HenryAI/KerasCodeExamples.txt · Datasets at Hugging Face

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Training a CTC-based model for automatic speech recognition.

Introduction
Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields.

This demonstration shows how to combine a 2D CNN, RNN and a Connectionist Temporal Classification (CTC) loss to build an ASR. CTC is an algorithm used to train deep neural networks in speech recognition, handwriting recognition and other sequence problems. CTC is used when we don’t know how the input aligns with the output (how the characters in the transcript align to the audio). The model we create is similar to DeepSpeech2.

We will use the LJSpeech dataset from the LibriVox project. It consists of short audio clips of a single speaker reading passages from 7 non-fiction books.

We will evaluate the quality of the model using Word Error Rate (WER). WER is obtained by adding up the substitutions, insertions, and deletions that occur in a sequence of recognized words. Divide that number by the total number of words originally spoken. The result is the WER. To get the WER score you need to install the jiwer package. You can use the following command line:

pip install jiwer
References:

LJSpeech Dataset
Speech recognition
Sequence Modeling With CTC
DeepSpeech2
Setup
import pandas as pd
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
from IPython import display
from jiwer import wer
Load the LJSpeech Dataset
Let's download the LJSpeech Dataset. The dataset contains 13,100 audio files as wav files in the /wavs/ folder. The label (transcript) for each audio file is a string given in the metadata.csv file. The fields are:

ID: this is the name of the corresponding .wav file
Transcription: words spoken by the reader (UTF-8)
Normalized transcription: transcription with numbers, ordinals, and monetary units expanded into full words (UTF-8).
For this demo we will use on the \"Normalized transcription\" field.

Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22,050 Hz.

data_url = \"https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2\"
data_path = keras.utils.get_file(\"LJSpeech-1.1\", data_url, untar=True)
wavs_path = data_path + \"/wavs/\"
metadata_path = data_path + \"/metadata.csv\"


# Read metadata file and parse it
metadata_df = pd.read_csv(metadata_path, sep=\"\|\", header=None, quoting=3)
metadata_df.columns = [\"file_name\", \"transcription\", \"normalized_transcription\"]
metadata_df = metadata_df[[\"file_name\", \"normalized_transcription\"]]
metadata_df = metadata_df.sample(frac=1).reset_index(drop=True)
metadata_df.head(3)
file_name normalized_transcription
0 LJ042-0218 to the entire land and complete foundations of...
1 LJ004-0218 a week's allowance at a time, was abolished, a...
2 LJ005-0151 in others women were very properly exempted fr...
We now split the data into training and validation set.

split = int(len(metadata_df) * 0.90)
df_train = metadata_df[:split]
df_val = metadata_df[split:]

print(f\"Size of the training set: {len(df_train)}\")
print(f\"Size of the training set: {len(df_val)}\")
Size of the training set: 11790
Size of the training set: 1310
Preprocessing
We first prepare the vocabulary to be used.

# The set of characters accepted in the transcription.
characters = [x for x in \"abcdefghijklmnopqrstuvwxyz'?! \"]
# Mapping characters to integers
char_to_num = keras.layers.StringLookup(vocabulary=characters, oov_token=\"\")
# Mapping integers back to original characters
num_to_char = keras.layers.StringLookup(
vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True
)

print(
f\"The vocabulary is: {char_to_num.get_vocabulary()} \"
f\"(size ={char_to_num.vocabulary_size()})\"
)
The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', ' '] (size =31)

2021-09-28 21:16:33.150832: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-09-28 21:16:33.692813: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2021-09-28 21:16:33.692847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9124 MB memory: -> device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:65:00.0, compute capability: 7.5
Next, we create the function that describes the transformation that we apply to each element of our dataset.

# An integer scalar Tensor. The window length in samples.
frame_length = 256
# An integer scalar Tensor. The number of samples to step.
frame_step = 160
# An integer scalar Tensor. The size of the FFT to apply.
# If not provided, uses the smallest power of 2 enclosing frame_length.
fft_length = 384


def encode_single_sample(wav_file, label):
###########################################
## Process the Audio
##########################################

Training a CTC-based model for automatic speech recognition.

Introduction

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields.

This demonstration shows how to combine a 2D CNN, RNN and a Connectionist Temporal Classification (CTC) loss to build an ASR. CTC is an algorithm used to train deep neural networks in speech recognition, handwriting recognition and other sequence problems. CTC is used when we don’t know how the input aligns with the output (how the characters in the transcript align to the audio). The model we create is similar to DeepSpeech2.

We will use the LJSpeech dataset from the LibriVox project. It consists of short audio clips of a single speaker reading passages from 7 non-fiction books.

We will evaluate the quality of the model using Word Error Rate (WER). WER is obtained by adding up the substitutions, insertions, and deletions that occur in a sequence of recognized words. Divide that number by the total number of words originally spoken. The result is the WER. To get the WER score you need to install the jiwer package. You can use the following command line:

pip install jiwer

References:

LJSpeech Dataset

Speech recognition

Sequence Modeling With CTC

DeepSpeech2

Setup

import pandas as pd

import numpy as np

import tensorflow as tf

from tensorflow import keras

from tensorflow.keras import layers

import matplotlib.pyplot as plt

from IPython import display

from jiwer import wer

Load the LJSpeech Dataset

Let's download the LJSpeech Dataset. The dataset contains 13,100 audio files as wav files in the /wavs/ folder. The label (transcript) for each audio file is a string given in the metadata.csv file. The fields are:

ID: this is the name of the corresponding .wav file

Transcription: words spoken by the reader (UTF-8)

Normalized transcription: transcription with numbers, ordinals, and monetary units expanded into full words (UTF-8).

For this demo we will use on the \"Normalized transcription\" field.

Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22,050 Hz.

data_url = \"https://data.keithito.com/data/speech/LJSpeech-1.1.tar.bz2\"

data_path = keras.utils.get_file(\"LJSpeech-1.1\", data_url, untar=True)

wavs_path = data_path + \"/wavs/\"

metadata_path = data_path + \"/metadata.csv\"

# Read metadata file and parse it

metadata_df = pd.read_csv(metadata_path, sep=\"|\", header=None, quoting=3)

metadata_df.columns = [\"file_name\", \"transcription\", \"normalized_transcription\"]

metadata_df = metadata_df[[\"file_name\", \"normalized_transcription\"]]

metadata_df = metadata_df.sample(frac=1).reset_index(drop=True)

metadata_df.head(3)

file_name normalized_transcription

0 LJ042-0218 to the entire land and complete foundations of...

1 LJ004-0218 a week's allowance at a time, was abolished, a...

2 LJ005-0151 in others women were very properly exempted fr...

We now split the data into training and validation set.

split = int(len(metadata_df) * 0.90)

df_train = metadata_df[:split]

df_val = metadata_df[split:]

print(f\"Size of the training set: {len(df_train)}\")

print(f\"Size of the training set: {len(df_val)}\")

Size of the training set: 11790

Size of the training set: 1310

Preprocessing

We first prepare the vocabulary to be used.

# The set of characters accepted in the transcription.

characters = [x for x in \"abcdefghijklmnopqrstuvwxyz'?! \"]

# Mapping characters to integers

char_to_num = keras.layers.StringLookup(vocabulary=characters, oov_token=\"\")

# Mapping integers back to original characters

num_to_char = keras.layers.StringLookup(

vocabulary=char_to_num.get_vocabulary(), oov_token=\"\", invert=True

)

print(

f\"The vocabulary is: {char_to_num.get_vocabulary()} \"

f\"(size ={char_to_num.vocabulary_size()})\"

)

The vocabulary is: ['', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', \"'\", '?', '!', ' '] (size =31)

2021-09-28 21:16:33.150832: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA

To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2021-09-28 21:16:33.692813: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.

2021-09-28 21:16:33.692847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 9124 MB memory: -> device: 0, name: GeForce RTX 2080 Ti, pci bus id: 0000:65:00.0, compute capability: 7.5

Next, we create the function that describes the transformation that we apply to each element of our dataset.

# An integer scalar Tensor. The window length in samples.

frame_length = 256

# An integer scalar Tensor. The number of samples to step.

frame_step = 160

# An integer scalar Tensor. The size of the FFT to apply.

# If not provided, uses the smallest power of 2 enclosing frame_length.

fft_length = 384

def encode_single_sample(wav_file, label):

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

## Process the Audio

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