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

**Author** `Moto Hira <moto@fb.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>`__.

"""

import torch
import torchaudio

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


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


######################################################################
# 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.
#


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

# %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")


######################################################################
# 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`.
#

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()

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


######################################################################
# 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/>`__])
#

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)

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

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


######################################################################
# 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.
#


@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)


################################################################################
# Visualization
################################################################################
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()

######################################################################
# 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.
#


# 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)


################################################################################
# Visualization
################################################################################
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()


######################################################################
# 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.
#

# 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)


################################################################################
# Visualization
################################################################################
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()

################################################################################
#

# 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)


######################################################################
#

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


######################################################################
#

display_segment(0)

######################################################################
#

display_segment(1)

######################################################################
#

display_segment(2)

######################################################################
#

display_segment(3)

######################################################################
#

display_segment(4)

######################################################################
#

display_segment(5)

######################################################################
#

display_segment(6)

######################################################################
#

display_segment(7)

######################################################################
#

display_segment(8)

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