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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) |
---------------------------------------------------------------------------------------------------- |
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) |