whisperX/whisperx/alignment.py

""""
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"
}


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 or t2 - t1 < 0.02:
            print("Failed to align segment: 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]

        with torch.inference_mode():
            if model_type == "torchaudio":
                emissions, _ = model(waveform_segment.to(device))
            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()
            sentence_end = curr_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