"""
Title: Automatic Speech Recognition with Transformer
Author: [Apoorv Nandan](https://twitter.com/NandanApoorv)
Date created: 2021/01/13
Last modified: 2021/01/13
Description: Training a sequence-to-sequence Transformer for automatic speech recognition.
Accelerator: GPU
"""
"""
## Introduction

Automatic speech recognition (ASR) consists of transcribing audio speech segments into text.
ASR can be treated as a sequence-to-sequence problem, where the
audio can be represented as a sequence of feature vectors
and the text as a sequence of characters, words, or subword tokens.

For this demonstration, we will use the LJSpeech dataset from the
[LibriVox](https://librivox.org/) project. It consists of short
audio clips of a single speaker reading passages from 7 non-fiction books.
Our model will be similar to the original Transformer (both encoder and decoder)
as proposed in the paper, "Attention is All You Need".


**References:**

- [Attention is All You Need](https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf)
- [Very Deep Self-Attention Networks for End-to-End Speech Recognition](https://arxiv.org/pdf/1904.13377.pdf)
- [Speech Transformers](https://ieeexplore.ieee.org/document/8462506)
- [LJSpeech Dataset](https://keithito.com/LJ-Speech-Dataset/)
"""


import os
import random
from glob import glob
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers


"""
## Define the Transformer Input Layer

When processing past target tokens for the decoder, we compute the sum of
position embeddings and token embeddings.

When processing audio features, we apply convolutional layers to downsample
them (via convolution strides) and process local relationships.
"""


class TokenEmbedding(layers.Layer):
    def __init__(self, num_vocab=1000, maxlen=100, num_hid=64):
        super().__init__()
        self.emb = tf.keras.layers.Embedding(num_vocab, num_hid)
        self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=num_hid)

    def call(self, x):
        maxlen = tf.shape(x)[-1]
        x = self.emb(x)
        positions = tf.range(start=0, limit=maxlen, delta=1)
        positions = self.pos_emb(positions)
        return x + positions


class SpeechFeatureEmbedding(layers.Layer):
    def __init__(self, num_hid=64, maxlen=100):
        super().__init__()
        self.conv1 = tf.keras.layers.Conv1D(
            num_hid, 11, strides=2, padding="same", activation="relu"
        )
        self.conv2 = tf.keras.layers.Conv1D(
            num_hid, 11, strides=2, padding="same", activation="relu"
        )
        self.conv3 = tf.keras.layers.Conv1D(
            num_hid, 11, strides=2, padding="same", activation="relu"
        )

    def call(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        return self.conv3(x)


"""
## Transformer Encoder Layer
"""


class TransformerEncoder(layers.Layer):
    def __init__(self, embed_dim, num_heads, feed_forward_dim, rate=0.1):
        super().__init__()
        self.att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
        self.ffn = keras.Sequential(
            [
                layers.Dense(feed_forward_dim, activation="relu"),
                layers.Dense(embed_dim),
            ]
        )
        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
        self.dropout1 = layers.Dropout(rate)
        self.dropout2 = layers.Dropout(rate)

    def call(self, inputs, training):
        attn_output = self.att(inputs, inputs)
        attn_output = self.dropout1(attn_output, training=training)
        out1 = self.layernorm1(inputs + attn_output)
        ffn_output = self.ffn(out1)
        ffn_output = self.dropout2(ffn_output, training=training)
        return self.layernorm2(out1 + ffn_output)


"""
## Transformer Decoder Layer
"""


class TransformerDecoder(layers.Layer):
    def __init__(self, embed_dim, num_heads, feed_forward_dim, dropout_rate=0.1):
        super().__init__()
        self.layernorm1 = layers.LayerNormalization(epsilon=1e-6)
        self.layernorm2 = layers.LayerNormalization(epsilon=1e-6)
        self.layernorm3 = layers.LayerNormalization(epsilon=1e-6)
        self.self_att = layers.MultiHeadAttention(
            num_heads=num_heads, key_dim=embed_dim
        )
        self.enc_att = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
        self.self_dropout = layers.Dropout(0.5)
        self.enc_dropout = layers.Dropout(0.1)
        self.ffn_dropout = layers.Dropout(0.1)
        self.ffn = keras.Sequential(
            [
                layers.Dense(feed_forward_dim, activation="relu"),
                layers.Dense(embed_dim),
            ]
        )

    def causal_attention_mask(self, batch_size, n_dest, n_src, dtype):
        """Masks the upper half of the dot product matrix in self attention.

        This prevents flow of information from future tokens to current token.
        1's in the lower triangle, counting from the lower right corner.
        """
        i = tf.range(n_dest)[:, None]
        j = tf.range(n_src)
        m = i >= j - n_src + n_dest
        mask = tf.cast(m, dtype)
        mask = tf.reshape(mask, [1, n_dest, n_src])
        mult = tf.concat(
            [tf.expand_dims(batch_size, -1), tf.constant([1, 1], dtype=tf.int32)], 0
        )
        return tf.tile(mask, mult)

    def call(self, enc_out, target):
        input_shape = tf.shape(target)
        batch_size = input_shape[0]
        seq_len = input_shape[1]
        causal_mask = self.causal_attention_mask(batch_size, seq_len, seq_len, tf.bool)
        target_att = self.self_att(target, target, attention_mask=causal_mask)
        target_norm = self.layernorm1(target + self.self_dropout(target_att))
        enc_out = self.enc_att(target_norm, enc_out)
        enc_out_norm = self.layernorm2(self.enc_dropout(enc_out) + target_norm)
        ffn_out = self.ffn(enc_out_norm)
        ffn_out_norm = self.layernorm3(enc_out_norm + self.ffn_dropout(ffn_out))
        return ffn_out_norm


"""
## Complete the Transformer model

Our model takes audio spectrograms as inputs and predicts a sequence of characters.
During training, we give the decoder the target character sequence shifted to the left
as input. During inference, the decoder uses its own past predictions to predict the
next token.
"""


class Transformer(keras.Model):
    def __init__(
        self,
        num_hid=64,
        num_head=2,
        num_feed_forward=128,
        source_maxlen=100,
        target_maxlen=100,
        num_layers_enc=4,
        num_layers_dec=1,
        num_classes=10,
    ):
        super().__init__()
        self.loss_metric = keras.metrics.Mean(name="loss")
        self.num_layers_enc = num_layers_enc
        self.num_layers_dec = num_layers_dec
        self.target_maxlen = target_maxlen
        self.num_classes = num_classes

        self.enc_input = SpeechFeatureEmbedding(num_hid=num_hid, maxlen=source_maxlen)
        self.dec_input = TokenEmbedding(
            num_vocab=num_classes, maxlen=target_maxlen, num_hid=num_hid
        )

        self.encoder = keras.Sequential(
            [self.enc_input]
            + [
                TransformerEncoder(num_hid, num_head, num_feed_forward)
                for _ in range(num_layers_enc)
            ]
        )

        for i in range(num_layers_dec):
            setattr(
                self,
                f"dec_layer_{i}",
                TransformerDecoder(num_hid, num_head, num_feed_forward),
            )

        self.classifier = layers.Dense(num_classes)

    def decode(self, enc_out, target):
        y = self.dec_input(target)
        for i in range(self.num_layers_dec):
            y = getattr(self, f"dec_layer_{i}")(enc_out, y)
        return y

    def call(self, inputs):
        source = inputs[0]
        target = inputs[1]
        x = self.encoder(source)
        y = self.decode(x, target)
        return self.classifier(y)

    @property
    def metrics(self):
        return [self.loss_metric]

    def train_step(self, batch):
        """Processes one batch inside model.fit()."""
        source = batch["source"]
        target = batch["target"]
        dec_input = target[:, :-1]
        dec_target = target[:, 1:]
        with tf.GradientTape() as tape:
            preds = self([source, dec_input])
            one_hot = tf.one_hot(dec_target, depth=self.num_classes)
            mask = tf.math.logical_not(tf.math.equal(dec_target, 0))
            loss = self.compiled_loss(one_hot, preds, sample_weight=mask)
        trainable_vars = self.trainable_variables
        gradients = tape.gradient(loss, trainable_vars)
        self.optimizer.apply_gradients(zip(gradients, trainable_vars))
        self.loss_metric.update_state(loss)
        return {"loss": self.loss_metric.result()}

    def test_step(self, batch):
        source = batch["source"]
        target = batch["target"]
        dec_input = target[:, :-1]
        dec_target = target[:, 1:]
        preds = self([source, dec_input])
        one_hot = tf.one_hot(dec_target, depth=self.num_classes)
        mask = tf.math.logical_not(tf.math.equal(dec_target, 0))
        loss = self.compiled_loss(one_hot, preds, sample_weight=mask)
        self.loss_metric.update_state(loss)
        return {"loss": self.loss_metric.result()}

    def generate(self, source, target_start_token_idx):
        """Performs inference over one batch of inputs using greedy decoding."""
        bs = tf.shape(source)[0]
        enc = self.encoder(source)
        dec_input = tf.ones((bs, 1), dtype=tf.int32) * target_start_token_idx
        dec_logits = []
        for i in range(self.target_maxlen - 1):
            dec_out = self.decode(enc, dec_input)
            logits = self.classifier(dec_out)
            logits = tf.argmax(logits, axis=-1, output_type=tf.int32)
            last_logit = tf.expand_dims(logits[:, -1], axis=-1)
            dec_logits.append(last_logit)
            dec_input = tf.concat([dec_input, last_logit], axis=-1)
        return dec_input


"""
## Download the dataset

Note: This requires ~3.6 GB of disk space and
takes ~5 minutes for the extraction of files.
"""

keras.utils.get_file(
    os.path.join(os.getcwd(), "data.tar.gz"),
    "https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2",
    extract=True,
    archive_format="tar",
    cache_dir=".",
)


saveto = "./datasets/LJSpeech-1.1"
wavs = glob("{}/**/*.wav".format(saveto), recursive=True)

id_to_text = {}
with open(os.path.join(saveto, "metadata.csv"), encoding="utf-8") as f:
    for line in f:
        id = line.strip().split("|")[0]
        text = line.strip().split("|")[2]
        id_to_text[id] = text


def get_data(wavs, id_to_text, maxlen=50):
    """returns mapping of audio paths and transcription texts"""
    data = []
    for w in wavs:
        id = w.split("/")[-1].split(".")[0]
        if len(id_to_text[id]) < maxlen:
            data.append({"audio": w, "text": id_to_text[id]})
    return data


"""
## Preprocess the dataset
"""


class VectorizeChar:
    def __init__(self, max_len=50):
        self.vocab = (
            ["-", "#", "<", ">"]
            + [chr(i + 96) for i in range(1, 27)]
            + [" ", ".", ",", "?"]
        )
        self.max_len = max_len
        self.char_to_idx = {}
        for i, ch in enumerate(self.vocab):
            self.char_to_idx[ch] = i

    def __call__(self, text):
        text = text.lower()
        text = text[: self.max_len - 2]
        text = "<" + text + ">"
        pad_len = self.max_len - len(text)
        return [self.char_to_idx.get(ch, 1) for ch in text] + [0] * pad_len

    def get_vocabulary(self):
        return self.vocab


max_target_len = 200  # all transcripts in out data are < 200 characters
data = get_data(wavs, id_to_text, max_target_len)
vectorizer = VectorizeChar(max_target_len)
print("vocab size", len(vectorizer.get_vocabulary()))


def create_text_ds(data):
    texts = [_["text"] for _ in data]
    text_ds = [vectorizer(t) for t in texts]
    text_ds = tf.data.Dataset.from_tensor_slices(text_ds)
    return text_ds


def path_to_audio(path):
    # spectrogram using stft
    audio = tf.io.read_file(path)
    audio, _ = tf.audio.decode_wav(audio, 1)
    audio = tf.squeeze(audio, axis=-1)
    stfts = tf.signal.stft(audio, frame_length=200, frame_step=80, fft_length=256)
    x = tf.math.pow(tf.abs(stfts), 0.5)
    # normalisation
    means = tf.math.reduce_mean(x, 1, keepdims=True)
    stddevs = tf.math.reduce_std(x, 1, keepdims=True)
    x = (x - means) / stddevs
    audio_len = tf.shape(x)[0]
    # padding to 10 seconds
    pad_len = 2754
    paddings = tf.constant([[0, pad_len], [0, 0]])
    x = tf.pad(x, paddings, "CONSTANT")[:pad_len, :]
    return x


def create_audio_ds(data):
    flist = [_["audio"] for _ in data]
    audio_ds = tf.data.Dataset.from_tensor_slices(flist)
    audio_ds = audio_ds.map(path_to_audio, num_parallel_calls=tf.data.AUTOTUNE)
    return audio_ds


def create_tf_dataset(data, bs=4):
    audio_ds = create_audio_ds(data)
    text_ds = create_text_ds(data)
    ds = tf.data.Dataset.zip((audio_ds, text_ds))
    ds = ds.map(lambda x, y: {"source": x, "target": y})
    ds = ds.batch(bs)
    ds = ds.prefetch(tf.data.AUTOTUNE)
    return ds


split = int(len(data) * 0.99)
train_data = data[:split]
test_data = data[split:]
ds = create_tf_dataset(train_data, bs=64)
val_ds = create_tf_dataset(test_data, bs=4)

"""
## Callbacks to display predictions
"""


class DisplayOutputs(keras.callbacks.Callback):
    def __init__(
        self, batch, idx_to_token, target_start_token_idx=27, target_end_token_idx=28
    ):
        """Displays a batch of outputs after every epoch

        Args:
            batch: A test batch containing the keys "source" and "target"
            idx_to_token: A List containing the vocabulary tokens corresponding to their indices
            target_start_token_idx: A start token index in the target vocabulary
            target_end_token_idx: An end token index in the target vocabulary
        """
        self.batch = batch
        self.target_start_token_idx = target_start_token_idx
        self.target_end_token_idx = target_end_token_idx
        self.idx_to_char = idx_to_token

    def on_epoch_end(self, epoch, logs=None):
        if epoch % 5 != 0:
            return
        source = self.batch["source"]
        target = self.batch["target"].numpy()
        bs = tf.shape(source)[0]
        preds = self.model.generate(source, self.target_start_token_idx)
        preds = preds.numpy()
        for i in range(bs):
            target_text = "".join([self.idx_to_char[_] for _ in target[i, :]])
            prediction = ""
            for idx in preds[i, :]:
                prediction += self.idx_to_char[idx]
                if idx == self.target_end_token_idx:
                    break
            print(f"target:     {target_text.replace('-','')}")
            print(f"prediction: {prediction}\n")


"""
## Learning rate schedule
"""


class CustomSchedule(keras.optimizers.schedules.LearningRateSchedule):
    def __init__(
        self,
        init_lr=0.00001,
        lr_after_warmup=0.001,
        final_lr=0.00001,
        warmup_epochs=15,
        decay_epochs=85,
        steps_per_epoch=203,
    ):
        super().__init__()
        self.init_lr = init_lr
        self.lr_after_warmup = lr_after_warmup
        self.final_lr = final_lr
        self.warmup_epochs = warmup_epochs
        self.decay_epochs = decay_epochs
        self.steps_per_epoch = steps_per_epoch

    def calculate_lr(self, epoch):
        """linear warm up - linear decay"""
        warmup_lr = (
            self.init_lr
            + ((self.lr_after_warmup - self.init_lr) / (self.warmup_epochs - 1)) * epoch
        )
        decay_lr = tf.math.maximum(
            self.final_lr,
            self.lr_after_warmup
            - (epoch - self.warmup_epochs)
            * (self.lr_after_warmup - self.final_lr)
            / self.decay_epochs,
        )
        return tf.math.minimum(warmup_lr, decay_lr)

    def __call__(self, step):
        epoch = step // self.steps_per_epoch
        return self.calculate_lr(epoch)


"""
## Create & train the end-to-end model
"""

batch = next(iter(val_ds))

# The vocabulary to convert predicted indices into characters
idx_to_char = vectorizer.get_vocabulary()
display_cb = DisplayOutputs(
    batch, idx_to_char, target_start_token_idx=2, target_end_token_idx=3
)  # set the arguments as per vocabulary index for '<' and '>'

model = Transformer(
    num_hid=200,
    num_head=2,
    num_feed_forward=400,
    target_maxlen=max_target_len,
    num_layers_enc=4,
    num_layers_dec=1,
    num_classes=34,
)
loss_fn = tf.keras.losses.CategoricalCrossentropy(
    from_logits=True,
    label_smoothing=0.1,
)

learning_rate = CustomSchedule(
    init_lr=0.00001,
    lr_after_warmup=0.001,
    final_lr=0.00001,
    warmup_epochs=15,
    decay_epochs=85,
    steps_per_epoch=len(ds),
)
optimizer = keras.optimizers.Adam(learning_rate)
model.compile(optimizer=optimizer, loss=loss_fn)

history = model.fit(ds, validation_data=val_ds, callbacks=[display_cb], epochs=1)

"""
In practice, you should train for around 100 epochs or more.

Some of the predicted text at or around epoch 35 may look as follows:
```
target:     <as they sat in the car, frazier asked oswald where his lunch was>
prediction: <as they sat in the car frazier his lunch ware mis lunch was>

target:     <under the entry for may one, nineteen sixty,>
prediction: <under the introus for may monee, nin the sixty,>
```
"""
