Datasets:
HenryAI
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KerasCodeExamples.txt

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for i in np.random.randint(0, len(predictions), 5):
print(f\"Target : {targets[i]}\")
print(f\"Prediction: {predictions[i]}\")
print(\"-\" * 100)
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Word Error Rate: 0.9998
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Target : two of the nine agents returned to their rooms the seven others proceeded to an establishment called the cellar coffee house
Prediction:
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Target : a scaffold was erected in front of that prison for the execution of several convicts named by the recorder
Prediction: sss
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Target : it was perpetrated upon a respectable country solicitor
Prediction: ss
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Target : oswald like all marine recruits received training on the rifle range at distances up to five hundred yards
Prediction:
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Target : chief rowley testified that agents on duty in such a situation usually stay within the building during their relief
Prediction: s
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Conclusion
In practice, you should train for around 50 epochs or more. Each epoch takes approximately 5-6mn using a GeForce RTX 2080 Ti GPU. The model we trained at 50 epochs has a Word Error Rate (WER) ≈ 16% to 17%.
Some of the transcriptions around epoch 50:
Audio file: LJ017-0009.wav
- Target : sir thomas overbury was undoubtedly poisoned by lord rochester in the reign
of james the first
- Prediction: cer thomas overbery was undoubtedly poisoned by lordrochester in the reign
of james the first
Audio file: LJ003-0340.wav
- Target : the committee does not seem to have yet understood that newgate could be
only and properly replaced
- Prediction: the committee does not seem to have yet understood that newgate could be
only and proberly replace
Audio file: LJ011-0136.wav
- Target : still no sentence of death was carried out for the offense and in eighteen
thirtytwo
- Prediction: still no sentence of death was carried out for the offense and in eighteen
thirtytwo
Training a sequence-to-sequence Transformer for automatic speech recognition.
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 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
Very Deep Self-Attention Networks for End-to-End Speech Recognition
Speech Transformers
LJSpeech 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 stides) 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\"
)
self.pos_emb = layers.Embedding(input_dim=maxlen, output_dim=num_hid)
def call(self, x):
x = self.conv1(x)