.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "tutorials/forced_alignment_tutorial.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_tutorials_forced_alignment_tutorial.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_tutorials_forced_alignment_tutorial.py:


Forced Alignment with Wav2Vec2
==============================

**Author**: `Moto Hira <moto@meta.com>`__

This tutorial shows how to align transcript to speech with
``torchaudio``, using CTC segmentation algorithm described in
`CTC-Segmentation of Large Corpora for German End-to-end Speech
Recognition <https://arxiv.org/abs/2007.09127>`__.

.. GENERATED FROM PYTHON SOURCE LINES 13-25

.. code-block:: default


    import torch
    import torchaudio

    print(torch.__version__)
    print(torchaudio.__version__)


    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print(device)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    1.13.0
    0.13.0
    cpu




.. GENERATED FROM PYTHON SOURCE LINES 26-39

Overview
--------

The process of alignment looks like the following.

1. Estimate the frame-wise label probability from audio waveform
2. Generate the trellis matrix which represents the probability of
   labels aligned at time step.
3. Find the most likely path from the trellis matrix.

In this example, we use ``torchaudio``\ ’s ``Wav2Vec2`` model for
acoustic feature extraction.


.. GENERATED FROM PYTHON SOURCE LINES 42-47

Preparation
-----------

First we import the necessary packages, and fetch data that we work on.


.. GENERATED FROM PYTHON SOURCE LINES 47-63

.. code-block:: default


    # %matplotlib inline

    from dataclasses import dataclass

    import IPython
    import matplotlib
    import matplotlib.pyplot as plt

    matplotlib.rcParams["figure.figsize"] = [16.0, 4.8]

    torch.random.manual_seed(0)

    SPEECH_FILE = torchaudio.utils.download_asset("tutorial-assets/Lab41-SRI-VOiCES-src-sp0307-ch127535-sg0042.wav")









.. GENERATED FROM PYTHON SOURCE LINES 64-80

Generate frame-wise label probability
-------------------------------------

The first step is to generate the label class porbability of each aduio
frame. We can use a Wav2Vec2 model that is trained for ASR. Here we use
:py:func:`torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H`.

``torchaudio`` provides easy access to pretrained models with associated
labels.

.. note::

   In the subsequent sections, we will compute the probability in
   log-domain to avoid numerical instability. For this purpose, we
   normalize the ``emission`` with :py:func:`torch.log_softmax`.


.. GENERATED FROM PYTHON SOURCE LINES 80-91

.. code-block:: default


    bundle = torchaudio.pipelines.WAV2VEC2_ASR_BASE_960H
    model = bundle.get_model().to(device)
    labels = bundle.get_labels()
    with torch.inference_mode():
        waveform, _ = torchaudio.load(SPEECH_FILE)
        emissions, _ = model(waveform.to(device))
        emissions = torch.log_softmax(emissions, dim=-1)

    emission = emissions[0].cpu().detach()








.. GENERATED FROM PYTHON SOURCE LINES 92-94

Visualization
###############################################################################

.. GENERATED FROM PYTHON SOURCE LINES 94-103

.. code-block:: default

    print(labels)
    plt.imshow(emission.T)
    plt.colorbar()
    plt.title("Frame-wise class probability")
    plt.xlabel("Time")
    plt.ylabel("Labels")
    plt.show()





.. image-sg:: /tutorials/images/sphx_glr_forced_alignment_tutorial_001.png
   :alt: Frame-wise class probability
   :srcset: /tutorials/images/sphx_glr_forced_alignment_tutorial_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    ('-', '|', 'E', 'T', 'A', 'O', 'N', 'I', 'H', 'S', 'R', 'D', 'L', 'U', 'M', 'W', 'C', 'F', 'G', 'Y', 'P', 'B', 'V', 'K', "'", 'X', 'J', 'Q', 'Z')




.. GENERATED FROM PYTHON SOURCE LINES 104-140

Generate alignment probability (trellis)
----------------------------------------

From the emission matrix, next we generate the trellis which represents
the probability of transcript labels occur at each time frame.

Trellis is 2D matrix with time axis and label axis. The label axis
represents the transcript that we are aligning. In the following, we use
:math:`t` to denote the index in time axis and :math:`j` to denote the
index in label axis. :math:`c_j` represents the label at label index
:math:`j`.

To generate, the probability of time step :math:`t+1`, we look at the
trellis from time step :math:`t` and emission at time step :math:`t+1`.
There are two path to reach to time step :math:`t+1` with label
:math:`c_{j+1}`. The first one is the case where the label was
:math:`c_{j+1}` at :math:`t` and there was no label change from
:math:`t` to :math:`t+1`. The other case is where the label was
:math:`c_j` at :math:`t` and it transitioned to the next label
:math:`c_{j+1}` at :math:`t+1`.

The follwoing diagram illustrates this transition.

.. image:: https://download.pytorch.org/torchaudio/tutorial-assets/ctc-forward.png

Since we are looking for the most likely transitions, we take the more
likely path for the value of :math:`k_{(t+1, j+1)}`, that is

:math:`k_{(t+1, j+1)} = max( k_{(t, j)} p(t+1, c_{j+1}), k_{(t, j+1)} p(t+1, repeat) )`

where :math:`k` represents is trellis matrix, and :math:`p(t, c_j)`
represents the probability of label :math:`c_j` at time step :math:`t`.
:math:`repeat` represents the blank token from CTC formulation. (For the
detail of CTC algorithm, please refer to the *Sequence Modeling with CTC*
[`distill.pub <https://distill.pub/2017/ctc/>`__])


.. GENERATED FROM PYTHON SOURCE LINES 140-173

.. code-block:: default


    transcript = "I|HAD|THAT|CURIOSITY|BESIDE|ME|AT|THIS|MOMENT"
    dictionary = {c: i for i, c in enumerate(labels)}

    tokens = [dictionary[c] for c in transcript]
    print(list(zip(transcript, tokens)))


    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


    trellis = get_trellis(emission, tokens)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    [('I', 7), ('|', 1), ('H', 8), ('A', 4), ('D', 11), ('|', 1), ('T', 3), ('H', 8), ('A', 4), ('T', 3), ('|', 1), ('C', 16), ('U', 13), ('R', 10), ('I', 7), ('O', 5), ('S', 9), ('I', 7), ('T', 3), ('Y', 19), ('|', 1), ('B', 21), ('E', 2), ('S', 9), ('I', 7), ('D', 11), ('E', 2), ('|', 1), ('M', 14), ('E', 2), ('|', 1), ('A', 4), ('T', 3), ('|', 1), ('T', 3), ('H', 8), ('I', 7), ('S', 9), ('|', 1), ('M', 14), ('O', 5), ('M', 14), ('E', 2), ('N', 6), ('T', 3)]




.. GENERATED FROM PYTHON SOURCE LINES 174-176

Visualization
###############################################################################

.. GENERATED FROM PYTHON SOURCE LINES 176-181

.. code-block:: default

    plt.imshow(trellis[1:, 1:].T, origin="lower")
    plt.annotate("- Inf", (trellis.size(1) / 5, trellis.size(1) / 1.5))
    plt.colorbar()
    plt.show()




.. image-sg:: /tutorials/images/sphx_glr_forced_alignment_tutorial_002.png
   :alt: forced alignment tutorial
   :srcset: /tutorials/images/sphx_glr_forced_alignment_tutorial_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 182-185

In the above visualization, we can see that there is a trace of high
probability crossing the matrix diagonally.


.. GENERATED FROM PYTHON SOURCE LINES 188-207

Find the most likely path (backtracking)
----------------------------------------

Once the trellis is generated, we will traverse it following the
elements with high probability.

We will start from the last label index with the time step of highest
probability, then, we traverse back in time, picking stay
(:math:`c_j \rightarrow c_j`) or transition
(:math:`c_j \rightarrow c_{j+1}`), based on the post-transition
probability :math:`k_{t, j} p(t+1, c_{j+1})` or
:math:`k_{t, j+1} p(t+1, repeat)`.

Transition is done once the label reaches the beginning.

The trellis matrix is used for path-finding, but for the final
probability of each segment, we take the frame-wise probability from
emission matrix.


.. GENERATED FROM PYTHON SOURCE LINES 207-257

.. code-block:: default



    @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:
            raise ValueError("Failed to align")
        return path[::-1]


    path = backtrack(trellis, emission, tokens)
    for p in path:
        print(p)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Point(token_index=0, time_index=30, score=0.9999842643737793)
    Point(token_index=0, time_index=31, score=0.9846959710121155)
    Point(token_index=0, time_index=32, score=0.9999706745147705)
    Point(token_index=0, time_index=33, score=0.15398800373077393)
    Point(token_index=1, time_index=34, score=0.9999173879623413)
    Point(token_index=1, time_index=35, score=0.6080105900764465)
    Point(token_index=2, time_index=36, score=0.9997721314430237)
    Point(token_index=2, time_index=37, score=0.9997127652168274)
    Point(token_index=3, time_index=38, score=0.9999358654022217)
    Point(token_index=3, time_index=39, score=0.9861598610877991)
    Point(token_index=4, time_index=40, score=0.9238577485084534)
    Point(token_index=4, time_index=41, score=0.9257409572601318)
    Point(token_index=4, time_index=42, score=0.01566212624311447)
    Point(token_index=5, time_index=43, score=0.9998378753662109)
    Point(token_index=6, time_index=44, score=0.9988443851470947)
    Point(token_index=6, time_index=45, score=0.10147196799516678)
    Point(token_index=7, time_index=46, score=0.9999427795410156)
    Point(token_index=7, time_index=47, score=0.9999943971633911)
    Point(token_index=8, time_index=48, score=0.9979604482650757)
    Point(token_index=8, time_index=49, score=0.03603758662939072)
    Point(token_index=8, time_index=50, score=0.06163697689771652)
    Point(token_index=9, time_index=51, score=4.3358733819331974e-05)
    Point(token_index=10, time_index=52, score=0.9999799728393555)
    Point(token_index=10, time_index=53, score=0.9967100620269775)
    Point(token_index=10, time_index=54, score=0.9999256134033203)
    Point(token_index=11, time_index=55, score=0.9999983310699463)
    Point(token_index=11, time_index=56, score=0.9990687966346741)
    Point(token_index=11, time_index=57, score=0.9999996423721313)
    Point(token_index=11, time_index=58, score=0.9999996423721313)
    Point(token_index=11, time_index=59, score=0.8457656502723694)
    Point(token_index=12, time_index=60, score=0.9999996423721313)
    Point(token_index=12, time_index=61, score=0.9996013045310974)
    Point(token_index=13, time_index=62, score=0.999998927116394)
    Point(token_index=13, time_index=63, score=0.0035240992438048124)
    Point(token_index=13, time_index=64, score=1.0)
    Point(token_index=13, time_index=65, score=1.0)
    Point(token_index=14, time_index=66, score=0.9999915361404419)
    Point(token_index=14, time_index=67, score=0.9971572160720825)
    Point(token_index=14, time_index=68, score=0.9999990463256836)
    Point(token_index=14, time_index=69, score=0.9999992847442627)
    Point(token_index=14, time_index=70, score=0.9999997615814209)
    Point(token_index=14, time_index=71, score=0.9999998807907104)
    Point(token_index=14, time_index=72, score=0.9999880790710449)
    Point(token_index=14, time_index=73, score=0.011424383148550987)
    Point(token_index=15, time_index=74, score=0.9999977350234985)
    Point(token_index=15, time_index=75, score=0.9996136426925659)
    Point(token_index=15, time_index=76, score=0.999998927116394)
    Point(token_index=15, time_index=77, score=0.9727645516395569)
    Point(token_index=16, time_index=78, score=0.999998927116394)
    Point(token_index=16, time_index=79, score=0.9949339032173157)
    Point(token_index=16, time_index=80, score=0.999998927116394)
    Point(token_index=16, time_index=81, score=0.9999121427536011)
    Point(token_index=17, time_index=82, score=0.9999774694442749)
    Point(token_index=17, time_index=83, score=0.6575831770896912)
    Point(token_index=17, time_index=84, score=0.9984298348426819)
    Point(token_index=18, time_index=85, score=0.9999876022338867)
    Point(token_index=18, time_index=86, score=0.9993745684623718)
    Point(token_index=18, time_index=87, score=0.9999988079071045)
    Point(token_index=18, time_index=88, score=0.10426424443721771)
    Point(token_index=19, time_index=89, score=0.9999969005584717)
    Point(token_index=19, time_index=90, score=0.39782625436782837)
    Point(token_index=20, time_index=91, score=0.9999932050704956)
    Point(token_index=20, time_index=92, score=1.6989914684018004e-06)
    Point(token_index=20, time_index=93, score=0.986134946346283)
    Point(token_index=21, time_index=94, score=0.9999960660934448)
    Point(token_index=21, time_index=95, score=0.9992731213569641)
    Point(token_index=21, time_index=96, score=0.9993410706520081)
    Point(token_index=22, time_index=97, score=0.9999983310699463)
    Point(token_index=22, time_index=98, score=0.9999971389770508)
    Point(token_index=22, time_index=99, score=0.9999998807907104)
    Point(token_index=22, time_index=100, score=0.9999995231628418)
    Point(token_index=23, time_index=101, score=0.9999732971191406)
    Point(token_index=23, time_index=102, score=0.9983221888542175)
    Point(token_index=23, time_index=103, score=0.9999991655349731)
    Point(token_index=23, time_index=104, score=0.9999996423721313)
    Point(token_index=23, time_index=105, score=0.9999998807907104)
    Point(token_index=23, time_index=106, score=1.0)
    Point(token_index=23, time_index=107, score=0.9998629093170166)
    Point(token_index=24, time_index=108, score=0.9999980926513672)
    Point(token_index=24, time_index=109, score=0.9988582134246826)
    Point(token_index=25, time_index=110, score=0.9999798536300659)
    Point(token_index=25, time_index=111, score=0.8573304414749146)
    Point(token_index=26, time_index=112, score=0.9999847412109375)
    Point(token_index=26, time_index=113, score=0.9870284795761108)
    Point(token_index=26, time_index=114, score=1.9046094166697003e-05)
    Point(token_index=27, time_index=115, score=0.9999794960021973)
    Point(token_index=27, time_index=116, score=0.9998254776000977)
    Point(token_index=28, time_index=117, score=0.9999990463256836)
    Point(token_index=28, time_index=118, score=0.9999732971191406)
    Point(token_index=28, time_index=119, score=0.0009001801954582334)
    Point(token_index=29, time_index=120, score=0.999347984790802)
    Point(token_index=29, time_index=121, score=0.9975457787513733)
    Point(token_index=29, time_index=122, score=0.00030502272420562804)
    Point(token_index=30, time_index=123, score=0.9999344348907471)
    Point(token_index=30, time_index=124, score=6.078777914808597e-06)
    Point(token_index=31, time_index=125, score=0.9833180904388428)
    Point(token_index=32, time_index=126, score=0.9974581599235535)
    Point(token_index=32, time_index=127, score=0.0008237161673605442)
    Point(token_index=33, time_index=128, score=0.996515154838562)
    Point(token_index=33, time_index=129, score=0.017463242635130882)
    Point(token_index=34, time_index=130, score=0.9989172220230103)
    Point(token_index=35, time_index=131, score=0.9999697208404541)
    Point(token_index=35, time_index=132, score=0.9999842643737793)
    Point(token_index=36, time_index=133, score=0.9997640252113342)
    Point(token_index=36, time_index=134, score=0.5097627639770508)
    Point(token_index=37, time_index=135, score=0.9998301267623901)
    Point(token_index=37, time_index=136, score=0.08528485894203186)
    Point(token_index=37, time_index=137, score=0.00407379399985075)
    Point(token_index=38, time_index=138, score=0.9999815225601196)
    Point(token_index=38, time_index=139, score=0.012051556259393692)
    Point(token_index=38, time_index=140, score=0.9999980926513672)
    Point(token_index=38, time_index=141, score=0.0005778882768936455)
    Point(token_index=39, time_index=142, score=0.9999067783355713)
    Point(token_index=39, time_index=143, score=0.9999960660934448)
    Point(token_index=39, time_index=144, score=0.9999980926513672)
    Point(token_index=40, time_index=145, score=0.9999916553497314)
    Point(token_index=40, time_index=146, score=0.9971166849136353)
    Point(token_index=40, time_index=147, score=0.9981802701950073)
    Point(token_index=41, time_index=148, score=0.9999310970306396)
    Point(token_index=41, time_index=149, score=0.98795086145401)
    Point(token_index=41, time_index=150, score=0.9997627139091492)
    Point(token_index=42, time_index=151, score=0.9999535083770752)
    Point(token_index=43, time_index=152, score=0.9999715089797974)
    Point(token_index=44, time_index=153, score=0.6811069846153259)




.. GENERATED FROM PYTHON SOURCE LINES 258-260

Visualization
###############################################################################

.. GENERATED FROM PYTHON SOURCE LINES 260-272

.. code-block:: default

    def plot_trellis_with_path(trellis, path):
        # To plot trellis with path, we take advantage of 'nan' value
        trellis_with_path = trellis.clone()
        for _, p in enumerate(path):
            trellis_with_path[p.time_index, p.token_index] = float("nan")
        plt.imshow(trellis_with_path[1:, 1:].T, origin="lower")


    plot_trellis_with_path(trellis, path)
    plt.title("The path found by backtracking")
    plt.show()




.. image-sg:: /tutorials/images/sphx_glr_forced_alignment_tutorial_003.png
   :alt: The path found by backtracking
   :srcset: /tutorials/images/sphx_glr_forced_alignment_tutorial_003.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 273-279

Looking good. Now this path contains repetations for the same labels, so
let’s merge them to make it close to the original transcript.

When merging the multiple path points, we simply take the average
probability for the merged segments.


.. GENERATED FROM PYTHON SOURCE LINES 279-321

.. code-block:: default



    # 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):
        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


    segments = merge_repeats(path)
    for seg in segments:
        print(seg)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    I       (0.78): [   30,    34)
    |       (0.80): [   34,    36)
    H       (1.00): [   36,    38)
    A       (0.99): [   38,    40)
    D       (0.62): [   40,    43)
    |       (1.00): [   43,    44)
    T       (0.55): [   44,    46)
    H       (1.00): [   46,    48)
    A       (0.37): [   48,    51)
    T       (0.00): [   51,    52)
    |       (1.00): [   52,    55)
    C       (0.97): [   55,    60)
    U       (1.00): [   60,    62)
    R       (0.75): [   62,    66)
    I       (0.88): [   66,    74)
    O       (0.99): [   74,    78)
    S       (1.00): [   78,    82)
    I       (0.89): [   82,    85)
    T       (0.78): [   85,    89)
    Y       (0.70): [   89,    91)
    |       (0.66): [   91,    94)
    B       (1.00): [   94,    97)
    E       (1.00): [   97,   101)
    S       (1.00): [  101,   108)
    I       (1.00): [  108,   110)
    D       (0.93): [  110,   112)
    E       (0.66): [  112,   115)
    |       (1.00): [  115,   117)
    M       (0.67): [  117,   120)
    E       (0.67): [  120,   123)
    |       (0.50): [  123,   125)
    A       (0.98): [  125,   126)
    T       (0.50): [  126,   128)
    |       (0.51): [  128,   130)
    T       (1.00): [  130,   131)
    H       (1.00): [  131,   133)
    I       (0.75): [  133,   135)
    S       (0.36): [  135,   138)
    |       (0.50): [  138,   142)
    M       (1.00): [  142,   145)
    O       (1.00): [  145,   148)
    M       (1.00): [  148,   151)
    E       (1.00): [  151,   152)
    N       (1.00): [  152,   153)
    T       (0.68): [  153,   154)




.. GENERATED FROM PYTHON SOURCE LINES 322-324

Visualization
###############################################################################

.. GENERATED FROM PYTHON SOURCE LINES 324-369

.. code-block:: default

    def plot_trellis_with_segments(trellis, segments, transcript):
        # To plot trellis with path, we take advantage of 'nan' value
        trellis_with_path = trellis.clone()
        for i, seg in enumerate(segments):
            if seg.label != "|":
                trellis_with_path[seg.start + 1 : seg.end + 1, i + 1] = float("nan")

        fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9.5))
        ax1.set_title("Path, label and probability for each label")
        ax1.imshow(trellis_with_path.T, origin="lower")
        ax1.set_xticks([])

        for i, seg in enumerate(segments):
            if seg.label != "|":
                ax1.annotate(seg.label, (seg.start + 0.7, i + 0.3), weight="bold")
                ax1.annotate(f"{seg.score:.2f}", (seg.start - 0.3, i + 4.3))

        ax2.set_title("Label probability with and without repetation")
        xs, hs, ws = [], [], []
        for seg in segments:
            if seg.label != "|":
                xs.append((seg.end + seg.start) / 2 + 0.4)
                hs.append(seg.score)
                ws.append(seg.end - seg.start)
                ax2.annotate(seg.label, (seg.start + 0.8, -0.07), weight="bold")
        ax2.bar(xs, hs, width=ws, color="gray", alpha=0.5, edgecolor="black")

        xs, hs = [], []
        for p in path:
            label = transcript[p.token_index]
            if label != "|":
                xs.append(p.time_index + 1)
                hs.append(p.score)

        ax2.bar(xs, hs, width=0.5, alpha=0.5)
        ax2.axhline(0, color="black")
        ax2.set_xlim(ax1.get_xlim())
        ax2.set_ylim(-0.1, 1.1)


    plot_trellis_with_segments(trellis, segments, transcript)
    plt.tight_layout()
    plt.show()





.. image-sg:: /tutorials/images/sphx_glr_forced_alignment_tutorial_004.png
   :alt: Path, label and probability for each label, Label probability with and without repetation
   :srcset: /tutorials/images/sphx_glr_forced_alignment_tutorial_004.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 370-377

Looks good. Now let’s merge the words. The Wav2Vec2 model uses ``'|'``
as the word boundary, so we merge the segments before each occurance of
``'|'``.

Then, finally, we segment the original audio into segmented audio and
listen to them to see if the segmentation is correct.


.. GENERATED FROM PYTHON SOURCE LINES 377-401

.. code-block:: default


    # Merge words
    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


    word_segments = merge_words(segments)
    for word in word_segments:
        print(word)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    I       (0.78): [   30,    34)
    HAD     (0.84): [   36,    43)
    THAT    (0.52): [   44,    52)
    CURIOSITY       (0.89): [   55,    91)
    BESIDE  (0.94): [   94,   115)
    ME      (0.67): [  117,   123)
    AT      (0.66): [  125,   128)
    THIS    (0.70): [  130,   138)
    MOMENT  (0.97): [  142,   154)




.. GENERATED FROM PYTHON SOURCE LINES 402-404

Visualization
###############################################################################

.. GENERATED FROM PYTHON SOURCE LINES 404-453

.. code-block:: default

    def plot_alignments(trellis, segments, word_segments, waveform):
        trellis_with_path = trellis.clone()
        for i, seg in enumerate(segments):
            if seg.label != "|":
                trellis_with_path[seg.start + 1 : seg.end + 1, i + 1] = float("nan")

        fig, [ax1, ax2] = plt.subplots(2, 1, figsize=(16, 9.5))

        ax1.imshow(trellis_with_path[1:, 1:].T, origin="lower")
        ax1.set_xticks([])
        ax1.set_yticks([])

        for word in word_segments:
            ax1.axvline(word.start - 0.5)
            ax1.axvline(word.end - 0.5)

        for i, seg in enumerate(segments):
            if seg.label != "|":
                ax1.annotate(seg.label, (seg.start, i + 0.3))
                ax1.annotate(f"{seg.score:.2f}", (seg.start, i + 4), fontsize=8)

        # The original waveform
        ratio = waveform.size(0) / (trellis.size(0) - 1)
        ax2.plot(waveform)
        for word in word_segments:
            x0 = ratio * word.start
            x1 = ratio * word.end
            ax2.axvspan(x0, x1, alpha=0.1, color="red")
            ax2.annotate(f"{word.score:.2f}", (x0, 0.8))

        for seg in segments:
            if seg.label != "|":
                ax2.annotate(seg.label, (seg.start * ratio, 0.9))
        xticks = ax2.get_xticks()
        plt.xticks(xticks, xticks / bundle.sample_rate)
        ax2.set_xlabel("time [second]")
        ax2.set_yticks([])
        ax2.set_ylim(-1.0, 1.0)
        ax2.set_xlim(0, waveform.size(-1))


    plot_alignments(
        trellis,
        segments,
        word_segments,
        waveform[0],
    )
    plt.show()




.. image-sg:: /tutorials/images/sphx_glr_forced_alignment_tutorial_005.png
   :alt: forced alignment tutorial
   :srcset: /tutorials/images/sphx_glr_forced_alignment_tutorial_005.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 455-469

.. code-block:: default


    # A trick to embed the resulting audio to the generated file.
    # `IPython.display.Audio` has to be the last call in a cell,
    # and there should be only one call par cell.
    def display_segment(i):
        ratio = waveform.size(1) / (trellis.size(0) - 1)
        word = word_segments[i]
        x0 = int(ratio * word.start)
        x1 = int(ratio * word.end)
        print(f"{word.label} ({word.score:.2f}): {x0 / bundle.sample_rate:.3f} - {x1 / bundle.sample_rate:.3f} sec")
        segment = waveform[:, x0:x1]
        return IPython.display.Audio(segment.numpy(), rate=bundle.sample_rate)









.. GENERATED FROM PYTHON SOURCE LINES 471-477

.. code-block:: default


    # Generate the audio for each segment
    print(transcript)
    IPython.display.Audio(SPEECH_FILE)






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    I|HAD|THAT|CURIOSITY|BESIDE|ME|AT|THIS|MOMENT


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">

                    <audio  controls="controls" >
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.. GENERATED FROM PYTHON SOURCE LINES 484-487

.. code-block:: default


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.. rst-class:: sphx-glr-script-out

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.. GENERATED FROM PYTHON SOURCE LINES 489-492

.. code-block:: default


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.. rst-class:: sphx-glr-script-out

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.. GENERATED FROM PYTHON SOURCE LINES 494-497

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.. GENERATED FROM PYTHON SOURCE LINES 499-502

.. code-block:: default


    display_segment(4)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    BESIDE (0.94): 1.891 - 2.314 sec


.. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 504-507

.. code-block:: default


    display_segment(5)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    ME (0.67): 2.354 - 2.474 sec


.. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 509-512

.. code-block:: default


    display_segment(6)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    AT (0.66): 2.515 - 2.575 sec


.. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 514-517

.. code-block:: default


    display_segment(7)





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    THIS (0.70): 2.615 - 2.776 sec


.. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 519-522

.. code-block:: default


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    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 523-529

Conclusion
----------

In this tutorial, we looked how to use torchaudio’s Wav2Vec2 model to
perform CTC segmentation for forced alignment.



.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  2.589 seconds)


.. _sphx_glr_download_tutorials_forced_alignment_tutorial.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: forced_alignment_tutorial.py <forced_alignment_tutorial.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: forced_alignment_tutorial.ipynb <forced_alignment_tutorial.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_