alignment.py

"""
Forced Alignment with Whisper
C. Max Bain
"""
import logging
import math

from dataclasses import dataclass
from typing import Iterable, Optional, Union, List

import numpy as np
import pandas as pd
import torch
import torchaudio
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor

from utils import SAMPLE_RATE, load_audio
from schema import (
    AlignedTranscriptionResult,
    SingleSegment,
    SingleAlignedSegment,
    SingleWordSegment,
    SegmentData,
)
import nltk
from nltk.data import load as nltk_load

logger = logging.getLogger(__name__)

PUNKT_LANGUAGES = {
    'cs': 'czech',
    'da': 'danish',
    'de': 'german',
    'el': 'greek',
    'en': 'english',
    'es': 'spanish',
    'et': 'estonian',
    'fi': 'finnish',
    'fr': 'french',
    'it': 'italian',
    'nl': 'dutch',
    'no': 'norwegian',
    'pl': 'polish',
    'pt': 'portuguese',
    'sl': 'slovene',
    'sv': 'swedish',
    'tr': 'turkish',
    "ml": "malayalam",
    "ru": "russian",
}


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-vlsp2020',
    "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-v2",
    "nn": "NbAiLab/nb-wav2vec2-1b-nynorsk",
    "sk": "comodoro/wav2vec2-xls-r-300m-sk-cv8",
    "sl": "anton-l/wav2vec2-large-xlsr-53-slovenian",
    "hr": "classla/wav2vec2-xls-r-parlaspeech-hr",
    "ro": "gigant/romanian-wav2vec2",
    "eu": "stefan-it/wav2vec2-large-xlsr-53-basque",
    "gl": "ifrz/wav2vec2-large-xlsr-galician",
    "ka": "xsway/wav2vec2-large-xlsr-georgian",
    "lv": "jimregan/wav2vec2-large-xlsr-latvian-cv",
    "tl": "Khalsuu/filipino-wav2vec2-l-xls-r-300m-official",
    "sv": "KBLab/wav2vec2-large-voxrex-swedish",
}


def interpolate_nans(x, method='nearest'):
    if x.notnull().sum() > 1:
        return x.interpolate(method=method).ffill().bfill()
    else:
        return x.ffill().bfill()


def load_align_model(language_code: str, device: str, model_name: Optional[str] = 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:
            logger.error(f"No default alignment model for language: {language_code}. "
                        f"Please find a wav2vec2.0 model finetuned on this language at https://huggingface.co/models, "
                        f"then pass the model name via --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, cache_dir=model_dir)
            align_model = Wav2Vec2ForCTC.from_pretrained(model_name, cache_dir=model_dir)
        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)
    # Store temporary processing values
    segment_data: dict[int, SegmentData] = {}
    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)
            else:
                # add placeholder
                clean_char.append('*')
                clean_cdx.append(cdx)

        clean_wdx = []
        for wdx, wrd in enumerate(per_word):
            if any([c in model_dictionary.keys() for c in wrd.lower()]):
                clean_wdx.append(wdx)
            else:
                # index for placeholder
                clean_wdx.append(wdx)


        # Use language-specific Punkt model if available otherwise we fallback to English.
        punkt_lang = PUNKT_LANGUAGES.get(model_lang, 'english')
        try:
            sentence_splitter = nltk_load(f'tokenizers/punkt_tab/{punkt_lang}.pickle')
        except LookupError:
            nltk.download('punkt_tab', quiet=True)
            sentence_splitter = nltk_load(f'tokenizers/punkt_tab/{punkt_lang}.pickle')
        sentence_spans = list(sentence_splitter.span_tokenize(text))

        segment_data[sdx] = {
            "clean_char": clean_char,
            "clean_cdx": clean_cdx,
            "clean_wdx": clean_wdx,
            "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": [],
            "chars": None,
        }

        if return_char_alignments:
            aligned_seg["chars"] = []

        # check we can align
        if len(segment_data[sdx]["clean_char"]) == 0:
            logger.warning(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:
            logger.warning(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_data[sdx]["clean_char"])
        tokens = [model_dictionary.get(c, -1) 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)
        path = backtrack_beam(trellis, emission, tokens, blank_id, beam_width=2)

        if path is None:
            logger.warning(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_data[sdx]["clean_cdx"]:
                char_seg = char_segments[segment_data[sdx]["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 sdx2, (sstart, send) in enumerate(segment_data[sdx]["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"] = sdx2

            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 = torch.zeros((num_frame, num_tokens))
    trellis[1:, 0] = torch.cumsum(emission[1:, blank_id], 0)
    trellis[0, 1:] = -float("inf")
    trellis[-num_tokens + 1:, 0] = float("inf")

    for t in range(num_frame - 1):
        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[1:]],
            trellis[t, :-1] + get_wildcard_emission(emission[t], tokens[1:], blank_id),
        )
    return trellis


def get_wildcard_emission(frame_emission, tokens, blank_id):
    """Processing token emission scores containing wildcards (vectorized version)

    Args:
        frame_emission: Emission probability vector for the current frame
        tokens: List of token indices
        blank_id: ID of the blank token

    Returns:
        tensor: Maximum probability score for each token position
    """
    assert 0 <= blank_id < len(frame_emission)

    # Convert tokens to a tensor if they are not already
    tokens = torch.tensor(tokens) if not isinstance(tokens, torch.Tensor) else tokens

    # Create a mask to identify wildcard positions
    wildcard_mask = (tokens == -1)

    # Get scores for non-wildcard positions
    regular_scores = frame_emission[tokens.clamp(min=0).long()]  # clamp to avoid -1 index

    # Create a mask and compute the maximum value without modifying frame_emission
    max_valid_score = frame_emission.clone()   # Create a copy
    max_valid_score[blank_id] = float('-inf')  # Modify the copy to exclude the blank token
    max_valid_score = max_valid_score.max()

    # Use where operation to combine results
    result = torch.where(wildcard_mask, max_valid_score, regular_scores)

    return result


@dataclass
class Point:
    token_index: int
    time_index: int
    score: float


def backtrack(trellis, emission, tokens, blank_id=0):
    t, j = trellis.size(0) - 1, trellis.size(1) - 1

    path = [Point(j, t, emission[t, blank_id].exp().item())]
    while j > 0:
        # Should not happen but just in case
        assert t > 0

        # 1. Figure out if the current position was stay or change
        # Frame-wise score of stay vs change
        p_stay = emission[t - 1, blank_id]
        # p_change = emission[t - 1, tokens[j]]
        p_change = get_wildcard_emission(emission[t - 1], [tokens[j]], blank_id)[0]

        # Context-aware score for stay vs change
        stayed = trellis[t - 1, j] + p_stay
        changed = trellis[t - 1, j - 1] + p_change

        # Update position
        t -= 1
        if changed > stayed:
            j -= 1

        # Store the path with frame-wise probability.
        prob = (p_change if changed > stayed else p_stay).exp().item()
        path.append(Point(j, t, prob))

    # Now j == 0, which means, it reached the SoS.
    # Fill up the rest for the sake of visualization
    while t > 0:
        prob = emission[t - 1, blank_id].exp().item()
        path.append(Point(j, t - 1, prob))
        t -= 1

    return path[::-1]



@dataclass
class Path:
    points: List[Point]
    score: float


@dataclass
class BeamState:
    """State in beam search."""
    token_index: int   # Current token position
    time_index: int    # Current time step
    score: float       # Cumulative score
    path: List[Point]  # Path history


def backtrack_beam(trellis, emission, tokens, blank_id=0, beam_width=5):
    """Standard CTC beam search backtracking implementation.

    Args:
        trellis (torch.Tensor): The trellis (or lattice) of shape (T, N), where T is the number of time steps
                                and N is the number of tokens (including the blank token).
        emission (torch.Tensor): The emission probabilities of shape (T, N).
        tokens (List[int]): List of token indices (excluding the blank token).
        blank_id (int, optional): The ID of the blank token. Defaults to 0.
        beam_width (int, optional): The number of top paths to keep during beam search. Defaults to 5.

    Returns:
        List[Point]: the best path
    """
    T, J = trellis.size(0) - 1, trellis.size(1) - 1

    init_state = BeamState(
        token_index=J,
        time_index=T,
        score=trellis[T, J],
        path=[Point(J, T, emission[T, blank_id].exp().item())]
    )

    beams = [init_state]

    while beams and beams[0].token_index > 0:
        next_beams = []

        for beam in beams:
            t, j = beam.time_index, beam.token_index

            if t <= 0:
                continue

            p_stay = emission[t - 1, blank_id]
            p_change = get_wildcard_emission(emission[t - 1], [tokens[j]], blank_id)[0]

            stay_score = trellis[t - 1, j]
            change_score = trellis[t - 1, j - 1] if j > 0 else float('-inf')

            # Stay
            if not math.isinf(stay_score):
                new_path = beam.path.copy()
                new_path.append(Point(j, t - 1, p_stay.exp().item()))
                next_beams.append(BeamState(
                    token_index=j,
                    time_index=t - 1,
                    score=stay_score,
                    path=new_path
                ))

            # Change
            if j > 0 and not math.isinf(change_score):
                new_path = beam.path.copy()
                new_path.append(Point(j - 1, t - 1, p_change.exp().item()))
                next_beams.append(BeamState(
                    token_index=j - 1,
                    time_index=t - 1,
                    score=change_score,
                    path=new_path
                ))

        # sort by score
        beams = sorted(next_beams, key=lambda x: x.score, reverse=True)[:beam_width]

        if not beams:
            break

    if not beams:
        return None

    best_beam = beams[0]
    t = best_beam.time_index
    j = best_beam.token_index
    while t > 0:
        prob = emission[t - 1, blank_id].exp().item()
        best_beam.path.append(Point(j, t - 1, prob))
        t -= 1

    return best_beam.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