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alignment.py
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alignment.py
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""""
Forced Alignment with Whisper
C. Max Bain
"""
from dataclasses import dataclass
from typing import Iterable, Union, List
import numpy as np
import pandas as pd
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
from .audio import SAMPLE_RATE, load_audio
from .utils import interpolate_nans
from .types import AlignedTranscriptionResult, SingleSegment, SingleAlignedSegment, SingleWordSegment
import nltk
from nltk.tokenize.punkt import PunktSentenceTokenizer, PunktParameters
PUNKT_ABBREVIATIONS = ['dr', 'vs', 'mr', 'mrs', 'prof']
LANGUAGES_WITHOUT_SPACES = ["ja", "zh"]
DEFAULT_ALIGN_MODELS_TORCH = {
"en": "WAV2VEC2_ASR_BASE_960H",
"fr": "VOXPOPULI_ASR_BASE_10K_FR",
"de": "VOXPOPULI_ASR_BASE_10K_DE",
"es": "VOXPOPULI_ASR_BASE_10K_ES",
"it": "VOXPOPULI_ASR_BASE_10K_IT",
}
DEFAULT_ALIGN_MODELS_HF = {
"ja": "jonatasgrosman/wav2vec2-large-xlsr-53-japanese",
"zh": "jonatasgrosman/wav2vec2-large-xlsr-53-chinese-zh-cn",
"nl": "jonatasgrosman/wav2vec2-large-xlsr-53-dutch",
"uk": "Yehor/wav2vec2-xls-r-300m-uk-with-small-lm",
"pt": "jonatasgrosman/wav2vec2-large-xlsr-53-portuguese",
"ar": "jonatasgrosman/wav2vec2-large-xlsr-53-arabic",
"cs": "comodoro/wav2vec2-xls-r-300m-cs-250",
"ru": "jonatasgrosman/wav2vec2-large-xlsr-53-russian",
"pl": "jonatasgrosman/wav2vec2-large-xlsr-53-polish",
"hu": "jonatasgrosman/wav2vec2-large-xlsr-53-hungarian",
"fi": "jonatasgrosman/wav2vec2-large-xlsr-53-finnish",
"fa": "jonatasgrosman/wav2vec2-large-xlsr-53-persian",
"el": "jonatasgrosman/wav2vec2-large-xlsr-53-greek",
"tr": "mpoyraz/wav2vec2-xls-r-300m-cv7-turkish",
"da": "saattrupdan/wav2vec2-xls-r-300m-ftspeech",
"he": "imvladikon/wav2vec2-xls-r-300m-hebrew",
"vi": 'nguyenvulebinh/wav2vec2-base-vi',
"ko": "kresnik/wav2vec2-large-xlsr-korean",
"ur": "kingabzpro/wav2vec2-large-xls-r-300m-Urdu",
"te": "anuragshas/wav2vec2-large-xlsr-53-telugu",
"hi": "theainerd/Wav2Vec2-large-xlsr-hindi",
"ca": "softcatala/wav2vec2-large-xlsr-catala",
"ml": "gvs/wav2vec2-large-xlsr-malayalam",
"no": "NbAiLab/nb-wav2vec2-1b-bokmaal",
"nn": "NbAiLab/nb-wav2vec2-300m-nynorsk",
}
def load_align_model(language_code, device, model_name=None, model_dir=None):
if model_name is None:
# use default model
if language_code in DEFAULT_ALIGN_MODELS_TORCH:
model_name = DEFAULT_ALIGN_MODELS_TORCH[language_code]
elif language_code in DEFAULT_ALIGN_MODELS_HF:
model_name = DEFAULT_ALIGN_MODELS_HF[language_code]
else:
print(f"There is no default alignment model set for this language ({language_code}).\
Please find a wav2vec2.0 model finetuned on this language in https://huggingface.co/models, then pass the model name in --align_model [MODEL_NAME]")
raise ValueError(f"No default align-model for language: {language_code}")
if model_name in torchaudio.pipelines.__all__:
pipeline_type = "torchaudio"
bundle = torchaudio.pipelines.__dict__[model_name]
align_model = bundle.get_model(dl_kwargs={"model_dir": model_dir}).to(device)
labels = bundle.get_labels()
align_dictionary = {c.lower(): i for i, c in enumerate(labels)}
else:
try:
processor = Wav2Vec2Processor.from_pretrained(model_name)
align_model = Wav2Vec2ForCTC.from_pretrained(model_name)
except Exception as e:
print(e)
print(f"Error loading model from huggingface, check https://huggingface.co/models for finetuned wav2vec2.0 models")
raise ValueError(f'The chosen align_model "{model_name}" could not be found in huggingface (https://huggingface.co/models) or torchaudio (https://pytorch.org/audio/stable/pipelines.html#id14)')
pipeline_type = "huggingface"
align_model = align_model.to(device)
labels = processor.tokenizer.get_vocab()
align_dictionary = {char.lower(): code for char,code in processor.tokenizer.get_vocab().items()}
align_metadata = {"language": language_code, "dictionary": align_dictionary, "type": pipeline_type}
return align_model, align_metadata
def align(
transcript: Iterable[SingleSegment],
model: torch.nn.Module,
align_model_metadata: dict,
audio: Union[str, np.ndarray, torch.Tensor],
device: str,
interpolate_method: str = "nearest",
return_char_alignments: bool = False,
print_progress: bool = False,
combined_progress: bool = False,
) -> AlignedTranscriptionResult:
"""
Align phoneme recognition predictions to known transcription.
"""
if not torch.is_tensor(audio):
if isinstance(audio, str):
audio = load_audio(audio)
audio = torch.from_numpy(audio)
if len(audio.shape) == 1:
audio = audio.unsqueeze(0)
MAX_DURATION = audio.shape[1] / SAMPLE_RATE
model_dictionary = align_model_metadata["dictionary"]
model_lang = align_model_metadata["language"]
model_type = align_model_metadata["type"]
# 1. Preprocess to keep only characters in dictionary
total_segments = len(transcript)
for sdx, segment in enumerate(transcript):
# strip spaces at beginning / end, but keep track of the amount.
if print_progress:
base_progress = ((sdx + 1) / total_segments) * 100
percent_complete = (50 + base_progress / 2) if combined_progress else base_progress
print(f"Progress: {percent_complete:.2f}%...")
num_leading = len(segment["text"]) - len(segment["text"].lstrip())
num_trailing = len(segment["text"]) - len(segment["text"].rstrip())
text = segment["text"]
# split into words
if model_lang not in LANGUAGES_WITHOUT_SPACES:
per_word = text.split(" ")
else:
per_word = text
clean_char, clean_cdx = [], []
for cdx, char in enumerate(text):
char_ = char.lower()
# wav2vec2 models use "|" character to represent spaces
if model_lang not in LANGUAGES_WITHOUT_SPACES:
char_ = char_.replace(" ", "|")
# ignore whitespace at beginning and end of transcript
if cdx < num_leading:
pass
elif cdx > len(text) - num_trailing - 1:
pass
elif char_ in model_dictionary.keys():
clean_char.append(char_)
clean_cdx.append(cdx)
clean_wdx = []
for wdx, wrd in enumerate(per_word):
if any([c in model_dictionary.keys() for c in wrd]):
clean_wdx.append(wdx)
punkt_param = PunktParameters()
punkt_param.abbrev_types = set(PUNKT_ABBREVIATIONS)
sentence_splitter = PunktSentenceTokenizer(punkt_param)
sentence_spans = list(sentence_splitter.span_tokenize(text))
segment["clean_char"] = clean_char
segment["clean_cdx"] = clean_cdx
segment["clean_wdx"] = clean_wdx
segment["sentence_spans"] = sentence_spans
aligned_segments: List[SingleAlignedSegment] = []
# 2. Get prediction matrix from alignment model & align
for sdx, segment in enumerate(transcript):
t1 = segment["start"]
t2 = segment["end"]
text = segment["text"]
aligned_seg: SingleAlignedSegment = {
"start": t1,
"end": t2,
"text": text,
"words": [],
}
if return_char_alignments:
aligned_seg["chars"] = []
# check we can align
if len(segment["clean_char"]) == 0:
print(f'Failed to align segment ("{segment["text"]}"): no characters in this segment found in model dictionary, resorting to original...')
aligned_segments.append(aligned_seg)
continue
if t1 >= MAX_DURATION:
print(f'Failed to align segment ("{segment["text"]}"): original start time longer than audio duration, skipping...')
aligned_segments.append(aligned_seg)
continue
text_clean = "".join(segment["clean_char"])
tokens = [model_dictionary[c] for c in text_clean]
f1 = int(t1 * SAMPLE_RATE)
f2 = int(t2 * SAMPLE_RATE)
# TODO: Probably can get some speedup gain with batched inference here
waveform_segment = audio[:, f1:f2]
# Handle the minimum input length for wav2vec2 models
if waveform_segment.shape[-1] < 400:
lengths = torch.as_tensor([waveform_segment.shape[-1]]).to(device)
waveform_segment = torch.nn.functional.pad(
waveform_segment, (0, 400 - waveform_segment.shape[-1])
)
else:
lengths = None
with torch.inference_mode():
if model_type == "torchaudio":
emissions, _ = model(waveform_segment.to(device), lengths=lengths)
elif model_type == "huggingface":
emissions = model(waveform_segment.to(device)).logits
else:
raise NotImplementedError(f"Align model of type {model_type} not supported.")
emissions = torch.log_softmax(emissions, dim=-1)
emission = emissions[0].cpu().detach()
blank_id = 0
for char, code in model_dictionary.items():
if char == '[pad]' or char == '<pad>':
blank_id = code
trellis = get_trellis(emission, tokens, blank_id)
path = backtrack(trellis, emission, tokens, blank_id)
if path is None:
print(f'Failed to align segment ("{segment["text"]}"): backtrack failed, resorting to original...')
aligned_segments.append(aligned_seg)
continue
char_segments = merge_repeats(path, text_clean)
duration = t2 -t1
ratio = duration * waveform_segment.size(0) / (trellis.size(0) - 1)
# assign timestamps to aligned characters
char_segments_arr = []
word_idx = 0
for cdx, char in enumerate(text):
start, end, score = None, None, None
if cdx in segment["clean_cdx"]:
char_seg = char_segments[segment["clean_cdx"].index(cdx)]
start = round(char_seg.start * ratio + t1, 3)
end = round(char_seg.end * ratio + t1, 3)
score = round(char_seg.score, 3)
char_segments_arr.append(
{
"char": char,
"start": start,
"end": end,
"score": score,
"word-idx": word_idx,
}
)
# increment word_idx, nltk word tokenization would probably be more robust here, but us space for now...
if model_lang in LANGUAGES_WITHOUT_SPACES:
word_idx += 1
elif cdx == len(text) - 1 or text[cdx+1] == " ":
word_idx += 1
char_segments_arr = pd.DataFrame(char_segments_arr)
aligned_subsegments = []
# assign sentence_idx to each character index
char_segments_arr["sentence-idx"] = None
for sdx, (sstart, send) in enumerate(segment["sentence_spans"]):
curr_chars = char_segments_arr.loc[(char_segments_arr.index >= sstart) & (char_segments_arr.index <= send)]
char_segments_arr.loc[(char_segments_arr.index >= sstart) & (char_segments_arr.index <= send), "sentence-idx"] = sdx
sentence_text = text[sstart:send]
sentence_start = curr_chars["start"].min()
end_chars = curr_chars[curr_chars["char"] != ' ']
sentence_end = end_chars["end"].max()
sentence_words = []
for word_idx in curr_chars["word-idx"].unique():
word_chars = curr_chars.loc[curr_chars["word-idx"] == word_idx]
word_text = "".join(word_chars["char"].tolist()).strip()
if len(word_text) == 0:
continue
# dont use space character for alignment
word_chars = word_chars[word_chars["char"] != " "]
word_start = word_chars["start"].min()
word_end = word_chars["end"].max()
word_score = round(word_chars["score"].mean(), 3)
# -1 indicates unalignable
word_segment = {"word": word_text}
if not np.isnan(word_start):
word_segment["start"] = word_start
if not np.isnan(word_end):
word_segment["end"] = word_end
if not np.isnan(word_score):
word_segment["score"] = word_score
sentence_words.append(word_segment)
aligned_subsegments.append({
"text": sentence_text,
"start": sentence_start,
"end": sentence_end,
"words": sentence_words,
})
if return_char_alignments:
curr_chars = curr_chars[["char", "start", "end", "score"]]
curr_chars.fillna(-1, inplace=True)
curr_chars = curr_chars.to_dict("records")
curr_chars = [{key: val for key, val in char.items() if val != -1} for char in curr_chars]
aligned_subsegments[-1]["chars"] = curr_chars
aligned_subsegments = pd.DataFrame(aligned_subsegments)
aligned_subsegments["start"] = interpolate_nans(aligned_subsegments["start"], method=interpolate_method)
aligned_subsegments["end"] = interpolate_nans(aligned_subsegments["end"], method=interpolate_method)
# concatenate sentences with same timestamps
agg_dict = {"text": " ".join, "words": "sum"}
if model_lang in LANGUAGES_WITHOUT_SPACES:
agg_dict["text"] = "".join
if return_char_alignments:
agg_dict["chars"] = "sum"
aligned_subsegments= aligned_subsegments.groupby(["start", "end"], as_index=False).agg(agg_dict)
aligned_subsegments = aligned_subsegments.to_dict('records')
aligned_segments += aligned_subsegments
# create word_segments list
word_segments: List[SingleWordSegment] = []
for segment in aligned_segments:
word_segments += segment["words"]
return {"segments": aligned_segments, "word_segments": word_segments}
"""
source: https://pytorch.org/tutorials/intermediate/forced_alignment_with_torchaudio_tutorial.html
"""
def get_trellis(emission, tokens, blank_id=0):
num_frame = emission.size(0)
num_tokens = len(tokens)
# Trellis has extra diemsions for both time axis and tokens.
# The extra dim for tokens represents <SoS> (start-of-sentence)
# The extra dim for time axis is for simplification of the code.
trellis = torch.empty((num_frame + 1, num_tokens + 1))
trellis[0, 0] = 0
trellis[1:, 0] = torch.cumsum(emission[:, 0], 0)
trellis[0, -num_tokens:] = -float("inf")
trellis[-num_tokens:, 0] = float("inf")
for t in range(num_frame):
trellis[t + 1, 1:] = torch.maximum(
# Score for staying at the same token
trellis[t, 1:] + emission[t, blank_id],
# Score for changing to the next token
trellis[t, :-1] + emission[t, tokens],
)
return trellis
@dataclass
class Point:
token_index: int
time_index: int
score: float
def backtrack(trellis, emission, tokens, blank_id=0):
# Note:
# j and t are indices for trellis, which has extra dimensions
# for time and tokens at the beginning.
# When referring to time frame index `T` in trellis,
# the corresponding index in emission is `T-1`.
# Similarly, when referring to token index `J` in trellis,
# the corresponding index in transcript is `J-1`.
j = trellis.size(1) - 1
t_start = torch.argmax(trellis[:, j]).item()
path = []
for t in range(t_start, 0, -1):
# 1. Figure out if the current position was stay or change
# Note (again):
# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
# Score for token staying the same from time frame J-1 to T.
stayed = trellis[t - 1, j] + emission[t - 1, blank_id]
# Score for token changing from C-1 at T-1 to J at T.
changed = trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]
# 2. Store the path with frame-wise probability.
prob = emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
# Return token index and time index in non-trellis coordinate.
path.append(Point(j - 1, t - 1, prob))
# 3. Update the token
if changed > stayed:
j -= 1
if j == 0:
break
else:
# failed
return None
return path[::-1]
# Merge the labels
@dataclass
class Segment:
label: str
start: int
end: int
score: float
def __repr__(self):
return f"{self.label}\t({self.score:4.2f}): [{self.start:5d}, {self.end:5d})"
@property
def length(self):
return self.end - self.start
def merge_repeats(path, transcript):
i1, i2 = 0, 0
segments = []
while i1 < len(path):
while i2 < len(path) and path[i1].token_index == path[i2].token_index:
i2 += 1
score = sum(path[k].score for k in range(i1, i2)) / (i2 - i1)
segments.append(
Segment(
transcript[path[i1].token_index],
path[i1].time_index,
path[i2 - 1].time_index + 1,
score,
)
)
i1 = i2
return segments
def merge_words(segments, separator="|"):
words = []
i1, i2 = 0, 0
while i1 < len(segments):
if i2 >= len(segments) or segments[i2].label == separator:
if i1 != i2:
segs = segments[i1:i2]
word = "".join([seg.label for seg in segs])
score = sum(seg.score * seg.length for seg in segs) / sum(seg.length for seg in segs)
words.append(Segment(word, segments[i1].start, segments[i2 - 1].end, score))
i1 = i2 + 1
i2 = i1
else:
i2 += 1
return words