconds) to the Utterance object utt_obj.
Args:
utt_obj: Utterance object to which we will add t_start and t_end for its
constituent segments/words/tokens.
alignment_utt: a list of ints indicating which token does the alignment pass through at each
timestep (will take the form [0, 0, 1, 1, ..., <num of tokens including blanks in uterance>]).
output_timestep_duration: a float indicating the duration of a single output timestep from
the ASR Model.
Returns:
utt_obj: updated Utterance object.
"""
# General idea for the algorithm of how we add t_start and t_end
# the timestep where a token s starts is the location of the first appearance of s_start in alignment_utt
# the timestep where a token s ends is the location of the final appearance of s_end in alignment_utt
# We will make dictionaries num_to_first_alignment_appearance and
# num_to_last_appearance and use that to update all of
# the t_start and t_end values in utt_obj.
# We will put t_start = t_end = -1 for tokens that are skipped (should only be blanks)
num_to_first_alignment_appearance = dict()
num_to_last_alignment_appearance = dict()
prev_s = -1 # use prev_s to keep track of when the s changes
for t, s in enumerate(alignment_utt):
if s > prev_s:
num_to_first_alignment_appearance[s] = t
if prev_s >= 0: # dont record prev_s = -1
num_to_last_alignment_appearance[prev_s] = t - 1
prev_s = s
# add last appearance of the final s
num_to_last_alignment_appearance[prev_s] = len(alignment_utt) - 1
# update all the t_start and t_end in utt_obj
for segment_or_token in utt_obj.segments_and_tokens:
if type(segment_or_token) is Segment:
segment = segment_or_token
segment.t_start = num_to_first_alignment_appearance[segment.s_start] * output_timestep_duration
segment.t_end = (num_to_last_alignment_appearance[segment.s_end] + 1) * output_timestep_duration
for word_or_token in segment.words_and_tokens:
if type(word_or_token) is Word:
word = word_or_token
word.t_start = num_to_first_alignment_appearance[word.s_start] * output_timestep_duration
word.t_end = (num_to_last_alignment_appearance[word.s_end] + 1) * output_timestep_duration
for token in word.tokens:
if token.s_start in num_to_first_alignment_appearance:
token.t_start = num_to_first_alignment_appearance[token.s_start] * output_timestep_duration
else:
token.t_start = -1
if token.s_end in num_to_last_alignment_appearance:
token.t_end = (
num_to_last_alignment_appearance[token.s_end] + 1
) * output_timestep_duration
else:
token.t_end = -1
else:
token = word_or_token
if token.s_start in num_to_first_alignment_appearance:
token.t_start = num_to_first_alignment_appearance[token.s_start] * output_timestep_duration
else:
token.t_start = -1
if token.s_end in num_to_last_alignment_appearance:
token.t_end = (num_to_last_alignment_appearance[token.s_end] + 1) * output_timestep_duration
else:
token.t_end = -1
else:
token = segment_or_token
if token.s_start in num_to_first_alignment_appearance:
token.t_start = num_to_first_alignment_appearance[token.s_start] * output_timestep_duration
else:
token.t_start = -1
if token.s_end in num_to_last_alignment_appearance:
token.t_end = (num_to_last_alignment_appearance[token.s_end] + 1) * output_timestep_duration
else:
token.t_end = -1
return utt_obj
def get_word_timings(
alignment_level, utt_obj,
):
boundary_info_utt = []
for segment_or_token in utt_obj.segments_and_tokens:
if type(segment_or_token) is Segment:
segment = segment_or_token
for word_or_token in segment.words_and_tokens:
if type(word_or_token) is Word:
word = word_or_token
if alignment_level == "words":
boundary_info_utt.append(word)
word_timestamps=[]
for boundary_info_ in boundary_info_utt: # loop over every token/word/segment
# skip if t_start = t_end = negative number because we used it as a marker to skip some blank tokens
if not (boundary_info_.t_start < 0 or boundary_info_.t_end < 0):
text = boundary_info_.text
start_time = boundary_info_.t_start
end_time = boundary_info_.t_end
text = text.replace(" ", SPACE_TOKEN)
word_timestamps.append((text, start_time, end_time))
return word_timestamps
def get_start_end_for_segments(word_timestamps):
segment_timestamps=[]
word_list = []
beginning = None
for word, start, end in word_timestamps:
if beginning is None:
beginning = start
word = word.capitalize()
word_list.append(word)
if word.endswith('.') or word.endswith('?') or word.endswith('!'):
segment = ' '.join(word_list)
segment_timestamps.append((segment, beginning, end))
beginning = None
word_list = []
segment = ' '.join(word_list)
segment_timestamps.append((segment, beginning, end))
return segment_timestamps
def align_tdt_to_ctc_timestamps(tdt_txt, model, audio_filepath):
tdt_txt = tdt_txt[0][0] if tdt_txt is not None else tdt_txt
if isinstance(model, EncDecHybridRNNTCTCModel):
ctc_cfg = CTCDecodingConfig()
ctc_cfg.decoding = "greedy_batch"
model.change_decoding_strategy(decoding_cfg=ctc_cfg, decoder_type="ctc")
else:
raise ValueError("Currently supporting hybrid models")
if torch.cuda.is_available():
viterbi_device = torch.device('cuda')
else:
viterbi_device = torch.device('cpu')
with torch.cuda.amp.autocast(enabled=False, dtype=torch.bfloat16):
with torch.inference_mode():
hypotheses = model.transcribe([audio_filepath], return_hypotheses=True, batch_size=1)
if type(hypotheses) == tuple and len(hypotheses) == 2:
hypotheses = hypotheses[0]
log_probs_list_batch = [hypotheses[0].y_sequence]
T_list_batch = [hypotheses[0].y_sequence.shape[0]]
ctc_pred_text = hypotheses[0].text if tdt_txt is None else tdt_txt
utt_obj = get_utt_obj(
ctc_pred_text,
model,
None,
T_list_batch[0],
audio_filepath,
_get_utt_id(audio_filepath, 1),
)
utt_obj.pred_text = ctc_pred_text
y_list_batch = [utt_obj.token_ids_with_blanks]
U_list_batch = [len(utt_obj.token_ids_with_blanks)]
if hasattr(model, 'tokenizer'):
V = len(model.tokenizer.vocab) + 1
else:
V = len(model.decoder.vocabulary) + 1
# turn log_probs, y, T, U into dense tensors for fast computation during Viterbi decoding
T_max = max(T_list_batch)
U_max = max(U_list_batch)
# V = the number of tokens in the vocabulary + 1 for the blank token.
if hasattr(model, 'tokenizer'):
V = len(model.tokenizer.vocab) + 1
else:
V = len(model.decoder.vocabulary) + 1
T_batch = torch.tensor(T_list_batch)
U_batch = torch.tensor(U_list_batch)
# make log_probs_batch tensor of shape (B x T_max x V)
log_probs_batch = V_NEGATIVE_NUM * torch.ones((1, T_max, V))
for b, log_probs_utt in enumerate(log_probs_list_batch):
t = log_probs_utt.shape[0]
log_probs_batch[b, :t, :] = log_probs_utt
y_batch = V * torch.ones((1, U_max), dtype=torch.int64)
for b, y_utt in enumerate(y_list_batch):
U_utt = U_batch[b]
y_batch[b, :U_utt] = torch.tensor(y_utt)
model_downsample_factor = 8
output_timestep_duration = (
model.preprocessor.featurizer.hop_length * model_downsample_factor / model.cfg.preprocessor.sample_rate
)
alignments_batch = viterbi_decoding(log_probs_batch, y_batch, T_batch, U_batch, viterbi_device)
utt_obj = add_t_start_end_to_utt_obj(utt_obj, alignments_batch[0], output_timestep_duration)
word_timestamps = get_word_timings("words", utt_obj=utt_obj)
segmet_timestamps = get_start_end_for_segments(word_timestamps)
return segmet_timestamps
# def main():
# # model = 'nvidia/parakeet-tdt_ctc-1.1b.nemo'
# # from nemo.collections.asr.models import ASRModel
# # asr_model = ASRModel.from_pretrained(model).to('cuda')
# # asr_model.eval()
# # Segments = align_tdt_to_ctc_timestamps(None, asr_model, 'processed_file.flac')
# if __name__ == '__main__':
# main()