HenryAI/KerasBERTv1-Data · Datasets at Hugging Face

text stringlengths 0 4.99k
prediction: <the iaio the t h aint oohe te te an he t te o e t as e t t he te the the o t t ie o so o te o the te s s t tre olin o o oon cnt theaie to o te s te o soo hete te tte o e the th s oas pe te the ad
target: <in all of these roles the president must go to the people.>
prediction: <the iaio the t h aint oohe te te an he t te o e t as e t t he te the the o t t ie o so o te o the te s s t tre olin o o oon cnt theaie to o te s te o soo hete te tte o e the th s oas pe te the ad
target: <and to have succeeded in other speculations.>
prediction: <the iaio the t h aint oohe te te an he t te o e t as e t t he te the the o t t ie o so o te o the te s s t tre olin o o oon cnt theaie to o te s te o soo hete te tte o e the th s oas pe te the ad
target: <and which certainly hold good for the vast majority of animals and plants, are of universal application.>
prediction: <the iaio the t h aint oohe te te an he t te o e t as e t t he te the the o t t ie o s s t te o the te s s t tre olin o o oon cnt theaie to o te s te o soo hete te tte o e the th s oas pe te the ad
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,>

Inversion of audio from mel-spectograms using the MelGAN architecture and feature matching.

Introduction
Autoregressive vocoders have been ubiquitous for a majority of the history of speech processing, but for most of their existence they have lacked parallelism. MelGAN is a non-autoregressive, fully convolutional vocoder architecture used for purposes ranging from spectral inversion and speech enhancement to present-day state-of-the-art speech synthesis when used as a decoder with models like Tacotron2 or FastSpeech that convert text to mel spectrograms.

In this tutorial, we will have a look at the MelGAN architecture and how it can achieve fast spectral inversion, i.e. conversion of spectrograms to audio waves. The MelGAN implemented in this tutorial is similar to the original implementation with only the difference of method of padding for convolutions where we will use 'same' instead of reflect padding.

Importing and Defining Hyperparameters
!pip install -qqq tensorflow_addons
!pip install -qqq tensorflow-io
import tensorflow as tf
import tensorflow_io as tfio
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow_addons import layers as addon_layers

# Setting logger level to avoid input shape warnings
tf.get_logger().setLevel(\"ERROR\")

# Defining hyperparameters

DESIRED_SAMPLES = 8192
LEARNING_RATE_GEN = 1e-5
LEARNING_RATE_DISC = 1e-6
BATCH_SIZE = 16

mse = keras.losses.MeanSquaredError()
mae = keras.losses.MeanAbsoluteError()
\|████████████████████████████████\| 1.1 MB 5.1 MB/s
\|████████████████████████████████\| 22.7 MB 1.7 MB/s
\|████████████████████████████████\| 2.1 MB 36.2 MB/s
Loading the Dataset
This example uses the LJSpeech dataset.

The LJSpeech dataset is primarily used for text-to-speech and consists of 13,100 discrete speech samples taken from 7 non-fiction books, having a total length of approximately 24 hours. The MelGAN training is only concerned with the audio waves so we process only the WAV files and ignore the audio annotations.

!wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
!tar -xf /content/LJSpeech-1.1.tar.bz2
--2021-09-16 11:45:24-- https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2
Resolving data.keithito.com (data.keithito.com)... 174.138.79.61
Connecting to data.keithito.com (data.keithito.com)\|174.138.79.61\|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 2748572632 (2.6G) [application/octet-stream]
Saving to: ‘LJSpeech-1.1.tar.bz2’
LJSpeech-1.1.tar.bz 100%[===================>] 2.56G 68.3MB/s in 36s
2021-09-16 11:46:01 (72.2 MB/s) - ‘LJSpeech-1.1.tar.bz2’ saved [2748572632/2748572632]
We create a tf.data.Dataset to load and process the audio files on the fly. The preprocess() function takes the file path as input and returns two instances of the wave, one for input and one as the ground truth for comparsion. The input wave will be mapped to a spectrogram using the custom MelSpec layer as shown later in this example.

# Splitting the dataset into training and testing splits
wavs = tf.io.gfile.glob(\"LJSpeech-1.1/wavs/*.wav\")
print(f\"Number of audio files: {len(wavs)}\")

# Mapper function for loading the audio. This function returns two instances of the wave
def preprocess(filename):
audio = tf.audio.decode_wav(tf.io.read_file(filename), 1, DESIRED_SAMPLES).audio
return audio, audio


# Create tf.data.Dataset objects and apply preprocessing
train_dataset = tf.data.Dataset.from_tensor_slices((wavs,))
train_dataset = train_dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)
Number of audio files: 13100
Defining custom layers for MelGAN
The MelGAN architecture consists of 3 main modules:

The residual block
Dilated convolutional block
Discriminator block
MelGAN

Since the network takes a mel-spectrogram as input, we will create an additional custom layer which can convert the raw audio wave to a spectrogram on-the-fly. We use the raw audio tensor from train_dataset and map it to a mel-spectrogram using the MelSpec layer below.

# Custom keras layer for on-the-fly audio to spectrogram conversion


class MelSpec(layers.Layer):
def __init__(
self,
frame_length=1024,
frame_step=256,
fft_length=None,
sampling_rate=22050,
num_mel_channels=80,

prediction: <the iaio the t h aint oohe te te an he t te o e t as e t t he te the the o t t ie o so o te o the te s s t tre olin o o oon cnt theaie to o te s te o soo hete te tte o e the th s oas pe te the ad

target: <in all of these roles the president must go to the people.>

prediction: <the iaio the t h aint oohe te te an he t te o e t as e t t he te the the o t t ie o so o te o the te s s t tre olin o o oon cnt theaie to o te s te o soo hete te tte o e the th s oas pe te the ad

target: <and to have succeeded in other speculations.>

prediction: <the iaio the t h aint oohe te te an he t te o e t as e t t he te the the o t t ie o so o te o the te s s t tre olin o o oon cnt theaie to o te s te o soo hete te tte o e the th s oas pe te the ad

target: <and which certainly hold good for the vast majority of animals and plants, are of universal application.>

prediction: <the iaio the t h aint oohe te te an he t te o e t as e t t he te the the o t t ie o s s t te o the te s s t tre olin o o oon cnt theaie to o te s te o soo hete te tte o e the th s oas pe te the ad

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,>

Inversion of audio from mel-spectograms using the MelGAN architecture and feature matching.

Introduction

Autoregressive vocoders have been ubiquitous for a majority of the history of speech processing, but for most of their existence they have lacked parallelism. MelGAN is a non-autoregressive, fully convolutional vocoder architecture used for purposes ranging from spectral inversion and speech enhancement to present-day state-of-the-art speech synthesis when used as a decoder with models like Tacotron2 or FastSpeech that convert text to mel spectrograms.

In this tutorial, we will have a look at the MelGAN architecture and how it can achieve fast spectral inversion, i.e. conversion of spectrograms to audio waves. The MelGAN implemented in this tutorial is similar to the original implementation with only the difference of method of padding for convolutions where we will use 'same' instead of reflect padding.

Importing and Defining Hyperparameters

!pip install -qqq tensorflow_addons

!pip install -qqq tensorflow-io

import tensorflow as tf

import tensorflow_io as tfio

from tensorflow import keras

from tensorflow.keras import layers

from tensorflow_addons import layers as addon_layers

# Setting logger level to avoid input shape warnings

tf.get_logger().setLevel(\"ERROR\")

# Defining hyperparameters

DESIRED_SAMPLES = 8192

LEARNING_RATE_GEN = 1e-5

LEARNING_RATE_DISC = 1e-6

BATCH_SIZE = 16

mse = keras.losses.MeanSquaredError()

mae = keras.losses.MeanAbsoluteError()

|████████████████████████████████| 1.1 MB 5.1 MB/s

|████████████████████████████████| 22.7 MB 1.7 MB/s

|████████████████████████████████| 2.1 MB 36.2 MB/s

Loading the Dataset

This example uses the LJSpeech dataset.

The LJSpeech dataset is primarily used for text-to-speech and consists of 13,100 discrete speech samples taken from 7 non-fiction books, having a total length of approximately 24 hours. The MelGAN training is only concerned with the audio waves so we process only the WAV files and ignore the audio annotations.

!wget https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2

!tar -xf /content/LJSpeech-1.1.tar.bz2

--2021-09-16 11:45:24-- https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2

Resolving data.keithito.com (data.keithito.com)... 174.138.79.61

Connecting to data.keithito.com (data.keithito.com)|174.138.79.61|:443... connected.

HTTP request sent, awaiting response... 200 OK

Length: 2748572632 (2.6G) [application/octet-stream]

Saving to: ‘LJSpeech-1.1.tar.bz2’

LJSpeech-1.1.tar.bz 100%[===================>] 2.56G 68.3MB/s in 36s

2021-09-16 11:46:01 (72.2 MB/s) - ‘LJSpeech-1.1.tar.bz2’ saved [2748572632/2748572632]

We create a tf.data.Dataset to load and process the audio files on the fly. The preprocess() function takes the file path as input and returns two instances of the wave, one for input and one as the ground truth for comparsion. The input wave will be mapped to a spectrogram using the custom MelSpec layer as shown later in this example.

# Splitting the dataset into training and testing splits

wavs = tf.io.gfile.glob(\"LJSpeech-1.1/wavs/*.wav\")

print(f\"Number of audio files: {len(wavs)}\")

# Mapper function for loading the audio. This function returns two instances of the wave

def preprocess(filename):

audio = tf.audio.decode_wav(tf.io.read_file(filename), 1, DESIRED_SAMPLES).audio

return audio, audio

# Create tf.data.Dataset objects and apply preprocessing

train_dataset = tf.data.Dataset.from_tensor_slices((wavs,))

train_dataset = train_dataset.map(preprocess, num_parallel_calls=tf.data.AUTOTUNE)

Number of audio files: 13100

Defining custom layers for MelGAN

The MelGAN architecture consists of 3 main modules:

The residual block

Dilated convolutional block

Discriminator block

MelGAN

Since the network takes a mel-spectrogram as input, we will create an additional custom layer which can convert the raw audio wave to a spectrogram on-the-fly. We use the raw audio tensor from train_dataset and map it to a mel-spectrogram using the MelSpec layer below.

# Custom keras layer for on-the-fly audio to spectrogram conversion

class MelSpec(layers.Layer):

def __init__(

self,

frame_length=1024,

frame_step=256,

fft_length=None,

sampling_rate=22050,

num_mel_channels=80,