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1
+ {
2
+ "cells": [
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+ {
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+ "cell_type": "markdown",
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+ "id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6",
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+ "metadata": {
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+ "id": "75b58048-7d14-4fc6-8085-1fc08c81b4a6"
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+ },
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+ "source": [
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+ "# Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers"
11
+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "fbfa8ad5-4cdc-4512-9058-836cbbf65e1a",
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+ "metadata": {
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+ "id": "fbfa8ad5-4cdc-4512-9058-836cbbf65e1a"
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+ },
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+ "source": [
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+ "In this Colab, we present a step-by-step guide on how to fine-tune Whisper \n",
21
+ "for any multilingual ASR dataset using Hugging Face 🤗 Transformers. This is a \n",
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+ "more \"hands-on\" version of the accompanying [blog post](https://huggingface.co/blog/fine-tune-whisper). \n",
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+ "For a more in-depth explanation of Whisper, the Common Voice dataset and the theory behind fine-tuning, the reader is advised to refer to the blog post."
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+ ]
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+ },
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+ {
27
+ "cell_type": "markdown",
28
+ "id": "afe0d503-ae4e-4aa7-9af4-dbcba52db41e",
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+ "metadata": {
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+ "id": "afe0d503-ae4e-4aa7-9af4-dbcba52db41e"
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+ },
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+ "source": [
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+ "## Introduction"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0",
39
+ "metadata": {
40
+ "id": "9ae91ed4-9c3e-4ade-938e-f4c2dcfbfdc0"
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+ },
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+ "source": [
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+ "Whisper is a pre-trained model for automatic speech recognition (ASR) \n",
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+ "published in [September 2022](https://openai.com/blog/whisper/) by the authors \n",
45
+ "Alec Radford et al. from OpenAI. Unlike many of its predecessors, such as \n",
46
+ "[Wav2Vec 2.0](https://arxiv.org/abs/2006.11477), which are pre-trained \n",
47
+ "on un-labelled audio data, Whisper is pre-trained on a vast quantity of \n",
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+ "**labelled** audio-transcription data, 680,000 hours to be precise. \n",
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+ "This is an order of magnitude more data than the un-labelled audio data used \n",
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+ "to train Wav2Vec 2.0 (60,000 hours). What is more, 117,000 hours of this \n",
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+ "pre-training data is multilingual ASR data. This results in checkpoints \n",
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+ "that can be applied to over 96 languages, many of which are considered \n",
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+ "_low-resource_.\n",
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+ "\n",
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+ "When scaled to 680,000 hours of labelled pre-training data, Whisper models \n",
56
+ "demonstrate a strong ability to generalise to many datasets and domains.\n",
57
+ "The pre-trained checkpoints achieve competitive results to state-of-the-art \n",
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+ "ASR systems, with near 3% word error rate (WER) on the test-clean subset of \n",
59
+ "LibriSpeech ASR and a new state-of-the-art on TED-LIUM with 4.7% WER (_c.f._ \n",
60
+ "Table 8 of the [Whisper paper](https://cdn.openai.com/papers/whisper.pdf)).\n",
61
+ "The extensive multilingual ASR knowledge acquired by Whisper during pre-training \n",
62
+ "can be leveraged for other low-resource languages; through fine-tuning, the \n",
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+ "pre-trained checkpoints can be adapted for specific datasets and languages \n",
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+ "to further improve upon these results. We'll show just how Whisper can be fine-tuned \n",
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+ "for low-resource languages in this Colab."
66
+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "e59b91d6-be24-4b5e-bb38-4977ea143a72",
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+ "metadata": {
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+ "id": "e59b91d6-be24-4b5e-bb38-4977ea143a72"
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+ },
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+ "source": [
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+ "<figure>\n",
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+ "<img src=\"https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/whisper_architecture.svg\" alt=\"Trulli\" style=\"width:100%\">\n",
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+ "<figcaption align = \"center\"><b>Figure 1:</b> Whisper model. The architecture \n",
78
+ "follows the standard Transformer-based encoder-decoder model. A \n",
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+ "log-Mel spectrogram is input to the encoder. The last encoder \n",
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+ "hidden states are input to the decoder via cross-attention mechanisms. The \n",
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+ "decoder autoregressively predicts text tokens, jointly conditional on the \n",
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+ "encoder hidden states and previously predicted tokens. Figure source: \n",
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+ "<a href=\"https://openai.com/blog/whisper/\">OpenAI Whisper Blog</a>.</figcaption>\n",
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+ "</figure>"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "21b6316e-8a55-4549-a154-66d3da2ab74a",
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+ "metadata": {
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+ "id": "21b6316e-8a55-4549-a154-66d3da2ab74a"
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+ },
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+ "source": [
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+ "The Whisper checkpoints come in five configurations of varying model sizes.\n",
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+ "The smallest four are trained on either English-only or multilingual data.\n",
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+ "The largest checkpoint is multilingual only. All nine of the pre-trained checkpoints \n",
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+ "are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The \n",
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+ "checkpoints are summarised in the following table with links to the models on the Hub:\n",
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+ "\n",
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+ "| Size | Layers | Width | Heads | Parameters | English-only | Multilingual |\n",
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+ "|--------|--------|-------|-------|------------|------------------------------------------------------|---------------------------------------------------|\n",
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+ "| tiny | 4 | 384 | 6 | 39 M | [✓](https://huggingface.co/openai/whisper-tiny.en) | [✓](https://huggingface.co/openai/whisper-tiny.) |\n",
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+ "| base | 6 | 512 | 8 | 74 M | [✓](https://huggingface.co/openai/whisper-base.en) | [✓](https://huggingface.co/openai/whisper-base) |\n",
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+ "| small | 12 | 768 | 12 | 244 M | [✓](https://huggingface.co/openai/whisper-small.en) | [✓](https://huggingface.co/openai/whisper-small) |\n",
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+ "| medium | 24 | 1024 | 16 | 769 M | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |\n",
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+ "| large | 32 | 1280 | 20 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large) |\n",
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+ "\n",
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+ "For demonstration purposes, we'll fine-tune the multilingual version of the \n",
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+ "[`\"small\"`](https://huggingface.co/openai/whisper-small) checkpoint with 244M params (~= 1GB). \n",
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+ "As for our data, we'll train and evaluate our system on a low-resource language \n",
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+ "taken from the [Common Voice](https://huggingface.co/datasets/mozilla-foundation/fleurs_11_0)\n",
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+ "dataset. We'll show that with as little as 8 hours of fine-tuning data, we can achieve \n",
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+ "strong performance in this language."
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "3a680dfc-cbba-4f6c-8a1f-e1a5ff3f123a",
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+ "metadata": {
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+ "id": "3a680dfc-cbba-4f6c-8a1f-e1a5ff3f123a"
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+ },
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+ "source": [
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+ "------------------------------------------------------------------------\n",
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+ "\n",
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+ "\\\\({}^1\\\\) The name Whisper follows from the acronym “WSPSR”, which stands for “Web-scale Supervised Pre-training for Speech Recognition”."
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0",
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+ "metadata": {
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+ "id": "b219c9dd-39b6-4a95-b2a1-3f547a1e7bc0"
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+ },
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+ "source": [
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+ "## Load Dataset\n",
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+ "Loading MS-MY Dataset from FLEURS.\n",
137
+ "Combine train and validation set."
138
+ ]
139
+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "id": "a2787582-554f-44ce-9f38-4180a5ed6b44",
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+ "metadata": {
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+ "id": "a2787582-554f-44ce-9f38-4180a5ed6b44"
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+ },
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+ "outputs": [],
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+ "source": [
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+ "from datasets import load_dataset, DatasetDict\n",
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+ "\n",
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+ "# fleurs = DatasetDict()\n",
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+ "# fleurs[\"train\"] = load_dataset(\"google/fleurs\", \"id_id\", split=\"train+validation\", use_auth_token=True)\n",
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+ "# fleurs[\"test\"] = load_dataset(\"google/fleurs\", \"id_id\", split=\"test\", use_auth_token=True)\n",
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+ "\n",
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+ "# fleurs = fleurs.remove_columns([\"id\", \"num_samples\", \"path\", \"raw_transcription\", \"gender\", \"lang_id\", \"language\", \"lang_group_id\"])\n",
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+ "\n",
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+ "# print(fleurs)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "id": "d087b451",
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+ "metadata": {
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+ "scrolled": false
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+ },
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "Found cached dataset common_voice_11_0 (/home/ubuntu/.cache/huggingface/datasets/mozilla-foundation___common_voice_11_0/yue/11.0.0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f)\n",
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+ "Found cached dataset common_voice_11_0 (/home/ubuntu/.cache/huggingface/datasets/mozilla-foundation___common_voice_11_0/yue/11.0.0/f8e47235d9b4e68fa24ed71d63266a02018ccf7194b2a8c9c598a5f3ab304d9f)\n"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "DatasetDict({\n",
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+ " train: Dataset({\n",
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+ " features: ['audio', 'transcription'],\n",
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+ " num_rows: 5296\n",
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+ " })\n",
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+ " test: Dataset({\n",
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+ " features: ['audio', 'transcription'],\n",
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+ " num_rows: 2438\n",
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+ " })\n",
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+ "})\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "cv = DatasetDict()\n",
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+ "cv[\"train\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"yue\", split=\"train+validation\", use_auth_token=True)\n",
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+ "cv[\"test\"] = load_dataset(\"mozilla-foundation/common_voice_11_0\", \"yue\", split=\"test\", use_auth_token=True)\n",
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+ "\n",
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+ "cv = cv.remove_columns([\"client_id\", \"path\", 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'])\n",
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+ "cv = cv.rename_column('sentence', 'transcription')\n",
200
+ "print(cv)"
201
+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 3,
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+ "id": "f681bfef",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "# lbv = DatasetDict()\n",
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+ "# lbv[\"train\"] = load_dataset(\"indonesian-nlp/librivox-indonesia\", \"ind\", split=\"train\", use_auth_token=True)\n",
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+ "# lbv[\"test\"] = load_dataset(\"indonesian-nlp/librivox-indonesia\", \"ind\", split=\"test\", use_auth_token=True)\n",
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+ "\n",
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+ "# lbv = lbv.remove_columns([\"path\", \"language\", \"reader\"])\n",
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+ "# lbv = lbv.rename_column('sentence', 'transcription')\n",
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+ "# print(lbv)"
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+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605",
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+ "metadata": {
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+ "id": "2d63b2d2-f68a-4d74-b7f1-5127f6d16605"
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+ },
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+ "source": [
226
+ "## Prepare Feature Extractor, Tokenizer and Data"
227
+ ]
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+ },
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+ {
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+ "cell_type": "markdown",
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+ "id": "601c3099-1026-439e-93e2-5635b3ba5a73",
232
+ "metadata": {
233
+ "id": "601c3099-1026-439e-93e2-5635b3ba5a73"
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+ },
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+ "source": [
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+ "The ASR pipeline can be de-composed into three stages: \n",
237
+ "1) A feature extractor which pre-processes the raw audio-inputs\n",
238
+ "2) The model which performs the sequence-to-sequence mapping \n",
239
+ "3) A tokenizer which post-processes the model outputs to text format\n",
240
+ "\n",
241
+ "In 🤗 Transformers, the Whisper model has an associated feature extractor and tokenizer, \n",
242
+ "called [WhisperFeatureExtractor](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperFeatureExtractor)\n",
243
+ "and [WhisperTokenizer](https://huggingface.co/docs/transformers/main/model_doc/whisper#transformers.WhisperTokenizer) \n",
244
+ "respectively.\n",
245
+ "\n",
246
+ "We'll go through details for setting-up the feature extractor and tokenizer one-by-one!"
247
+ ]
248
+ },
249
+ {
250
+ "cell_type": "markdown",
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+ "id": "560332eb-3558-41a1-b500-e83a9f695f84",
252
+ "metadata": {
253
+ "id": "560332eb-3558-41a1-b500-e83a9f695f84"
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+ },
255
+ "source": [
256
+ "### Load WhisperFeatureExtractor"
257
+ ]
258
+ },
259
+ {
260
+ "cell_type": "markdown",
261
+ "id": "32ec8068-0bd7-412d-b662-0edb9d1e7365",
262
+ "metadata": {
263
+ "id": "32ec8068-0bd7-412d-b662-0edb9d1e7365"
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+ },
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+ "source": [
266
+ "The Whisper feature extractor performs two operations:\n",
267
+ "1. Pads / truncates the audio inputs to 30s: any audio inputs shorter than 30s are padded to 30s with silence (zeros), and those longer that 30s are truncated to 30s\n",
268
+ "2. Converts the audio inputs to _log-Mel spectrogram_ input features, a visual representation of the audio and the form of the input expected by the Whisper model"
269
+ ]
270
+ },
271
+ {
272
+ "cell_type": "markdown",
273
+ "id": "589d9ec1-d12b-4b64-93f7-04c63997da19",
274
+ "metadata": {
275
+ "id": "589d9ec1-d12b-4b64-93f7-04c63997da19"
276
+ },
277
+ "source": [
278
+ "<figure>\n",
279
+ "<img src=\"https://raw.githubusercontent.com/sanchit-gandhi/notebooks/main/spectrogram.jpg\" alt=\"Trulli\" style=\"width:100%\">\n",
280
+ "<figcaption align = \"center\"><b>Figure 2:</b> Conversion of sampled audio array to log-Mel spectrogram.\n",
281
+ "Left: sampled 1-dimensional audio signal. Right: corresponding log-Mel spectrogram. Figure source:\n",
282
+ "<a href=\"https://ai.googleblog.com/2019/04/specaugment-new-data-augmentation.html\">Google SpecAugment Blog</a>.\n",
283
+ "</figcaption>"
284
+ ]
285
+ },
286
+ {
287
+ "cell_type": "markdown",
288
+ "id": "b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa",
289
+ "metadata": {
290
+ "id": "b2ef54d5-b946-4c1d-9fdc-adc5d01b46aa"
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+ },
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+ "source": [
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+ "We'll load the feature extractor from the pre-trained checkpoint with the default values:"
294
+ ]
295
+ },
296
+ {
297
+ "cell_type": "code",
298
+ "execution_count": 4,
299
+ "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5",
300
+ "metadata": {
301
+ "id": "bc77d7bb-f9e2-47f5-b663-30f7a4321ce5"
302
+ },
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+ "outputs": [],
304
+ "source": [
305
+ "from transformers import WhisperFeatureExtractor\n",
306
+ "\n",
307
+ "feature_extractor = WhisperFeatureExtractor.from_pretrained(\"openai/whisper-large-v2\")"
308
+ ]
309
+ },
310
+ {
311
+ "cell_type": "markdown",
312
+ "id": "93748af7-b917-4ecf-a0c8-7d89077ff9cb",
313
+ "metadata": {
314
+ "id": "93748af7-b917-4ecf-a0c8-7d89077ff9cb"
315
+ },
316
+ "source": [
317
+ "### Load WhisperTokenizer"
318
+ ]
319
+ },
320
+ {
321
+ "cell_type": "markdown",
322
+ "id": "2bc82609-a9fb-447a-a2af-99597c864029",
323
+ "metadata": {
324
+ "id": "2bc82609-a9fb-447a-a2af-99597c864029"
325
+ },
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+ "source": [
327
+ "The Whisper model outputs a sequence of _token ids_. The tokenizer maps each of these token ids to their corresponding text string. For Hindi, we can load the pre-trained tokenizer and use it for fine-tuning without any further modifications. We simply have to \n",
328
+ "specify the target language and the task. These arguments inform the \n",
329
+ "tokenizer to prefix the language and task tokens to the start of encoded \n",
330
+ "label sequences:"
331
+ ]
332
+ },
333
+ {
334
+ "cell_type": "code",
335
+ "execution_count": 13,
336
+ "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
337
+ "metadata": {
338
+ "id": "c7b07f9b-ae0e-4f89-98f0-0c50d432eab6",
339
+ "outputId": "5c004b44-86e7-4e00-88be-39e0af5eed69"
340
+ },
341
+ "outputs": [],
342
+ "source": [
343
+ "from transformers import WhisperTokenizer\n",
344
+ "\n",
345
+ "tokenizer = WhisperTokenizer.from_pretrained(\"openai/whisper-large-v2\", language=\"Chinese\", task=\"transcribe\")"
346
+ ]
347
+ },
348
+ {
349
+ "cell_type": "markdown",
350
+ "id": "d2ef23f3-f4a8-483a-a2dc-080a7496cb1b",
351
+ "metadata": {
352
+ "id": "d2ef23f3-f4a8-483a-a2dc-080a7496cb1b"
353
+ },
354
+ "source": [
355
+ "### Combine To Create A WhisperProcessor"
356
+ ]
357
+ },
358
+ {
359
+ "cell_type": "markdown",
360
+ "id": "5ff67654-5a29-4bb8-a69d-0228946c6f8d",
361
+ "metadata": {
362
+ "id": "5ff67654-5a29-4bb8-a69d-0228946c6f8d"
363
+ },
364
+ "source": [
365
+ "To simplify using the feature extractor and tokenizer, we can _wrap_ \n",
366
+ "both into a single `WhisperProcessor` class. This processor object \n",
367
+ "inherits from the `WhisperFeatureExtractor` and `WhisperProcessor`, \n",
368
+ "and can be used on the audio inputs and model predictions as required. \n",
369
+ "In doing so, we only need to keep track of two objects during training: \n",
370
+ "the `processor` and the `model`:"
371
+ ]
372
+ },
373
+ {
374
+ "cell_type": "code",
375
+ "execution_count": 14,
376
+ "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6",
377
+ "metadata": {
378
+ "id": "77d9f0c5-8607-4642-a8ac-c3ab2e223ea6"
379
+ },
380
+ "outputs": [],
381
+ "source": [
382
+ "from transformers import WhisperProcessor\n",
383
+ "\n",
384
+ "processor = WhisperProcessor.from_pretrained(\"openai/whisper-large-v2\", language=\"Chinese\", task=\"transcribe\")"
385
+ ]
386
+ },
387
+ {
388
+ "cell_type": "markdown",
389
+ "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c",
390
+ "metadata": {
391
+ "id": "381acd09-0b0f-4d04-9eb3-f028ac0e5f2c"
392
+ },
393
+ "source": [
394
+ "### Prepare Data"
395
+ ]
396
+ },
397
+ {
398
+ "cell_type": "code",
399
+ "execution_count": 7,
400
+ "id": "c69246a2",
401
+ "metadata": {},
402
+ "outputs": [],
403
+ "source": [
404
+ "from datasets import Audio\n",
405
+ "\n",
406
+ "cv = cv.cast_column(\"audio\", Audio(sampling_rate=16000))\n",
407
+ "# fleurs = fleurs.cast_column(\"audio\", Audio(sampling_rate=16000))\n",
408
+ "# lbv = lbv.cast_column(\"audio\", Audio(sampling_rate=16000))"
409
+ ]
410
+ },
411
+ {
412
+ "cell_type": "markdown",
413
+ "id": "3df7378a-a4c0-45d7-8d07-defbd1062ab6",
414
+ "metadata": {},
415
+ "source": [
416
+ "We'll define our pre-processing strategy. We advise that you **do not** lower-case the transcriptions or remove punctuation unless mixing different datasets. This will enable you to fine-tune Whisper models that can predict punctuation and casing. Later, you will see how we can evaluate the predictions without punctuation or casing, so that the models benefit from the WER improvement obtained by normalising the transcriptions while still predicting fully formatted transcriptions."
417
+ ]
418
+ },
419
+ {
420
+ "cell_type": "code",
421
+ "execution_count": 8,
422
+ "id": "d041650e-1c48-4439-87b3-5b6f4a514107",
423
+ "metadata": {},
424
+ "outputs": [],
425
+ "source": [
426
+ "from transformers.models.whisper.english_normalizer import BasicTextNormalizer\n",
427
+ "\n",
428
+ "do_lower_case = False\n",
429
+ "do_remove_punctuation = False\n",
430
+ "\n",
431
+ "normalizer = BasicTextNormalizer()"
432
+ ]
433
+ },
434
+ {
435
+ "cell_type": "markdown",
436
+ "id": "89e12c2e-2f14-479b-987b-f0c75c881095",
437
+ "metadata": {},
438
+ "source": [
439
+ "Now we can write a function to prepare our data ready for the model:\n",
440
+ "1. We load and resample the audio data by calling `batch[\"audio\"]`. As explained above, 🤗 Datasets performs any necessary resampling operations on the fly.\n",
441
+ "2. We use the feature extractor to compute the log-Mel spectrogram input features from our 1-dimensional audio array.\n",
442
+ "3. We perform any optional pre-processing (lower-case or remove punctuation).\n",
443
+ "4. We encode the transcriptions to label ids through the use of the tokenizer."
444
+ ]
445
+ },
446
+ {
447
+ "cell_type": "code",
448
+ "execution_count": 9,
449
+ "id": "4c79b333",
450
+ "metadata": {
451
+ "scrolled": false
452
+ },
453
+ "outputs": [],
454
+ "source": [
455
+ "from audiomentations import Compose, AddGaussianNoise, TimeStretch, PitchShift\n",
456
+ "\n",
457
+ "augment_waveform = Compose([\n",
458
+ "# AddGaussianNoise(min_amplitude=0.005, max_amplitude=0.015, p=0.3),\n",
459
+ " TimeStretch(min_rate=0.9, max_rate=1.25, p=0.3, leave_length_unchanged=False),\n",
460
+ "# PitchShift(min_semitones=-4, max_semitones=4, p=0.3),\n",
461
+ " ])\n",
462
+ "\n",
463
+ "def augment_dataset(batch):\n",
464
+ "\n",
465
+ " audio = batch[\"audio\"][\"array\"]\n",
466
+ " # apply augmentation\n",
467
+ " augmented_audio = augment_waveform(samples=audio, sample_rate=16000)\n",
468
+ "\n",
469
+ " batch[\"audio\"][\"array\"] = augmented_audio\n",
470
+ "\n",
471
+ " return batch"
472
+ ]
473
+ },
474
+ {
475
+ "cell_type": "code",
476
+ "execution_count": 15,
477
+ "id": "c085911c-a10a-41ef-8874-306e0503e9bb",
478
+ "metadata": {},
479
+ "outputs": [],
480
+ "source": [
481
+ "def prepare_dataset(batch):\n",
482
+ " # load and (possibly) resample audio data to 16kHz\n",
483
+ " audio = batch[\"audio\"]\n",
484
+ "\n",
485
+ " # compute log-Mel input features from input audio array \n",
486
+ " batch[\"input_features\"] = processor.feature_extractor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_features[0]\n",
487
+ " # compute input length of audio sample in seconds\n",
488
+ " batch[\"input_length\"] = len(audio[\"array\"]) / audio[\"sampling_rate\"]\n",
489
+ " \n",
490
+ " # optional pre-processing steps\n",
491
+ " transcription = batch[\"transcription\"]\n",
492
+ " if do_lower_case:\n",
493
+ " transcription = transcription.lower()\n",
494
+ " if do_remove_punctuation:\n",
495
+ " transcription = normalizer(transcription).strip()\n",
496
+ " \n",
497
+ " # encode target text to label ids\n",
498
+ " batch[\"labels\"] = processor.tokenizer(transcription).input_ids\n",
499
+ " return batch"
500
+ ]
501
+ },
502
+ {
503
+ "cell_type": "markdown",
504
+ "id": "8c960965-9fb6-466f-9dbd-c9d43e71d9d0",
505
+ "metadata": {
506
+ "id": "70b319fb-2439-4ef6-a70d-a47bf41c4a13"
507
+ },
508
+ "source": [
509
+ "We can apply the data preparation function to all of our training examples using dataset's `.map` method. The argument `num_proc` specifies how many CPU cores to use. Setting `num_proc` > 1 will enable multiprocessing. If the `.map` method hangs with multiprocessing, set `num_proc=1` and process the dataset sequentially."
510
+ ]
511
+ },
512
+ {
513
+ "cell_type": "code",
514
+ "execution_count": 11,
515
+ "id": "db271164",
516
+ "metadata": {},
517
+ "outputs": [
518
+ {
519
+ "data": {
520
+ "application/vnd.jupyter.widget-view+json": {
521
+ "model_id": "f9683eb693c449c19719183b46b54fbb",
522
+ "version_major": 2,
523
+ "version_minor": 0
524
+ },
525
+ "text/plain": [
526
+ " 0%| | 0/5296 [00:00<?, ?ex/s]"
527
+ ]
528
+ },
529
+ "metadata": {},
530
+ "output_type": "display_data"
531
+ }
532
+ ],
533
+ "source": [
534
+ "# fleurs['train'] = fleurs['train'].map(augment_dataset)\n",
535
+ "cv['train'] = cv['train'].map(augment_dataset)\n",
536
+ "# lbv['train'] = lbv['train'].map(augment_dataset)"
537
+ ]
538
+ },
539
+ {
540
+ "cell_type": "code",
541
+ "execution_count": 16,
542
+ "id": "b459b0c5",
543
+ "metadata": {},
544
+ "outputs": [
545
+ {
546
+ "data": {
547
+ "application/vnd.jupyter.widget-view+json": {
548
+ "model_id": "7bd2c16519ff4309a00299e19cce5365",
549
+ "version_major": 2,
550
+ "version_minor": 0
551
+ },
552
+ "text/plain": [
553
+ " 0%| | 0/5296 [00:00<?, ?ex/s]"
554
+ ]
555
+ },
556
+ "metadata": {},
557
+ "output_type": "display_data"
558
+ },
559
+ {
560
+ "data": {
561
+ "application/vnd.jupyter.widget-view+json": {
562
+ "model_id": "82fcd95608b24d5794fa063e7d3686bb",
563
+ "version_major": 2,
564
+ "version_minor": 0
565
+ },
566
+ "text/plain": [
567
+ " 0%| | 0/2438 [00:00<?, ?ex/s]"
568
+ ]
569
+ },
570
+ "metadata": {},
571
+ "output_type": "display_data"
572
+ }
573
+ ],
574
+ "source": [
575
+ "# fleurs = fleurs.map(prepare_dataset, remove_columns=fleurs.column_names['train'], num_proc=1).with_format(\"torch\")\n",
576
+ "cv = cv.map(prepare_dataset, remove_columns=cv.column_names['train'], num_proc=1).with_format(\"torch\")\n",
577
+ "# lbv = lbv.map(prepare_dataset, remove_columns=lbv.column_names['train'], num_proc=1).with_format(\"torch\")"
578
+ ]
579
+ },
580
+ {
581
+ "cell_type": "code",
582
+ "execution_count": 20,
583
+ "id": "e9034b52",
584
+ "metadata": {},
585
+ "outputs": [],
586
+ "source": [
587
+ "# from datasets import concatenate_datasets\n",
588
+ "\n",
589
+ "# cc = DatasetDict()\n",
590
+ "# cc['train'] = concatenate_datasets([fleurs['train'], cv['train'], lbv['train']])\n",
591
+ "# cc['test'] = cv['test']"
592
+ ]
593
+ },
594
+ {
595
+ "cell_type": "markdown",
596
+ "id": "54ce0fdb-7218-4a4d-b175-383980fec0df",
597
+ "metadata": {},
598
+ "source": [
599
+ "Finally, we filter any training data with audio samples longer than 30s. These samples would otherwise be truncated by the Whisper feature-extractor which could affect the stability of training. We define a function that returns `True` for samples that are less than 30s, and `False` for those that are longer:"
600
+ ]
601
+ },
602
+ {
603
+ "cell_type": "code",
604
+ "execution_count": 17,
605
+ "id": "01cb25ef-4bb0-4325-9461-f59198acadf6",
606
+ "metadata": {},
607
+ "outputs": [],
608
+ "source": [
609
+ "max_input_length = 30.0\n",
610
+ "\n",
611
+ "def is_audio_in_length_range(length):\n",
612
+ " return length < max_input_length"
613
+ ]
614
+ },
615
+ {
616
+ "cell_type": "markdown",
617
+ "id": "30e676a8-7ca8-4850-8c5d-5b2b00d13fba",
618
+ "metadata": {},
619
+ "source": [
620
+ "We apply our filter function to all samples of our training dataset through 🤗 Datasets' `.filter` method:"
621
+ ]
622
+ },
623
+ {
624
+ "cell_type": "code",
625
+ "execution_count": 18,
626
+ "id": "333f7f6e-6053-4d3b-8924-c733c79b82ac",
627
+ "metadata": {},
628
+ "outputs": [
629
+ {
630
+ "data": {
631
+ "application/vnd.jupyter.widget-view+json": {
632
+ "model_id": "518997e4f5324bdf82b4906be1c550c9",
633
+ "version_major": 2,
634
+ "version_minor": 0
635
+ },
636
+ "text/plain": [
637
+ " 0%| | 0/6 [00:00<?, ?ba/s]"
638
+ ]
639
+ },
640
+ "metadata": {},
641
+ "output_type": "display_data"
642
+ }
643
+ ],
644
+ "source": [
645
+ "cv['train'] = cv['train'].filter(\n",
646
+ " is_audio_in_length_range,\n",
647
+ " input_columns=[\"input_length\"],\n",
648
+ ")"
649
+ ]
650
+ },
651
+ {
652
+ "cell_type": "markdown",
653
+ "id": "263a5a58-0239-4a25-b0df-c625fc9c5810",
654
+ "metadata": {
655
+ "id": "263a5a58-0239-4a25-b0df-c625fc9c5810"
656
+ },
657
+ "source": [
658
+ "## Training and Evaluation"
659
+ ]
660
+ },
661
+ {
662
+ "cell_type": "markdown",
663
+ "id": "a693e768-c5a6-453f-89a1-b601dcf7daf7",
664
+ "metadata": {
665
+ "id": "a693e768-c5a6-453f-89a1-b601dcf7daf7"
666
+ },
667
+ "source": [
668
+ "Now that we've prepared our data, we're ready to dive into the training pipeline. \n",
669
+ "The [🤗 Trainer](https://huggingface.co/transformers/master/main_classes/trainer.html?highlight=trainer)\n",
670
+ "will do much of the heavy lifting for us. All we have to do is:\n",
671
+ "\n",
672
+ "- Define a data collator: the data collator takes our pre-processed data and prepares PyTorch tensors ready for the model.\n",
673
+ "\n",
674
+ "- Evaluation metrics: during evaluation, we want to evaluate the model using the [word error rate (WER)](https://huggingface.co/metrics/wer) metric. We need to define a `compute_metrics` function that handles this computation.\n",
675
+ "\n",
676
+ "- Load a pre-trained checkpoint: we need to load a pre-trained checkpoint and configure it correctly for training.\n",
677
+ "\n",
678
+ "- Define the training configuration: this will be used by the 🤗 Trainer to define the training schedule.\n",
679
+ "\n",
680
+ "Once we've fine-tuned the model, we will evaluate it on the test data to verify that we have correctly trained it \n",
681
+ "to transcribe speech in Hindi."
682
+ ]
683
+ },
684
+ {
685
+ "cell_type": "markdown",
686
+ "id": "8d230e6d-624c-400a-bbf5-fa660881df25",
687
+ "metadata": {
688
+ "id": "8d230e6d-624c-400a-bbf5-fa660881df25"
689
+ },
690
+ "source": [
691
+ "### Define a Data Collator"
692
+ ]
693
+ },
694
+ {
695
+ "cell_type": "markdown",
696
+ "id": "04def221-0637-4a69-b242-d3f0c1d0ee78",
697
+ "metadata": {
698
+ "id": "04def221-0637-4a69-b242-d3f0c1d0ee78"
699
+ },
700
+ "source": [
701
+ "The data collator for a sequence-to-sequence speech model is unique in the sense that it \n",
702
+ "treats the `input_features` and `labels` independently: the `input_features` must be \n",
703
+ "handled by the feature extractor and the `labels` by the tokenizer.\n",
704
+ "\n",
705
+ "The `input_features` are already padded to 30s and converted to a log-Mel spectrogram \n",
706
+ "of fixed dimension by action of the feature extractor, so all we have to do is convert the `input_features`\n",
707
+ "to batched PyTorch tensors. We do this using the feature extractor's `.pad` method with `return_tensors=pt`.\n",
708
+ "\n",
709
+ "The `labels` on the other hand are un-padded. We first pad the sequences\n",
710
+ "to the maximum length in the batch using the tokenizer's `.pad` method. The padding tokens \n",
711
+ "are then replaced by `-100` so that these tokens are **not** taken into account when \n",
712
+ "computing the loss. We then cut the BOS token from the start of the label sequence as we \n",
713
+ "append it later during training.\n",
714
+ "\n",
715
+ "We can leverage the `WhisperProcessor` we defined earlier to perform both the \n",
716
+ "feature extractor and the tokenizer operations:"
717
+ ]
718
+ },
719
+ {
720
+ "cell_type": "code",
721
+ "execution_count": 19,
722
+ "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5",
723
+ "metadata": {
724
+ "id": "8326221e-ec13-4731-bb4e-51e5fc1486c5"
725
+ },
726
+ "outputs": [],
727
+ "source": [
728
+ "import torch\n",
729
+ "\n",
730
+ "from dataclasses import dataclass\n",
731
+ "from typing import Any, Dict, List, Union\n",
732
+ "\n",
733
+ "@dataclass\n",
734
+ "class DataCollatorSpeechSeq2SeqWithPadding:\n",
735
+ " processor: Any\n",
736
+ "\n",
737
+ " def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
738
+ " # split inputs and labels since they have to be of different lengths and need different padding methods\n",
739
+ " # first treat the audio inputs by simply returning torch tensors\n",
740
+ " input_features = [{\"input_features\": feature[\"input_features\"]} for feature in features]\n",
741
+ " batch = self.processor.feature_extractor.pad(input_features, return_tensors=\"pt\")\n",
742
+ "\n",
743
+ " # get the tokenized label sequences\n",
744
+ " label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
745
+ " # pad the labels to max length\n",
746
+ " labels_batch = self.processor.tokenizer.pad(label_features, return_tensors=\"pt\")\n",
747
+ "\n",
748
+ " # replace padding with -100 to ignore loss correctly\n",
749
+ " labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
750
+ "\n",
751
+ " # if bos token is appended in previous tokenization step,\n",
752
+ " # cut bos token here as it's append later anyways\n",
753
+ " if (labels[:, 0] == self.processor.tokenizer.bos_token_id).all().cpu().item():\n",
754
+ " labels = labels[:, 1:]\n",
755
+ "\n",
756
+ " batch[\"labels\"] = labels\n",
757
+ "\n",
758
+ " return batch"
759
+ ]
760
+ },
761
+ {
762
+ "cell_type": "markdown",
763
+ "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86",
764
+ "metadata": {
765
+ "id": "3cae7dbf-8a50-456e-a3a8-7fd005390f86"
766
+ },
767
+ "source": [
768
+ "Let's initialise the data collator we've just defined:"
769
+ ]
770
+ },
771
+ {
772
+ "cell_type": "code",
773
+ "execution_count": 20,
774
+ "id": "fc834702-c0d3-4a96-b101-7b87be32bf42",
775
+ "metadata": {
776
+ "id": "fc834702-c0d3-4a96-b101-7b87be32bf42"
777
+ },
778
+ "outputs": [],
779
+ "source": [
780
+ "data_collator = DataCollatorSpeechSeq2SeqWithPadding(processor=processor)"
781
+ ]
782
+ },
783
+ {
784
+ "cell_type": "markdown",
785
+ "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698",
786
+ "metadata": {
787
+ "id": "d62bb2ab-750a-45e7-82e9-61d6f4805698"
788
+ },
789
+ "source": [
790
+ "### Evaluation Metrics"
791
+ ]
792
+ },
793
+ {
794
+ "cell_type": "markdown",
795
+ "id": "66fee1a7-a44c-461e-b047-c3917221572e",
796
+ "metadata": {
797
+ "id": "66fee1a7-a44c-461e-b047-c3917221572e"
798
+ },
799
+ "source": [
800
+ "We'll use the word error rate (WER) metric, the 'de-facto' metric for assessing \n",
801
+ "ASR systems. For more information, refer to the WER [docs](https://huggingface.co/metrics/wer). We'll load the WER metric from 🤗 Evaluate:"
802
+ ]
803
+ },
804
+ {
805
+ "cell_type": "code",
806
+ "execution_count": 21,
807
+ "id": "b22b4011-f31f-4b57-b684-c52332f92890",
808
+ "metadata": {
809
+ "id": "b22b4011-f31f-4b57-b684-c52332f92890"
810
+ },
811
+ "outputs": [
812
+ {
813
+ "data": {
814
+ "application/vnd.jupyter.widget-view+json": {
815
+ "model_id": "2da06d3741bf4cee961e3352d32a2515",
816
+ "version_major": 2,
817
+ "version_minor": 0
818
+ },
819
+ "text/plain": [
820
+ "Downloading builder script: 0%| | 0.00/5.60k [00:00<?, ?B/s]"
821
+ ]
822
+ },
823
+ "metadata": {},
824
+ "output_type": "display_data"
825
+ }
826
+ ],
827
+ "source": [
828
+ "import evaluate\n",
829
+ "\n",
830
+ "# wer_metric = evaluate.load(\"wer\")\n",
831
+ "cer_metric = evaluate.load(\"cer\")"
832
+ ]
833
+ },
834
+ {
835
+ "cell_type": "markdown",
836
+ "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508",
837
+ "metadata": {
838
+ "id": "4f32cab6-31f0-4cb9-af4c-40ba0f5fc508"
839
+ },
840
+ "source": [
841
+ "We then simply have to define a function that takes our model \n",
842
+ "predictions and returns the WER metric. This function, called\n",
843
+ "`compute_metrics`, first replaces `-100` with the `pad_token_id`\n",
844
+ "in the `label_ids` (undoing the step we applied in the \n",
845
+ "data collator to ignore padded tokens correctly in the loss).\n",
846
+ "It then decodes the predicted and label ids to strings. Finally,\n",
847
+ "it computes the WER between the predictions and reference labels. \n",
848
+ "Here, we have the option of evaluating with the 'normalised' transcriptions \n",
849
+ "and predictions. We recommend you set this to `True` to benefit from the WER \n",
850
+ "improvement obtained by normalising the transcriptions."
851
+ ]
852
+ },
853
+ {
854
+ "cell_type": "code",
855
+ "execution_count": 22,
856
+ "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52",
857
+ "metadata": {
858
+ "id": "23959a70-22d0-4ffe-9fa1-72b61e75bb52"
859
+ },
860
+ "outputs": [],
861
+ "source": [
862
+ "# evaluate with the 'normalised' WER\n",
863
+ "do_normalize_eval = True\n",
864
+ "\n",
865
+ "def compute_metrics(pred):\n",
866
+ " pred_ids = pred.predictions\n",
867
+ " label_ids = pred.label_ids\n",
868
+ "\n",
869
+ " # replace -100 with the pad_token_id\n",
870
+ " label_ids[label_ids == -100] = processor.tokenizer.pad_token_id\n",
871
+ "\n",
872
+ " # we do not want to group tokens when computing the metrics\n",
873
+ " pred_str = processor.tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
874
+ " label_str = processor.tokenizer.batch_decode(label_ids, skip_special_tokens=True)\n",
875
+ "\n",
876
+ " if do_normalize_eval:\n",
877
+ " pred_str = [normalizer(pred) for pred in pred_str]\n",
878
+ " label_str = [normalizer(label) for label in label_str]\n",
879
+ "\n",
880
+ "# wer = 100 * wer_metric.compute(predictions=pred_str, references=label_str)\n",
881
+ " cer = 100 * cer_metric.compute(predictions=pred_str, references=label_str)\n",
882
+ "\n",
883
+ " return {\"cer\": cer}"
884
+ ]
885
+ },
886
+ {
887
+ "cell_type": "markdown",
888
+ "id": "daf2a825-6d9f-4a23-b145-c37c0039075b",
889
+ "metadata": {
890
+ "id": "daf2a825-6d9f-4a23-b145-c37c0039075b"
891
+ },
892
+ "source": [
893
+ "### Load a Pre-Trained Checkpoint"
894
+ ]
895
+ },
896
+ {
897
+ "cell_type": "markdown",
898
+ "id": "437a97fa-4864-476b-8abc-f28b8166cfa5",
899
+ "metadata": {
900
+ "id": "437a97fa-4864-476b-8abc-f28b8166cfa5"
901
+ },
902
+ "source": [
903
+ "Now let's load the pre-trained Whisper `small` checkpoint. Again, this \n",
904
+ "is trivial through use of 🤗 Transformers!"
905
+ ]
906
+ },
907
+ {
908
+ "cell_type": "code",
909
+ "execution_count": 23,
910
+ "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f",
911
+ "metadata": {
912
+ "id": "5a10cc4b-07ec-4ebd-ac1d-7c601023594f"
913
+ },
914
+ "outputs": [
915
+ {
916
+ "data": {
917
+ "application/vnd.jupyter.widget-view+json": {
918
+ "model_id": "d119f8acca8d46ed942a725edd74e680",
919
+ "version_major": 2,
920
+ "version_minor": 0
921
+ },
922
+ "text/plain": [
923
+ "Downloading: 0%| | 0.00/1.97k [00:00<?, ?B/s]"
924
+ ]
925
+ },
926
+ "metadata": {},
927
+ "output_type": "display_data"
928
+ },
929
+ {
930
+ "data": {
931
+ "application/vnd.jupyter.widget-view+json": {
932
+ "model_id": "7a70c500788546058ac4e13bced80f4d",
933
+ "version_major": 2,
934
+ "version_minor": 0
935
+ },
936
+ "text/plain": [
937
+ "Downloading: 0%| | 0.00/6.17G [00:00<?, ?B/s]"
938
+ ]
939
+ },
940
+ "metadata": {},
941
+ "output_type": "display_data"
942
+ }
943
+ ],
944
+ "source": [
945
+ "from transformers import WhisperForConditionalGeneration\n",
946
+ "\n",
947
+ "model = WhisperForConditionalGeneration.from_pretrained(\"openai/whisper-large-v2\")"
948
+ ]
949
+ },
950
+ {
951
+ "cell_type": "markdown",
952
+ "id": "a15ead5f-2277-4a39-937b-585c2497b2df",
953
+ "metadata": {
954
+ "id": "a15ead5f-2277-4a39-937b-585c2497b2df"
955
+ },
956
+ "source": [
957
+ "Override generation arguments - no tokens are forced as decoder outputs (see [`forced_decoder_ids`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.forced_decoder_ids)), no tokens are suppressed during generation (see [`suppress_tokens`](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.generation_utils.GenerationMixin.generate.suppress_tokens)). Set `use_cache` to False since we're using gradient checkpointing, and the two are incompatible:"
958
+ ]
959
+ },
960
+ {
961
+ "cell_type": "code",
962
+ "execution_count": 24,
963
+ "id": "62038ba3-88ed-4fce-84db-338f50dcd04f",
964
+ "metadata": {
965
+ "id": "62038ba3-88ed-4fce-84db-338f50dcd04f"
966
+ },
967
+ "outputs": [],
968
+ "source": [
969
+ "model.config.forced_decoder_ids = None\n",
970
+ "model.config.suppress_tokens = []\n",
971
+ "model.config.use_cache = False"
972
+ ]
973
+ },
974
+ {
975
+ "cell_type": "markdown",
976
+ "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06",
977
+ "metadata": {
978
+ "id": "2178dea4-80ca-47b6-b6ea-ba1915c90c06"
979
+ },
980
+ "source": [
981
+ "### Define the Training Configuration"
982
+ ]
983
+ },
984
+ {
985
+ "cell_type": "markdown",
986
+ "id": "c21af1e9-0188-4134-ac82-defc7bdcc436",
987
+ "metadata": {
988
+ "id": "c21af1e9-0188-4134-ac82-defc7bdcc436"
989
+ },
990
+ "source": [
991
+ "In the final step, we define all the parameters related to training. For more detail on the training arguments, refer to the Seq2SeqTrainingArguments [docs](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments)."
992
+ ]
993
+ },
994
+ {
995
+ "cell_type": "code",
996
+ "execution_count": 29,
997
+ "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a",
998
+ "metadata": {
999
+ "id": "0ae3e9af-97b7-4aa0-ae85-20b23b5bcb3a"
1000
+ },
1001
+ "outputs": [
1002
+ {
1003
+ "name": "stderr",
1004
+ "output_type": "stream",
1005
+ "text": [
1006
+ "PyTorch: setting up devices\n"
1007
+ ]
1008
+ }
1009
+ ],
1010
+ "source": [
1011
+ "from transformers import Seq2SeqTrainingArguments\n",
1012
+ "\n",
1013
+ "training_args = Seq2SeqTrainingArguments(\n",
1014
+ " output_dir=\"./\",\n",
1015
+ " per_device_train_batch_size=8,\n",
1016
+ " gradient_accumulation_steps=4, # increase by 2x for every 2x decrease in batch size\n",
1017
+ " learning_rate=1e-5,\n",
1018
+ " warmup_steps=100,\n",
1019
+ " max_steps=1000,\n",
1020
+ " gradient_checkpointing=True,\n",
1021
+ " fp16=True,\n",
1022
+ " evaluation_strategy=\"steps\",\n",
1023
+ " per_device_eval_batch_size=4,\n",
1024
+ " predict_with_generate=True,\n",
1025
+ " generation_max_length=225,\n",
1026
+ " save_steps=200,\n",
1027
+ " eval_steps=200,\n",
1028
+ " logging_steps=25,\n",
1029
+ " report_to=[\"tensorboard\"],\n",
1030
+ " load_best_model_at_end=True,\n",
1031
+ " metric_for_best_model=\"cer\",\n",
1032
+ " greater_is_better=False,\n",
1033
+ " push_to_hub=True,\n",
1034
+ ")"
1035
+ ]
1036
+ },
1037
+ {
1038
+ "cell_type": "markdown",
1039
+ "id": "b3a944d8-3112-4552-82a0-be25988b3857",
1040
+ "metadata": {
1041
+ "id": "b3a944d8-3112-4552-82a0-be25988b3857"
1042
+ },
1043
+ "source": [
1044
+ "**Note**: if one does not want to upload the model checkpoints to the Hub, \n",
1045
+ "set `push_to_hub=False`."
1046
+ ]
1047
+ },
1048
+ {
1049
+ "cell_type": "markdown",
1050
+ "id": "bac29114-d226-4f54-97cf-8718c9f94e1e",
1051
+ "metadata": {
1052
+ "id": "bac29114-d226-4f54-97cf-8718c9f94e1e"
1053
+ },
1054
+ "source": [
1055
+ "We can forward the training arguments to the 🤗 Trainer along with our model,\n",
1056
+ "dataset, data collator and `compute_metrics` function:"
1057
+ ]
1058
+ },
1059
+ {
1060
+ "cell_type": "code",
1061
+ "execution_count": 30,
1062
+ "id": "d546d7fe-0543-479a-b708-2ebabec19493",
1063
+ "metadata": {
1064
+ "id": "d546d7fe-0543-479a-b708-2ebabec19493"
1065
+ },
1066
+ "outputs": [
1067
+ {
1068
+ "name": "stderr",
1069
+ "output_type": "stream",
1070
+ "text": [
1071
+ "/home/ubuntu/whisper-large-v2-yue/./ is already a clone of https://huggingface.co/Scrya/whisper-large-v2-yue. Make sure you pull the latest changes with `repo.git_pull()`.\n",
1072
+ "max_steps is given, it will override any value given in num_train_epochs\n",
1073
+ "Using cuda_amp half precision backend\n"
1074
+ ]
1075
+ }
1076
+ ],
1077
+ "source": [
1078
+ "from transformers import Seq2SeqTrainer\n",
1079
+ "\n",
1080
+ "trainer = Seq2SeqTrainer(\n",
1081
+ " args=training_args,\n",
1082
+ " model=model,\n",
1083
+ " train_dataset=cv['train'],\n",
1084
+ " eval_dataset=cv['test'],\n",
1085
+ " data_collator=data_collator,\n",
1086
+ " compute_metrics=compute_metrics,\n",
1087
+ " tokenizer=processor.feature_extractor,\n",
1088
+ ")"
1089
+ ]
1090
+ },
1091
+ {
1092
+ "cell_type": "markdown",
1093
+ "id": "uOrRhDGtN5S4",
1094
+ "metadata": {
1095
+ "id": "uOrRhDGtN5S4"
1096
+ },
1097
+ "source": [
1098
+ "We'll save the processor object once before starting training. Since the processor is not trainable, it won't change over the course of training:"
1099
+ ]
1100
+ },
1101
+ {
1102
+ "cell_type": "code",
1103
+ "execution_count": 31,
1104
+ "id": "-2zQwMfEOBJq",
1105
+ "metadata": {
1106
+ "id": "-2zQwMfEOBJq"
1107
+ },
1108
+ "outputs": [
1109
+ {
1110
+ "name": "stderr",
1111
+ "output_type": "stream",
1112
+ "text": [
1113
+ "Feature extractor saved in ./preprocessor_config.json\n",
1114
+ "tokenizer config file saved in ./tokenizer_config.json\n",
1115
+ "Special tokens file saved in ./special_tokens_map.json\n",
1116
+ "added tokens file saved in ./added_tokens.json\n"
1117
+ ]
1118
+ }
1119
+ ],
1120
+ "source": [
1121
+ "processor.save_pretrained(training_args.output_dir)"
1122
+ ]
1123
+ },
1124
+ {
1125
+ "cell_type": "markdown",
1126
+ "id": "7f404cf9-4345-468c-8196-4bd101d9bd51",
1127
+ "metadata": {
1128
+ "id": "7f404cf9-4345-468c-8196-4bd101d9bd51"
1129
+ },
1130
+ "source": [
1131
+ "### Training"
1132
+ ]
1133
+ },
1134
+ {
1135
+ "cell_type": "markdown",
1136
+ "id": "5e8b8d56-5a70-4f68-bd2e-f0752d0bd112",
1137
+ "metadata": {
1138
+ "id": "5e8b8d56-5a70-4f68-bd2e-f0752d0bd112"
1139
+ },
1140
+ "source": [
1141
+ "Training will take approximately 5-10 hours depending on your GPU. The peak GPU memory for the given training configuration is approximately 36GB. \n",
1142
+ "Depending on your GPU, it is possible that you will encounter a CUDA `\"out-of-memory\"` error when you launch training. \n",
1143
+ "In this case, you can reduce the `per_device_train_batch_size` incrementally by factors of 2 \n",
1144
+ "and employ [`gradient_accumulation_steps`](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.Seq2SeqTrainingArguments.gradient_accumulation_steps)\n",
1145
+ "to compensate.\n",
1146
+ "\n",
1147
+ "To launch training, simply execute:"
1148
+ ]
1149
+ },
1150
+ {
1151
+ "cell_type": "code",
1152
+ "execution_count": null,
1153
+ "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
1154
+ "metadata": {
1155
+ "id": "ee8b7b8e-1c9a-4d77-9137-1778a629e6de",
1156
+ "scrolled": true
1157
+ },
1158
+ "outputs": [
1159
+ {
1160
+ "name": "stderr",
1161
+ "output_type": "stream",
1162
+ "text": [
1163
+ "The following columns in the training set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`, you can safely ignore this message.\n",
1164
+ "***** Running training *****\n",
1165
+ " Num examples = 5296\n",
1166
+ " Num Epochs = 7\n",
1167
+ " Instantaneous batch size per device = 8\n",
1168
+ " Total train batch size (w. parallel, distributed & accumulation) = 32\n",
1169
+ " Gradient Accumulation steps = 4\n",
1170
+ " Total optimization steps = 1000\n",
1171
+ " Number of trainable parameters = 1543304960\n"
1172
+ ]
1173
+ },
1174
+ {
1175
+ "data": {
1176
+ "text/html": [
1177
+ "\n",
1178
+ " <div>\n",
1179
+ " \n",
1180
+ " <progress value='11' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
1181
+ " [ 11/1000 01:03 < 1:55:52, 0.14 it/s, Epoch 0.06/7]\n",
1182
+ " </div>\n",
1183
+ " <table border=\"1\" class=\"dataframe\">\n",
1184
+ " <thead>\n",
1185
+ " <tr style=\"text-align: left;\">\n",
1186
+ " <th>Step</th>\n",
1187
+ " <th>Training Loss</th>\n",
1188
+ " <th>Validation Loss</th>\n",
1189
+ " </tr>\n",
1190
+ " </thead>\n",
1191
+ " <tbody>\n",
1192
+ " </tbody>\n",
1193
+ "</table><p>"
1194
+ ],
1195
+ "text/plain": [
1196
+ "<IPython.core.display.HTML object>"
1197
+ ]
1198
+ },
1199
+ "metadata": {},
1200
+ "output_type": "display_data"
1201
+ }
1202
+ ],
1203
+ "source": [
1204
+ "trainer.train()"
1205
+ ]
1206
+ },
1207
+ {
1208
+ "cell_type": "markdown",
1209
+ "id": "810ced54-7187-4a06-b2fe-ba6dcca94dc3",
1210
+ "metadata": {
1211
+ "id": "810ced54-7187-4a06-b2fe-ba6dcca94dc3"
1212
+ },
1213
+ "source": [
1214
+ "We can label our checkpoint with the `whisper-event` tag on push by setting the appropriate key-word arguments (kwargs):"
1215
+ ]
1216
+ },
1217
+ {
1218
+ "cell_type": "code",
1219
+ "execution_count": null,
1220
+ "id": "c704f91e-241b-48c9-b8e0-f0da396a9663",
1221
+ "metadata": {
1222
+ "id": "c704f91e-241b-48c9-b8e0-f0da396a9663"
1223
+ },
1224
+ "outputs": [],
1225
+ "source": [
1226
+ "kwargs = {\n",
1227
+ " \"dataset_tags\": \"mozilla-foundation/common_voice_11_0\",\n",
1228
+ " \"dataset\": \"mozilla-foundation/common_voice_11_0\", # a 'pretty' name for the training dataset\n",
1229
+ " \"language\": \"yue\",\n",
1230
+ " \"model_name\": \"Whisper Large V2 - Cantonese - Augmented\", # a 'pretty' name for your model\n",
1231
+ " \"finetuned_from\": \"openai/whisper-large-v2\",\n",
1232
+ " \"tasks\": \"automatic-speech-recognition\",\n",
1233
+ " \"tags\": \"whisper-event\",\n",
1234
+ "}"
1235
+ ]
1236
+ },
1237
+ {
1238
+ "cell_type": "markdown",
1239
+ "id": "090d676a-f944-4297-a938-a40eda0b2b68",
1240
+ "metadata": {
1241
+ "id": "090d676a-f944-4297-a938-a40eda0b2b68"
1242
+ },
1243
+ "source": [
1244
+ "The training results can now be uploaded to the Hub. To do so, execute the `push_to_hub` command and save the preprocessor object we created:"
1245
+ ]
1246
+ },
1247
+ {
1248
+ "cell_type": "code",
1249
+ "execution_count": null,
1250
+ "id": "d7030622-caf7-4039-939b-6195cdaa2585",
1251
+ "metadata": {
1252
+ "id": "d7030622-caf7-4039-939b-6195cdaa2585"
1253
+ },
1254
+ "outputs": [],
1255
+ "source": [
1256
+ "trainer.push_to_hub(**kwargs)"
1257
+ ]
1258
+ },
1259
+ {
1260
+ "cell_type": "code",
1261
+ "execution_count": null,
1262
+ "id": "e19f35cf",
1263
+ "metadata": {},
1264
+ "outputs": [],
1265
+ "source": [
1266
+ "fleurs_results = trainer.evaluate(fleurs['test'])\n",
1267
+ "print(fleurs_results)\n",
1268
+ "\n",
1269
+ "cv_results = trainer.evaluate(cv['test'])\n",
1270
+ "print(cv_results)\n",
1271
+ "\n",
1272
+ "lbv_results = trainer.evaluate(lbv['test'])\n",
1273
+ "print(lbv_results)"
1274
+ ]
1275
+ },
1276
+ {
1277
+ "cell_type": "code",
1278
+ "execution_count": null,
1279
+ "id": "1c1e53d0",
1280
+ "metadata": {},
1281
+ "outputs": [],
1282
+ "source": [
1283
+ "evaluate.push_to_hub(\n",
1284
+ " model_id='Scrya/whisper-medium-id',\n",
1285
+ " metric_value=round(fleurs_results['eval_wer'], 2),\n",
1286
+ " metric_type=\"wer\",\n",
1287
+ " metric_name=\"WER\",\n",
1288
+ " dataset_name='google/fleurs',\n",
1289
+ " dataset_type='google/fleurs',\n",
1290
+ " dataset_split='test',\n",
1291
+ " dataset_config='id_id',\n",
1292
+ " task_type=\"automatic-speech-recognition\",\n",
1293
+ " task_name=\"Automatic Speech Recognition\",\n",
1294
+ " overwrite=True\n",
1295
+ " )\n",
1296
+ "\n",
1297
+ "evaluate.push_to_hub(\n",
1298
+ " model_id='Scrya/whisper-medium-id',\n",
1299
+ " metric_value=round(fleurs_results['eval_cer'], 2),\n",
1300
+ " metric_type=\"cer\",\n",
1301
+ " metric_name=\"CER\",\n",
1302
+ " dataset_name='google/fleurs',\n",
1303
+ " dataset_type='google/fleurs',\n",
1304
+ " dataset_split='test',\n",
1305
+ " dataset_config='id_id',\n",
1306
+ " task_type=\"automatic-speech-recognition\",\n",
1307
+ " task_name=\"Automatic Speech Recognition\",\n",
1308
+ " overwrite=True\n",
1309
+ " )\n",
1310
+ "\n",
1311
+ "evaluate.push_to_hub(\n",
1312
+ " model_id='Scrya/whisper-medium-id',\n",
1313
+ " metric_value=round(cv_results['eval_wer'], 2),\n",
1314
+ " metric_type=\"wer\",\n",
1315
+ " metric_name=\"WER\",\n",
1316
+ " dataset_name='mozilla-foundation/common_voice_11_0',\n",
1317
+ " dataset_type='mozilla-foundation/common_voice_11_0',\n",
1318
+ " dataset_split='test',\n",
1319
+ " dataset_config='id',\n",
1320
+ " task_type=\"automatic-speech-recognition\",\n",
1321
+ " task_name=\"Automatic Speech Recognition\",\n",
1322
+ " overwrite=True\n",
1323
+ " )\n",
1324
+ "\n",
1325
+ "evaluate.push_to_hub(\n",
1326
+ " model_id='Scrya/whisper-medium-id',\n",
1327
+ " metric_value=round(cv_results['eval_cer'], 2),\n",
1328
+ " metric_type=\"cer\",\n",
1329
+ " metric_name=\"CER\",\n",
1330
+ " dataset_name='mozilla-foundation/common_voice_11_0',\n",
1331
+ " dataset_type='mozilla-foundation/common_voice_11_0',\n",
1332
+ " dataset_split='test',\n",
1333
+ " dataset_config='id',\n",
1334
+ " task_type=\"automatic-speech-recognition\",\n",
1335
+ " task_name=\"Automatic Speech Recognition\",\n",
1336
+ " overwrite=True\n",
1337
+ " )\n",
1338
+ "\n",
1339
+ "evaluate.push_to_hub(\n",
1340
+ " model_id='Scrya/whisper-medium-id',\n",
1341
+ " metric_value=round(lbv_results['eval_wer'], 2),\n",
1342
+ " metric_type=\"wer\",\n",
1343
+ " metric_name=\"WER\",\n",
1344
+ " dataset_name='indonesian-nlp/librivox-indonesia',\n",
1345
+ " dataset_type='indonesian-nlp/librivox-indonesia',\n",
1346
+ " dataset_split='test',\n",
1347
+ " dataset_config='ind',\n",
1348
+ " task_type=\"automatic-speech-recognition\",\n",
1349
+ " task_name=\"Automatic Speech Recognition\",\n",
1350
+ " overwrite=True\n",
1351
+ " )\n",
1352
+ "\n",
1353
+ "evaluate.push_to_hub(\n",
1354
+ " model_id='Scrya/whisper-medium-id',\n",
1355
+ " metric_value=round(lbv_results['eval_cer'], 2),\n",
1356
+ " metric_type=\"cer\",\n",
1357
+ " metric_name=\"CER\",\n",
1358
+ " dataset_name='indonesian-nlp/librivox-indonesia',\n",
1359
+ " dataset_type='indonesian-nlp/librivox-indonesia',\n",
1360
+ " dataset_split='test',\n",
1361
+ " dataset_config='ind',\n",
1362
+ " task_type=\"automatic-speech-recognition\",\n",
1363
+ " task_name=\"Automatic Speech Recognition\",\n",
1364
+ " overwrite=True\n",
1365
+ " )"
1366
+ ]
1367
+ },
1368
+ {
1369
+ "cell_type": "markdown",
1370
+ "id": "ca743fbd-602c-48d4-ba8d-a2fe60af64ba",
1371
+ "metadata": {
1372
+ "id": "ca743fbd-602c-48d4-ba8d-a2fe60af64ba"
1373
+ },
1374
+ "source": [
1375
+ "## Closing Remarks"
1376
+ ]
1377
+ },
1378
+ {
1379
+ "cell_type": "markdown",
1380
+ "id": "7f737783-2870-4e35-aa11-86a42d7d997a",
1381
+ "metadata": {
1382
+ "id": "7f737783-2870-4e35-aa11-86a42d7d997a"
1383
+ },
1384
+ "source": [
1385
+ "In this blog, we covered a step-by-step guide on fine-tuning Whisper for multilingual ASR \n",
1386
+ "using 🤗 Datasets, Transformers and the Hugging Face Hub. For more details on the Whisper model, the Common Voice dataset and the theory behind fine-tuning, refere to the accompanying [blog post](https://huggingface.co/blog/fine-tune-whisper). If you're interested in fine-tuning other \n",
1387
+ "Transformers models, both for English and multilingual ASR, be sure to check out the \n",
1388
+ "examples scripts at [examples/pytorch/speech-recognition](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition)."
1389
+ ]
1390
+ }
1391
+ ],
1392
+ "metadata": {
1393
+ "colab": {
1394
+ "include_colab_link": true,
1395
+ "provenance": []
1396
+ },
1397
+ "kernelspec": {
1398
+ "display_name": "Python 3 (ipykernel)",
1399
+ "language": "python",
1400
+ "name": "python3"
1401
+ },
1402
+ "language_info": {
1403
+ "codemirror_mode": {
1404
+ "name": "ipython",
1405
+ "version": 3
1406
+ },
1407
+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
1409
+ "name": "python",
1410
+ "nbconvert_exporter": "python",
1411
+ "pygments_lexer": "ipython3",
1412
+ "version": "3.8.10"
1413
+ }
1414
+ },
1415
+ "nbformat": 4,
1416
+ "nbformat_minor": 5
1417
+ }
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+ "max_length": 448,
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+ "max_source_positions": 1500,
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+ "max_target_positions": 448,
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+ "model_type": "whisper",
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+ "num_hidden_layers": 32,
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+ "num_mel_bins": 80,
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+ "pad_token_id": 50257,
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+ "scale_embedding": false,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.26.0.dev0",
40
+ "use_cache": false,
41
+ "vocab_size": 51865
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+ }
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1
+ {
2
+ "accessorise": "accessorize",
3
+ "accessorised": "accessorized",
4
+ "accessorises": "accessorizes",
5
+ "accessorising": "accessorizing",
6
+ "acclimatisation": "acclimatization",
7
+ "acclimatise": "acclimatize",
8
+ "acclimatised": "acclimatized",
9
+ "acclimatises": "acclimatizes",
10
+ "acclimatising": "acclimatizing",
11
+ "accoutrements": "accouterments",
12
+ "aeon": "eon",
13
+ "aeons": "eons",
14
+ "aerogramme": "aerogram",
15
+ "aerogrammes": "aerograms",
16
+ "aeroplane": "airplane",
17
+ "aeroplanes": "airplanes",
18
+ "aesthete": "esthete",
19
+ "aesthetes": "esthetes",
20
+ "aesthetic": "esthetic",
21
+ "aesthetically": "esthetically",
22
+ "aesthetics": "esthetics",
23
+ "aetiology": "etiology",
24
+ "ageing": "aging",
25
+ "aggrandisement": "aggrandizement",
26
+ "agonise": "agonize",
27
+ "agonised": "agonized",
28
+ "agonises": "agonizes",
29
+ "agonising": "agonizing",
30
+ "agonisingly": "agonizingly",
31
+ "almanack": "almanac",
32
+ "almanacks": "almanacs",
33
+ "aluminium": "aluminum",
34
+ "amortisable": "amortizable",
35
+ "amortisation": "amortization",
36
+ "amortisations": "amortizations",
37
+ "amortise": "amortize",
38
+ "amortised": "amortized",
39
+ "amortises": "amortizes",
40
+ "amortising": "amortizing",
41
+ "amphitheatre": "amphitheater",
42
+ "amphitheatres": "amphitheaters",
43
+ "anaemia": "anemia",
44
+ "anaemic": "anemic",
45
+ "anaesthesia": "anesthesia",
46
+ "anaesthetic": "anesthetic",
47
+ "anaesthetics": "anesthetics",
48
+ "anaesthetise": "anesthetize",
49
+ "anaesthetised": "anesthetized",
50
+ "anaesthetises": "anesthetizes",
51
+ "anaesthetising": "anesthetizing",
52
+ "anaesthetist": "anesthetist",
53
+ "anaesthetists": "anesthetists",
54
+ "anaesthetize": "anesthetize",
55
+ "anaesthetized": "anesthetized",
56
+ "anaesthetizes": "anesthetizes",
57
+ "anaesthetizing": "anesthetizing",
58
+ "analogue": "analog",
59
+ "analogues": "analogs",
60
+ "analyse": "analyze",
61
+ "analysed": "analyzed",
62
+ "analyses": "analyzes",
63
+ "analysing": "analyzing",
64
+ "anglicise": "anglicize",
65
+ "anglicised": "anglicized",
66
+ "anglicises": "anglicizes",
67
+ "anglicising": "anglicizing",
68
+ "annualised": "annualized",
69
+ "antagonise": "antagonize",
70
+ "antagonised": "antagonized",
71
+ "antagonises": "antagonizes",
72
+ "antagonising": "antagonizing",
73
+ "apologise": "apologize",
74
+ "apologised": "apologized",
75
+ "apologises": "apologizes",
76
+ "apologising": "apologizing",
77
+ "appal": "appall",
78
+ "appals": "appalls",
79
+ "appetiser": "appetizer",
80
+ "appetisers": "appetizers",
81
+ "appetising": "appetizing",
82
+ "appetisingly": "appetizingly",
83
+ "arbour": "arbor",
84
+ "arbours": "arbors",
85
+ "archaeologically": "archeologically",
86
+ "archaeologist": "archeologist",
87
+ "archaeologists": "archeologists",
88
+ "archaeology": "archeology</span>",
89
+ "archeological": "archaeological",
90
+ "ardour": "ardor",
91
+ "armour": "armor",
92
+ "armoured": "armored",
93
+ "armourer": "armorer",
94
+ "armourers": "armorers",
95
+ "armouries": "armories",
96
+ "armoury": "armory",
97
+ "artefact": "artifact",
98
+ "artefacts": "artifacts",
99
+ "authorise": "authorize",
100
+ "authorised": "authorized",
101
+ "authorises": "authorizes",
102
+ "authorising": "authorizing",
103
+ "axe": "ax",
104
+ "backpedalled": "backpedaled",
105
+ "backpedalling": "backpedaling",
106
+ "bannister": "banister",
107
+ "bannisters": "banisters",
108
+ "baptise": "baptize",
109
+ "baptised": "baptized",
110
+ "baptises": "baptizes",
111
+ "baptising": "baptizing",
112
+ "bastardise": "bastardize",
113
+ "bastardised": "bastardized",
114
+ "bastardises": "bastardizes",
115
+ "bastardising": "bastardizing",
116
+ "battleax": "battleaxe",
117
+ "baulk": "balk",
118
+ "baulked": "balked",
119
+ "baulking": "balking",
120
+ "baulks": "balks",
121
+ "bedevilled": "bedeviled",
122
+ "bedevilling": "bedeviling",
123
+ "behaviour": "behavior",
124
+ "behavioural": "behavioral",
125
+ "behaviourism": "behaviorism",
126
+ "behaviourist": "behaviorist",
127
+ "behaviourists": "behaviorists",
128
+ "behaviours": "behaviors",
129
+ "behove": "behoove",
130
+ "behoved": "behooved",
131
+ "behoves": "behooves",
132
+ "bejewelled": "bejeweled",
133
+ "belabour": "belabor",
134
+ "belaboured": "belabored",
135
+ "belabouring": "belaboring",
136
+ "belabours": "belabors",
137
+ "bevelled": "beveled",
138
+ "bevvies": "bevies",
139
+ "bevvy": "bevy",
140
+ "biassed": "biased",
141
+ "biassing": "biasing",
142
+ "bingeing": "binging",
143
+ "bougainvillaea": "bougainvillea",
144
+ "bougainvillaeas": "bougainvilleas",
145
+ "bowdlerise": "bowdlerize",
146
+ "bowdlerised": "bowdlerized",
147
+ "bowdlerises": "bowdlerizes",
148
+ "bowdlerising": "bowdlerizing",
149
+ "breathalyse": "breathalyze",
150
+ "breathalysed": "breathalyzed",
151
+ "breathalyser": "breathalyzer",
152
+ "breathalysers": "breathalyzers",
153
+ "breathalyses": "breathalyzes",
154
+ "breathalysing": "breathalyzing",
155
+ "brutalise": "brutalize",
156
+ "brutalised": "brutalized",
157
+ "brutalises": "brutalizes",
158
+ "brutalising": "brutalizing",
159
+ "busses": "buses",
160
+ "bussing": "busing",
161
+ "caesarean": "cesarean",
162
+ "caesareans": "cesareans",
163
+ "calibre": "caliber",
164
+ "calibres": "calibers",
165
+ "calliper": "caliper",
166
+ "callipers": "calipers",
167
+ "callisthenics": "calisthenics",
168
+ "canalise": "canalize",
169
+ "canalised": "canalized",
170
+ "canalises": "canalizes",
171
+ "canalising": "canalizing",
172
+ "cancelation": "cancellation",
173
+ "cancelations": "cancellations",
174
+ "cancelled": "canceled",
175
+ "cancelling": "canceling",
176
+ "candour": "candor",
177
+ "cannibalise": "cannibalize",
178
+ "cannibalised": "cannibalized",
179
+ "cannibalises": "cannibalizes",
180
+ "cannibalising": "cannibalizing",
181
+ "canonise": "canonize",
182
+ "canonised": "canonized",
183
+ "canonises": "canonizes",
184
+ "canonising": "canonizing",
185
+ "capitalise": "capitalize",
186
+ "capitalised": "capitalized",
187
+ "capitalises": "capitalizes",
188
+ "capitalising": "capitalizing",
189
+ "caramelise": "caramelize",
190
+ "caramelised": "caramelized",
191
+ "caramelises": "caramelizes",
192
+ "caramelising": "caramelizing",
193
+ "carbonise": "carbonize",
194
+ "carbonised": "carbonized",
195
+ "carbonises": "carbonizes",
196
+ "carbonising": "carbonizing",
197
+ "carolled": "caroled",
198
+ "carolling": "caroling",
199
+ "catalogue": "catalog",
200
+ "catalogued": "cataloged",
201
+ "catalogues": "catalogs",
202
+ "cataloguing": "cataloging",
203
+ "catalyse": "catalyze",
204
+ "catalysed": "catalyzed",
205
+ "catalyses": "catalyzes",
206
+ "catalysing": "catalyzing",
207
+ "categorise": "categorize",
208
+ "categorised": "categorized",
209
+ "categorises": "categorizes",
210
+ "categorising": "categorizing",
211
+ "cauterise": "cauterize",
212
+ "cauterised": "cauterized",
213
+ "cauterises": "cauterizes",
214
+ "cauterising": "cauterizing",
215
+ "cavilled": "caviled",
216
+ "cavilling": "caviling",
217
+ "centigramme": "centigram",
218
+ "centigrammes": "centigrams",
219
+ "centilitre": "centiliter",
220
+ "centilitres": "centiliters",
221
+ "centimetre": "centimeter",
222
+ "centimetres": "centimeters",
223
+ "centralise": "centralize",
224
+ "centralised": "centralized",
225
+ "centralises": "centralizes",
226
+ "centralising": "centralizing",
227
+ "centre": "center",
228
+ "centred": "centered",
229
+ "centrefold": "centerfold",
230
+ "centrefolds": "centerfolds",
231
+ "centrepiece": "centerpiece",
232
+ "centrepieces": "centerpieces",
233
+ "centres": "centers",
234
+ "channelled": "channeled",
235
+ "channelling": "channeling",
236
+ "characterise": "characterize",
237
+ "characterised": "characterized",
238
+ "characterises": "characterizes",
239
+ "characterising": "characterizing",
240
+ "cheque": "check",
241
+ "chequebook": "checkbook",
242
+ "chequebooks": "checkbooks",
243
+ "chequered": "checkered",
244
+ "cheques": "checks",
245
+ "chilli": "chili",
246
+ "chimaera": "chimera",
247
+ "chimaeras": "chimeras",
248
+ "chiselled": "chiseled",
249
+ "chiselling": "chiseling",
250
+ "circularise": "circularize",
251
+ "circularised": "circularized",
252
+ "circularises": "circularizes",
253
+ "circularising": "circularizing",
254
+ "civilise": "civilize",
255
+ "civilised": "civilized",
256
+ "civilises": "civilizes",
257
+ "civilising": "civilizing",
258
+ "clamour": "clamor",
259
+ "clamoured": "clamored",
260
+ "clamouring": "clamoring",
261
+ "clamours": "clamors",
262
+ "clangour": "clangor",
263
+ "clarinettist": "clarinetist",
264
+ "clarinettists": "clarinetists",
265
+ "collectivise": "collectivize",
266
+ "collectivised": "collectivized",
267
+ "collectivises": "collectivizes",
268
+ "collectivising": "collectivizing",
269
+ "colonisation": "colonization",
270
+ "colonise": "colonize",
271
+ "colonised": "colonized",
272
+ "coloniser": "colonizer",
273
+ "colonisers": "colonizers",
274
+ "colonises": "colonizes",
275
+ "colonising": "colonizing",
276
+ "colour": "color",
277
+ "colourant": "colorant",
278
+ "colourants": "colorants",
279
+ "coloured": "colored",
280
+ "coloureds": "coloreds",
281
+ "colourful": "colorful",
282
+ "colourfully": "colorfully",
283
+ "colouring": "coloring",
284
+ "colourize": "colorize",
285
+ "colourized": "colorized",
286
+ "colourizes": "colorizes",
287
+ "colourizing": "colorizing",
288
+ "colourless": "colorless",
289
+ "colours": "colors",
290
+ "commercialise": "commercialize",
291
+ "commercialised": "commercialized",
292
+ "commercialises": "commercializes",
293
+ "commercialising": "commercializing",
294
+ "compartmentalise": "compartmentalize",
295
+ "compartmentalised": "compartmentalized",
296
+ "compartmentalises": "compartmentalizes",
297
+ "compartmentalising": "compartmentalizing",
298
+ "computerise": "computerize",
299
+ "computerised": "computerized",
300
+ "computerises": "computerizes",
301
+ "computerising": "computerizing",
302
+ "conceptualise": "conceptualize",
303
+ "conceptualised": "conceptualized",
304
+ "conceptualises": "conceptualizes",
305
+ "conceptualising": "conceptualizing",
306
+ "connexion": "connection",
307
+ "connexions": "connections",
308
+ "contextualise": "contextualize",
309
+ "contextualised": "contextualized",
310
+ "contextualises": "contextualizes",
311
+ "contextualising": "contextualizing",
312
+ "cosier": "cozier",
313
+ "cosies": "cozies",
314
+ "cosiest": "coziest",
315
+ "cosily": "cozily",
316
+ "cosiness": "coziness",
317
+ "cosy": "cozy",
318
+ "councillor": "councilor",
319
+ "councillors": "councilors",
320
+ "counselled": "counseled",
321
+ "counselling": "counseling",
322
+ "counsellor": "counselor",
323
+ "counsellors": "counselors",
324
+ "crenelated": "crenellated",
325
+ "criminalise": "criminalize",
326
+ "criminalised": "criminalized",
327
+ "criminalises": "criminalizes",
328
+ "criminalising": "criminalizing",
329
+ "criticise": "criticize",
330
+ "criticised": "criticized",
331
+ "criticises": "criticizes",
332
+ "criticising": "criticizing",
333
+ "crueller": "crueler",
334
+ "cruellest": "cruelest",
335
+ "crystallisation": "crystallization",
336
+ "crystallise": "crystallize",
337
+ "crystallised": "crystallized",
338
+ "crystallises": "crystallizes",
339
+ "crystallising": "crystallizing",
340
+ "cudgelled": "cudgeled",
341
+ "cudgelling": "cudgeling",
342
+ "customise": "customize",
343
+ "customised": "customized",
344
+ "customises": "customizes",
345
+ "customising": "customizing",
346
+ "cypher": "cipher",
347
+ "cyphers": "ciphers",
348
+ "decentralisation": "decentralization",
349
+ "decentralise": "decentralize",
350
+ "decentralised": "decentralized",
351
+ "decentralises": "decentralizes",
352
+ "decentralising": "decentralizing",
353
+ "decriminalisation": "decriminalization",
354
+ "decriminalise": "decriminalize",
355
+ "decriminalised": "decriminalized",
356
+ "decriminalises": "decriminalizes",
357
+ "decriminalising": "decriminalizing",
358
+ "defence": "defense",
359
+ "defenceless": "defenseless",
360
+ "defences": "defenses",
361
+ "dehumanisation": "dehumanization",
362
+ "dehumanise": "dehumanize",
363
+ "dehumanised": "dehumanized",
364
+ "dehumanises": "dehumanizes",
365
+ "dehumanising": "dehumanizing",
366
+ "demeanour": "demeanor",
367
+ "demilitarisation": "demilitarization",
368
+ "demilitarise": "demilitarize",
369
+ "demilitarised": "demilitarized",
370
+ "demilitarises": "demilitarizes",
371
+ "demilitarising": "demilitarizing",
372
+ "demobilisation": "demobilization",
373
+ "demobilise": "demobilize",
374
+ "demobilised": "demobilized",
375
+ "demobilises": "demobilizes",
376
+ "demobilising": "demobilizing",
377
+ "democratisation": "democratization",
378
+ "democratise": "democratize",
379
+ "democratised": "democratized",
380
+ "democratises": "democratizes",
381
+ "democratising": "democratizing",
382
+ "demonise": "demonize",
383
+ "demonised": "demonized",
384
+ "demonises": "demonizes",
385
+ "demonising": "demonizing",
386
+ "demoralisation": "demoralization",
387
+ "demoralise": "demoralize",
388
+ "demoralised": "demoralized",
389
+ "demoralises": "demoralizes",
390
+ "demoralising": "demoralizing",
391
+ "denationalisation": "denationalization",
392
+ "denationalise": "denationalize",
393
+ "denationalised": "denationalized",
394
+ "denationalises": "denationalizes",
395
+ "denationalising": "denationalizing",
396
+ "deodorise": "deodorize",
397
+ "deodorised": "deodorized",
398
+ "deodorises": "deodorizes",
399
+ "deodorising": "deodorizing",
400
+ "depersonalise": "depersonalize",
401
+ "depersonalised": "depersonalized",
402
+ "depersonalises": "depersonalizes",
403
+ "depersonalising": "depersonalizing",
404
+ "deputise": "deputize",
405
+ "deputised": "deputized",
406
+ "deputises": "deputizes",
407
+ "deputising": "deputizing",
408
+ "desensitisation": "desensitization",
409
+ "desensitise": "desensitize",
410
+ "desensitised": "desensitized",
411
+ "desensitises": "desensitizes",
412
+ "desensitising": "desensitizing",
413
+ "destabilisation": "destabilization",
414
+ "destabilise": "destabilize",
415
+ "destabilised": "destabilized",
416
+ "destabilises": "destabilizes",
417
+ "destabilising": "destabilizing",
418
+ "dialled": "dialed",
419
+ "dialling": "dialing",
420
+ "dialogue": "dialog",
421
+ "dialogues": "dialogs",
422
+ "diarrhoea": "diarrhea",
423
+ "digitise": "digitize",
424
+ "digitised": "digitized",
425
+ "digitises": "digitizes",
426
+ "digitising": "digitizing",
427
+ "disc": "disk",
428
+ "discolour": "discolor",
429
+ "discoloured": "discolored",
430
+ "discolouring": "discoloring",
431
+ "discolours": "discolors",
432
+ "discs": "disks",
433
+ "disembowelled": "disemboweled",
434
+ "disembowelling": "disemboweling",
435
+ "disfavour": "disfavor",
436
+ "dishevelled": "disheveled",
437
+ "dishonour": "dishonor",
438
+ "dishonourable": "dishonorable",
439
+ "dishonourably": "dishonorably",
440
+ "dishonoured": "dishonored",
441
+ "dishonouring": "dishonoring",
442
+ "dishonours": "dishonors",
443
+ "disorganisation": "disorganization",
444
+ "disorganised": "disorganized",
445
+ "distil": "distill",
446
+ "distils": "distills",
447
+ "dramatisation": "dramatization",
448
+ "dramatisations": "dramatizations",
449
+ "dramatise": "dramatize",
450
+ "dramatised": "dramatized",
451
+ "dramatises": "dramatizes",
452
+ "dramatising": "dramatizing",
453
+ "draught": "draft",
454
+ "draughtboard": "draftboard",
455
+ "draughtboards": "draftboards",
456
+ "draughtier": "draftier",
457
+ "draughtiest": "draftiest",
458
+ "draughts": "drafts",
459
+ "draughtsman": "draftsman",
460
+ "draughtsmanship": "draftsmanship",
461
+ "draughtsmen": "draftsmen",
462
+ "draughtswoman": "draftswoman",
463
+ "draughtswomen": "draftswomen",
464
+ "draughty": "drafty",
465
+ "drivelled": "driveled",
466
+ "drivelling": "driveling",
467
+ "duelled": "dueled",
468
+ "duelling": "dueling",
469
+ "economise": "economize",
470
+ "economised": "economized",
471
+ "economises": "economizes",
472
+ "economising": "economizing",
473
+ "editorialise": "editorialize",
474
+ "editorialised": "editorialized",
475
+ "editorialises": "editorializes",
476
+ "editorialising": "editorializing",
477
+ "edoema": "edema",
478
+ "empathise": "empathize",
479
+ "empathised": "empathized",
480
+ "empathises": "empathizes",
481
+ "empathising": "empathizing",
482
+ "emphasise": "emphasize",
483
+ "emphasised": "emphasized",
484
+ "emphasises": "emphasizes",
485
+ "emphasising": "emphasizing",
486
+ "enamelled": "enameled",
487
+ "enamelling": "enameling",
488
+ "enamoured": "enamored",
489
+ "encyclopaedia": "encyclopedia",
490
+ "encyclopaedias": "encyclopedias",
491
+ "encyclopaedic": "encyclopedic",
492
+ "endeavour": "endeavor",
493
+ "endeavoured": "endeavored",
494
+ "endeavouring": "endeavoring",
495
+ "endeavours": "endeavors",
496
+ "energise": "energize",
497
+ "energised": "energized",
498
+ "energises": "energizes",
499
+ "energising": "energizing",
500
+ "enrol": "enroll",
501
+ "enrols": "enrolls",
502
+ "enthral": "enthrall",
503
+ "enthrals": "enthralls",
504
+ "epaulette": "epaulet",
505
+ "epaulettes": "epaulets",
506
+ "epicentre": "epicenter",
507
+ "epicentres": "epicenters",
508
+ "epilogue": "epilog",
509
+ "epilogues": "epilogs",
510
+ "epitomise": "epitomize",
511
+ "epitomised": "epitomized",
512
+ "epitomises": "epitomizes",
513
+ "epitomising": "epitomizing",
514
+ "equalisation": "equalization",
515
+ "equalise": "equalize",
516
+ "equalised": "equalized",
517
+ "equaliser": "equalizer",
518
+ "equalisers": "equalizers",
519
+ "equalises": "equalizes",
520
+ "equalising": "equalizing",
521
+ "eulogise": "eulogize",
522
+ "eulogised": "eulogized",
523
+ "eulogises": "eulogizes",
524
+ "eulogising": "eulogizing",
525
+ "evangelise": "evangelize",
526
+ "evangelised": "evangelized",
527
+ "evangelises": "evangelizes",
528
+ "evangelising": "evangelizing",
529
+ "exorcise": "exorcize",
530
+ "exorcised": "exorcized",
531
+ "exorcises": "exorcizes",
532
+ "exorcising": "exorcizing",
533
+ "extemporisation": "extemporization",
534
+ "extemporise": "extemporize",
535
+ "extemporised": "extemporized",
536
+ "extemporises": "extemporizes",
537
+ "extemporising": "extemporizing",
538
+ "externalisation": "externalization",
539
+ "externalisations": "externalizations",
540
+ "externalise": "externalize",
541
+ "externalised": "externalized",
542
+ "externalises": "externalizes",
543
+ "externalising": "externalizing",
544
+ "factorise": "factorize",
545
+ "factorised": "factorized",
546
+ "factorises": "factorizes",
547
+ "factorising": "factorizing",
548
+ "faecal": "fecal",
549
+ "faeces": "feces",
550
+ "familiarisation": "familiarization",
551
+ "familiarise": "familiarize",
552
+ "familiarised": "familiarized",
553
+ "familiarises": "familiarizes",
554
+ "familiarising": "familiarizing",
555
+ "fantasise": "fantasize",
556
+ "fantasised": "fantasized",
557
+ "fantasises": "fantasizes",
558
+ "fantasising": "fantasizing",
559
+ "favour": "favor",
560
+ "favourable": "favorable",
561
+ "favourably": "favorably",
562
+ "favoured": "favored",
563
+ "favouring": "favoring",
564
+ "favourite": "favorite",
565
+ "favourites": "favorites",
566
+ "favouritism": "favoritism",
567
+ "favours": "favors",
568
+ "feminise": "feminize",
569
+ "feminised": "feminized",
570
+ "feminises": "feminizes",
571
+ "feminising": "feminizing",
572
+ "fertilisation": "fertilization",
573
+ "fertilise": "fertilize",
574
+ "fertilised": "fertilized",
575
+ "fertiliser": "fertilizer",
576
+ "fertilisers": "fertilizers",
577
+ "fertilises": "fertilizes",
578
+ "fertilising": "fertilizing",
579
+ "fervour": "fervor",
580
+ "fibre": "fiber",
581
+ "fibreglass": "fiberglass",
582
+ "fibres": "fibers",
583
+ "fictionalisation": "fictionalization",
584
+ "fictionalisations": "fictionalizations",
585
+ "fictionalise": "fictionalize",
586
+ "fictionalised": "fictionalized",
587
+ "fictionalises": "fictionalizes",
588
+ "fictionalising": "fictionalizing",
589
+ "fillet": "filet",
590
+ "filleted": "fileted",
591
+ "filleting": "fileting",
592
+ "fillets": "filets",
593
+ "finalisation": "finalization",
594
+ "finalise": "finalize",
595
+ "finalised": "finalized",
596
+ "finalises": "finalizes",
597
+ "finalising": "finalizing",
598
+ "flautist": "flutist",
599
+ "flautists": "flutists",
600
+ "flavour": "flavor",
601
+ "flavoured": "flavored",
602
+ "flavouring": "flavoring",
603
+ "flavourings": "flavorings",
604
+ "flavourless": "flavorless",
605
+ "flavours": "flavors",
606
+ "flavoursome": "flavorsome",
607
+ "flyer / flier": "flier / flyer",
608
+ "foetal": "fetal",
609
+ "foetid": "fetid",
610
+ "foetus": "fetus",
611
+ "foetuses": "fetuses",
612
+ "formalisation": "formalization",
613
+ "formalise": "formalize",
614
+ "formalised": "formalized",
615
+ "formalises": "formalizes",
616
+ "formalising": "formalizing",
617
+ "fossilisation": "fossilization",
618
+ "fossilise": "fossilize",
619
+ "fossilised": "fossilized",
620
+ "fossilises": "fossilizes",
621
+ "fossilising": "fossilizing",
622
+ "fraternisation": "fraternization",
623
+ "fraternise": "fraternize",
624
+ "fraternised": "fraternized",
625
+ "fraternises": "fraternizes",
626
+ "fraternising": "fraternizing",
627
+ "fulfil": "fulfill",
628
+ "fulfilment": "fulfillment",
629
+ "fulfils": "fulfills",
630
+ "funnelled": "funneled",
631
+ "funnelling": "funneling",
632
+ "gage": "gauge",
633
+ "gaged": "gauged",
634
+ "gages": "gauges",
635
+ "gaging": "gauging",
636
+ "galvanise": "galvanize",
637
+ "galvanised": "galvanized",
638
+ "galvanises": "galvanizes",
639
+ "galvanising": "galvanizing",
640
+ "gambolled": "gamboled",
641
+ "gambolling": "gamboling",
642
+ "gaol": "jail",
643
+ "gaolbird": "jailbird",
644
+ "gaolbirds": "jailbirds",
645
+ "gaolbreak": "jailbreak",
646
+ "gaolbreaks": "jailbreaks",
647
+ "gaoled": "jailed",
648
+ "gaoler": "jailer",
649
+ "gaolers": "jailers",
650
+ "gaoling": "jailing",
651
+ "gaols": "jails",
652
+ "gasses": "gases",
653
+ "generalisation": "generalization",
654
+ "generalisations": "generalizations",
655
+ "generalise": "generalize",
656
+ "generalised": "generalized",
657
+ "generalises": "generalizes",
658
+ "generalising": "generalizing",
659
+ "ghettoise": "ghettoize",
660
+ "ghettoised": "ghettoized",
661
+ "ghettoises": "ghettoizes",
662
+ "ghettoising": "ghettoizing",
663
+ "gipsies": "gypsies",
664
+ "glamor": "glamour",
665
+ "glamorise": "glamorize",
666
+ "glamorised": "glamorized",
667
+ "glamorises": "glamorizes",
668
+ "glamorising": "glamorizing",
669
+ "globalisation": "globalization",
670
+ "globalise": "globalize",
671
+ "globalised": "globalized",
672
+ "globalises": "globalizes",
673
+ "globalising": "globalizing",
674
+ "glueing": "gluing",
675
+ "goitre": "goiter",
676
+ "goitres": "goiters",
677
+ "gonorrhoea": "gonorrhea",
678
+ "gramme": "gram",
679
+ "grammes": "grams",
680
+ "gravelled": "graveled",
681
+ "grey": "gray",
682
+ "greyed": "grayed",
683
+ "greying": "graying",
684
+ "greyish": "grayish",
685
+ "greyness": "grayness",
686
+ "greys": "grays",
687
+ "grovelled": "groveled",
688
+ "grovelling": "groveling",
689
+ "groyne": "groin",
690
+ "groynes": "groins",
691
+ "gruelling": "grueling",
692
+ "gruellingly": "gruelingly",
693
+ "gryphon": "griffin",
694
+ "gryphons": "griffins",
695
+ "gynaecological": "gynecological",
696
+ "gynaecologist": "gynecologist",
697
+ "gynaecologists": "gynecologists",
698
+ "gynaecology": "gynecology",
699
+ "haematological": "hematological",
700
+ "haematologist": "hematologist",
701
+ "haematologists": "hematologists",
702
+ "haematology": "hematology",
703
+ "haemoglobin": "hemoglobin",
704
+ "haemophilia": "hemophilia",
705
+ "haemophiliac": "hemophiliac",
706
+ "haemophiliacs": "hemophiliacs",
707
+ "haemorrhage": "hemorrhage",
708
+ "haemorrhaged": "hemorrhaged",
709
+ "haemorrhages": "hemorrhages",
710
+ "haemorrhaging": "hemorrhaging",
711
+ "haemorrhoids": "hemorrhoids",
712
+ "harbour": "harbor",
713
+ "harboured": "harbored",
714
+ "harbouring": "harboring",
715
+ "harbours": "harbors",
716
+ "harmonisation": "harmonization",
717
+ "harmonise": "harmonize",
718
+ "harmonised": "harmonized",
719
+ "harmonises": "harmonizes",
720
+ "harmonising": "harmonizing",
721
+ "homoeopath": "homeopath",
722
+ "homoeopathic": "homeopathic",
723
+ "homoeopaths": "homeopaths",
724
+ "homoeopathy": "homeopathy",
725
+ "homogenise": "homogenize",
726
+ "homogenised": "homogenized",
727
+ "homogenises": "homogenizes",
728
+ "homogenising": "homogenizing",
729
+ "honour": "honor",
730
+ "honourable": "honorable",
731
+ "honourably": "honorably",
732
+ "honoured": "honored",
733
+ "honouring": "honoring",
734
+ "honours": "honors",
735
+ "hospitalisation": "hospitalization",
736
+ "hospitalise": "hospitalize",
737
+ "hospitalised": "hospitalized",
738
+ "hospitalises": "hospitalizes",
739
+ "hospitalising": "hospitalizing",
740
+ "humanise": "humanize",
741
+ "humanised": "humanized",
742
+ "humanises": "humanizes",
743
+ "humanising": "humanizing",
744
+ "humour": "humor",
745
+ "humoured": "humored",
746
+ "humouring": "humoring",
747
+ "humourless": "humorless",
748
+ "humours": "humors",
749
+ "hybridise": "hybridize",
750
+ "hybridised": "hybridized",
751
+ "hybridises": "hybridizes",
752
+ "hybridising": "hybridizing",
753
+ "hypnotise": "hypnotize",
754
+ "hypnotised": "hypnotized",
755
+ "hypnotises": "hypnotizes",
756
+ "hypnotising": "hypnotizing",
757
+ "hypothesise": "hypothesize",
758
+ "hypothesised": "hypothesized",
759
+ "hypothesises": "hypothesizes",
760
+ "hypothesising": "hypothesizing",
761
+ "idealisation": "idealization",
762
+ "idealise": "idealize",
763
+ "idealised": "idealized",
764
+ "idealises": "idealizes",
765
+ "idealising": "idealizing",
766
+ "idolise": "idolize",
767
+ "idolised": "idolized",
768
+ "idolises": "idolizes",
769
+ "idolising": "idolizing",
770
+ "immobilisation": "immobilization",
771
+ "immobilise": "immobilize",
772
+ "immobilised": "immobilized",
773
+ "immobiliser": "immobilizer",
774
+ "immobilisers": "immobilizers",
775
+ "immobilises": "immobilizes",
776
+ "immobilising": "immobilizing",
777
+ "immortalise": "immortalize",
778
+ "immortalised": "immortalized",
779
+ "immortalises": "immortalizes",
780
+ "immortalising": "immortalizing",
781
+ "immunisation": "immunization",
782
+ "immunise": "immunize",
783
+ "immunised": "immunized",
784
+ "immunises": "immunizes",
785
+ "immunising": "immunizing",
786
+ "impanelled": "impaneled",
787
+ "impanelling": "impaneling",
788
+ "imperilled": "imperiled",
789
+ "imperilling": "imperiling",
790
+ "individualise": "individualize",
791
+ "individualised": "individualized",
792
+ "individualises": "individualizes",
793
+ "individualising": "individualizing",
794
+ "industrialise": "industrialize",
795
+ "industrialised": "industrialized",
796
+ "industrialises": "industrializes",
797
+ "industrialising": "industrializing",
798
+ "inflexion": "inflection",
799
+ "inflexions": "inflections",
800
+ "initialise": "initialize",
801
+ "initialised": "initialized",
802
+ "initialises": "initializes",
803
+ "initialising": "initializing",
804
+ "initialled": "initialed",
805
+ "initialling": "initialing",
806
+ "instal": "install",
807
+ "instalment": "installment",
808
+ "instalments": "installments",
809
+ "instals": "installs",
810
+ "instil": "instill",
811
+ "instils": "instills",
812
+ "institutionalisation": "institutionalization",
813
+ "institutionalise": "institutionalize",
814
+ "institutionalised": "institutionalized",
815
+ "institutionalises": "institutionalizes",
816
+ "institutionalising": "institutionalizing",
817
+ "intellectualise": "intellectualize",
818
+ "intellectualised": "intellectualized",
819
+ "intellectualises": "intellectualizes",
820
+ "intellectualising": "intellectualizing",
821
+ "internalisation": "internalization",
822
+ "internalise": "internalize",
823
+ "internalised": "internalized",
824
+ "internalises": "internalizes",
825
+ "internalising": "internalizing",
826
+ "internationalisation": "internationalization",
827
+ "internationalise": "internationalize",
828
+ "internationalised": "internationalized",
829
+ "internationalises": "internationalizes",
830
+ "internationalising": "internationalizing",
831
+ "ionisation": "ionization",
832
+ "ionise": "ionize",
833
+ "ionised": "ionized",
834
+ "ioniser": "ionizer",
835
+ "ionisers": "ionizers",
836
+ "ionises": "ionizes",
837
+ "ionising": "ionizing",
838
+ "italicise": "italicize",
839
+ "italicised": "italicized",
840
+ "italicises": "italicizes",
841
+ "italicising": "italicizing",
842
+ "itemise": "itemize",
843
+ "itemised": "itemized",
844
+ "itemises": "itemizes",
845
+ "itemising": "itemizing",
846
+ "jeopardise": "jeopardize",
847
+ "jeopardised": "jeopardized",
848
+ "jeopardises": "jeopardizes",
849
+ "jeopardising": "jeopardizing",
850
+ "jewelled": "jeweled",
851
+ "jeweller": "jeweler",
852
+ "jewellers": "jewelers",
853
+ "jewellery": "jewelry",
854
+ "judgement": "judgment",
855
+ "kilogramme": "kilogram",
856
+ "kilogrammes": "kilograms",
857
+ "kilometre": "kilometer",
858
+ "kilometres": "kilometers",
859
+ "labelled": "labeled",
860
+ "labelling": "labeling",
861
+ "labour": "labor",
862
+ "laboured": "labored",
863
+ "labourer": "laborer",
864
+ "labourers": "laborers",
865
+ "labouring": "laboring",
866
+ "labours": "labors",
867
+ "lacklustre": "lackluster",
868
+ "legalisation": "legalization",
869
+ "legalise": "legalize",
870
+ "legalised": "legalized",
871
+ "legalises": "legalizes",
872
+ "legalising": "legalizing",
873
+ "legitimise": "legitimize",
874
+ "legitimised": "legitimized",
875
+ "legitimises": "legitimizes",
876
+ "legitimising": "legitimizing",
877
+ "leukaemia": "leukemia",
878
+ "levelled": "leveled",
879
+ "leveller": "leveler",
880
+ "levellers": "levelers",
881
+ "levelling": "leveling",
882
+ "libelled": "libeled",
883
+ "libelling": "libeling",
884
+ "libellous": "libelous",
885
+ "liberalisation": "liberalization",
886
+ "liberalise": "liberalize",
887
+ "liberalised": "liberalized",
888
+ "liberalises": "liberalizes",
889
+ "liberalising": "liberalizing",
890
+ "licence": "license",
891
+ "licenced": "licensed",
892
+ "licences": "licenses",
893
+ "licencing": "licensing",
894
+ "likeable": "likable",
895
+ "lionisation": "lionization",
896
+ "lionise": "lionize",
897
+ "lionised": "lionized",
898
+ "lionises": "lionizes",
899
+ "lionising": "lionizing",
900
+ "liquidise": "liquidize",
901
+ "liquidised": "liquidized",
902
+ "liquidiser": "liquidizer",
903
+ "liquidisers": "liquidizers",
904
+ "liquidises": "liquidizes",
905
+ "liquidising": "liquidizing",
906
+ "litre": "liter",
907
+ "litres": "liters",
908
+ "localise": "localize",
909
+ "localised": "localized",
910
+ "localises": "localizes",
911
+ "localising": "localizing",
912
+ "louvre": "louver",
913
+ "louvred": "louvered",
914
+ "louvres": "louvers",
915
+ "lustre": "luster",
916
+ "magnetise": "magnetize",
917
+ "magnetised": "magnetized",
918
+ "magnetises": "magnetizes",
919
+ "magnetising": "magnetizing",
920
+ "manoeuvrability": "maneuverability",
921
+ "manoeuvrable": "maneuverable",
922
+ "manoeuvre": "maneuver",
923
+ "manoeuvred": "maneuvered",
924
+ "manoeuvres": "maneuvers",
925
+ "manoeuvring": "maneuvering",
926
+ "manoeuvrings": "maneuverings",
927
+ "marginalisation": "marginalization",
928
+ "marginalise": "marginalize",
929
+ "marginalised": "marginalized",
930
+ "marginalises": "marginalizes",
931
+ "marginalising": "marginalizing",
932
+ "marshalled": "marshaled",
933
+ "marshalling": "marshaling",
934
+ "marvelled": "marveled",
935
+ "marvelling": "marveling",
936
+ "marvellous": "marvelous",
937
+ "marvellously": "marvelously",
938
+ "materialisation": "materialization",
939
+ "materialise": "materialize",
940
+ "materialised": "materialized",
941
+ "materialises": "materializes",
942
+ "materialising": "materializing",
943
+ "maximisation": "maximization",
944
+ "maximise": "maximize",
945
+ "maximised": "maximized",
946
+ "maximises": "maximizes",
947
+ "maximising": "maximizing",
948
+ "meagre": "meager",
949
+ "mechanisation": "mechanization",
950
+ "mechanise": "mechanize",
951
+ "mechanised": "mechanized",
952
+ "mechanises": "mechanizes",
953
+ "mechanising": "mechanizing",
954
+ "mediaeval": "medieval",
955
+ "memorialise": "memorialize",
956
+ "memorialised": "memorialized",
957
+ "memorialises": "memorializes",
958
+ "memorialising": "memorializing",
959
+ "memorise": "memorize",
960
+ "memorised": "memorized",
961
+ "memorises": "memorizes",
962
+ "memorising": "memorizing",
963
+ "mesmerise": "mesmerize",
964
+ "mesmerised": "mesmerized",
965
+ "mesmerises": "mesmerizes",
966
+ "mesmerising": "mesmerizing",
967
+ "metabolise": "metabolize",
968
+ "metabolised": "metabolized",
969
+ "metabolises": "metabolizes",
970
+ "metabolising": "metabolizing",
971
+ "metre": "meter",
972
+ "metres": "meters",
973
+ "mhm": "hmm",
974
+ "micrometre": "micrometer",
975
+ "micrometres": "micrometers",
976
+ "militarise": "militarize",
977
+ "militarised": "militarized",
978
+ "militarises": "militarizes",
979
+ "militarising": "militarizing",
980
+ "milligramme": "milligram",
981
+ "milligrammes": "milligrams",
982
+ "millilitre": "milliliter",
983
+ "millilitres": "milliliters",
984
+ "millimetre": "millimeter",
985
+ "millimetres": "millimeters",
986
+ "miniaturisation": "miniaturization",
987
+ "miniaturise": "miniaturize",
988
+ "miniaturised": "miniaturized",
989
+ "miniaturises": "miniaturizes",
990
+ "miniaturising": "miniaturizing",
991
+ "minibusses": "minibuses",
992
+ "minimise": "minimize",
993
+ "minimised": "minimized",
994
+ "minimises": "minimizes",
995
+ "minimising": "minimizing",
996
+ "misbehaviour": "misbehavior",
997
+ "misdemeanour": "misdemeanor",
998
+ "misdemeanours": "misdemeanors",
999
+ "misspelt": "misspelled",
1000
+ "mitre": "miter",
1001
+ "mitres": "miters",
1002
+ "mm": "hmm",
1003
+ "mmm": "hmm",
1004
+ "mobilisation": "mobilization",
1005
+ "mobilise": "mobilize",
1006
+ "mobilised": "mobilized",
1007
+ "mobilises": "mobilizes",
1008
+ "mobilising": "mobilizing",
1009
+ "modelled": "modeled",
1010
+ "modeller": "modeler",
1011
+ "modellers": "modelers",
1012
+ "modelling": "modeling",
1013
+ "modernise": "modernize",
1014
+ "modernised": "modernized",
1015
+ "modernises": "modernizes",
1016
+ "modernising": "modernizing",
1017
+ "moisturise": "moisturize",
1018
+ "moisturised": "moisturized",
1019
+ "moisturiser": "moisturizer",
1020
+ "moisturisers": "moisturizers",
1021
+ "moisturises": "moisturizes",
1022
+ "moisturising": "moisturizing",
1023
+ "monologue": "monolog",
1024
+ "monologues": "monologs",
1025
+ "monopolisation": "monopolization",
1026
+ "monopolise": "monopolize",
1027
+ "monopolised": "monopolized",
1028
+ "monopolises": "monopolizes",
1029
+ "monopolising": "monopolizing",
1030
+ "moralise": "moralize",
1031
+ "moralised": "moralized",
1032
+ "moralises": "moralizes",
1033
+ "moralising": "moralizing",
1034
+ "motorised": "motorized",
1035
+ "mould": "mold",
1036
+ "moulded": "molded",
1037
+ "moulder": "molder",
1038
+ "mouldered": "moldered",
1039
+ "mouldering": "moldering",
1040
+ "moulders": "molders",
1041
+ "mouldier": "moldier",
1042
+ "mouldiest": "moldiest",
1043
+ "moulding": "molding",
1044
+ "mouldings": "moldings",
1045
+ "moulds": "molds",
1046
+ "mouldy": "moldy",
1047
+ "moult": "molt",
1048
+ "moulted": "molted",
1049
+ "moulting": "molting",
1050
+ "moults": "molts",
1051
+ "moustache": "mustache",
1052
+ "moustached": "mustached",
1053
+ "moustaches": "mustaches",
1054
+ "moustachioed": "mustachioed",
1055
+ "multicoloured": "multicolored",
1056
+ "nationalisation": "nationalization",
1057
+ "nationalisations": "nationalizations",
1058
+ "nationalise": "nationalize",
1059
+ "nationalised": "nationalized",
1060
+ "nationalises": "nationalizes",
1061
+ "nationalising": "nationalizing",
1062
+ "naturalisation": "naturalization",
1063
+ "naturalise": "naturalize",
1064
+ "naturalised": "naturalized",
1065
+ "naturalises": "naturalizes",
1066
+ "naturalising": "naturalizing",
1067
+ "neighbour": "neighbor",
1068
+ "neighbourhood": "neighborhood",
1069
+ "neighbourhoods": "neighborhoods",
1070
+ "neighbouring": "neighboring",
1071
+ "neighbourliness": "neighborliness",
1072
+ "neighbourly": "neighborly",
1073
+ "neighbours": "neighbors",
1074
+ "neutralisation": "neutralization",
1075
+ "neutralise": "neutralize",
1076
+ "neutralised": "neutralized",
1077
+ "neutralises": "neutralizes",
1078
+ "neutralising": "neutralizing",
1079
+ "normalisation": "normalization",
1080
+ "normalise": "normalize",
1081
+ "normalised": "normalized",
1082
+ "normalises": "normalizes",
1083
+ "normalising": "normalizing",
1084
+ "odour": "odor",
1085
+ "odourless": "odorless",
1086
+ "odours": "odors",
1087
+ "oesophagus": "esophagus",
1088
+ "oesophaguses": "esophaguses",
1089
+ "oestrogen": "estrogen",
1090
+ "offence": "offense",
1091
+ "offences": "offenses",
1092
+ "omelette": "omelet",
1093
+ "omelettes": "omelets",
1094
+ "optimise": "optimize",
1095
+ "optimised": "optimized",
1096
+ "optimises": "optimizes",
1097
+ "optimising": "optimizing",
1098
+ "organisation": "organization",
1099
+ "organisational": "organizational",
1100
+ "organisations": "organizations",
1101
+ "organise": "organize",
1102
+ "organised": "organized",
1103
+ "organiser": "organizer",
1104
+ "organisers": "organizers",
1105
+ "organises": "organizes",
1106
+ "organising": "organizing",
1107
+ "orthopaedic": "orthopedic",
1108
+ "orthopaedics": "orthopedics",
1109
+ "ostracise": "ostracize",
1110
+ "ostracised": "ostracized",
1111
+ "ostracises": "ostracizes",
1112
+ "ostracising": "ostracizing",
1113
+ "outmanoeuvre": "outmaneuver",
1114
+ "outmanoeuvred": "outmaneuvered",
1115
+ "outmanoeuvres": "outmaneuvers",
1116
+ "outmanoeuvring": "outmaneuvering",
1117
+ "overemphasise": "overemphasize",
1118
+ "overemphasised": "overemphasized",
1119
+ "overemphasises": "overemphasizes",
1120
+ "overemphasising": "overemphasizing",
1121
+ "oxidisation": "oxidization",
1122
+ "oxidise": "oxidize",
1123
+ "oxidised": "oxidized",
1124
+ "oxidises": "oxidizes",
1125
+ "oxidising": "oxidizing",
1126
+ "paederast": "pederast",
1127
+ "paederasts": "pederasts",
1128
+ "paediatric": "pediatric",
1129
+ "paediatrician": "pediatrician",
1130
+ "paediatricians": "pediatricians",
1131
+ "paediatrics": "pediatrics",
1132
+ "paedophile": "pedophile",
1133
+ "paedophiles": "pedophiles",
1134
+ "paedophilia": "pedophilia",
1135
+ "palaeolithic": "paleolithic",
1136
+ "palaeontologist": "paleontologist",
1137
+ "palaeontologists": "paleontologists",
1138
+ "palaeontology": "paleontology",
1139
+ "panelled": "paneled",
1140
+ "panelling": "paneling",
1141
+ "panellist": "panelist",
1142
+ "panellists": "panelists",
1143
+ "paralyse": "paralyze",
1144
+ "paralysed": "paralyzed",
1145
+ "paralyses": "paralyzes",
1146
+ "paralysing": "paralyzing",
1147
+ "parcelled": "parceled",
1148
+ "parcelling": "parceling",
1149
+ "parlour": "parlor",
1150
+ "parlours": "parlors",
1151
+ "particularise": "particularize",
1152
+ "particularised": "particularized",
1153
+ "particularises": "particularizes",
1154
+ "particularising": "particularizing",
1155
+ "passivisation": "passivization",
1156
+ "passivise": "passivize",
1157
+ "passivised": "passivized",
1158
+ "passivises": "passivizes",
1159
+ "passivising": "passivizing",
1160
+ "pasteurisation": "pasteurization",
1161
+ "pasteurise": "pasteurize",
1162
+ "pasteurised": "pasteurized",
1163
+ "pasteurises": "pasteurizes",
1164
+ "pasteurising": "pasteurizing",
1165
+ "patronise": "patronize",
1166
+ "patronised": "patronized",
1167
+ "patronises": "patronizes",
1168
+ "patronising": "patronizing",
1169
+ "patronisingly": "patronizingly",
1170
+ "pedalled": "pedaled",
1171
+ "pedalling": "pedaling",
1172
+ "pedestrianisation": "pedestrianization",
1173
+ "pedestrianise": "pedestrianize",
1174
+ "pedestrianised": "pedestrianized",
1175
+ "pedestrianises": "pedestrianizes",
1176
+ "pedestrianising": "pedestrianizing",
1177
+ "penalise": "penalize",
1178
+ "penalised": "penalized",
1179
+ "penalises": "penalizes",
1180
+ "penalising": "penalizing",
1181
+ "pencilled": "penciled",
1182
+ "pencilling": "penciling",
1183
+ "personalise": "personalize",
1184
+ "personalised": "personalized",
1185
+ "personalises": "personalizes",
1186
+ "personalising": "personalizing",
1187
+ "pharmacopoeia": "pharmacopeia",
1188
+ "pharmacopoeias": "pharmacopeias",
1189
+ "philosophise": "philosophize",
1190
+ "philosophised": "philosophized",
1191
+ "philosophises": "philosophizes",
1192
+ "philosophising": "philosophizing",
1193
+ "philtre": "filter",
1194
+ "philtres": "filters",
1195
+ "phoney": "phony",
1196
+ "plagiarise": "plagiarize",
1197
+ "plagiarised": "plagiarized",
1198
+ "plagiarises": "plagiarizes",
1199
+ "plagiarising": "plagiarizing",
1200
+ "plough": "plow",
1201
+ "ploughed": "plowed",
1202
+ "ploughing": "plowing",
1203
+ "ploughman": "plowman",
1204
+ "ploughmen": "plowmen",
1205
+ "ploughs": "plows",
1206
+ "ploughshare": "plowshare",
1207
+ "ploughshares": "plowshares",
1208
+ "polarisation": "polarization",
1209
+ "polarise": "polarize",
1210
+ "polarised": "polarized",
1211
+ "polarises": "polarizes",
1212
+ "polarising": "polarizing",
1213
+ "politicisation": "politicization",
1214
+ "politicise": "politicize",
1215
+ "politicised": "politicized",
1216
+ "politicises": "politicizes",
1217
+ "politicising": "politicizing",
1218
+ "popularisation": "popularization",
1219
+ "popularise": "popularize",
1220
+ "popularised": "popularized",
1221
+ "popularises": "popularizes",
1222
+ "popularising": "popularizing",
1223
+ "pouffe": "pouf",
1224
+ "pouffes": "poufs",
1225
+ "practise": "practice",
1226
+ "practised": "practiced",
1227
+ "practises": "practices",
1228
+ "practising": "practicing",
1229
+ "praesidium": "presidium",
1230
+ "praesidiums": "presidiums",
1231
+ "pressurisation": "pressurization",
1232
+ "pressurise": "pressurize",
1233
+ "pressurised": "pressurized",
1234
+ "pressurises": "pressurizes",
1235
+ "pressurising": "pressurizing",
1236
+ "pretence": "pretense",
1237
+ "pretences": "pretenses",
1238
+ "primaeval": "primeval",
1239
+ "prioritisation": "prioritization",
1240
+ "prioritise": "prioritize",
1241
+ "prioritised": "prioritized",
1242
+ "prioritises": "prioritizes",
1243
+ "prioritising": "prioritizing",
1244
+ "privatisation": "privatization",
1245
+ "privatisations": "privatizations",
1246
+ "privatise": "privatize",
1247
+ "privatised": "privatized",
1248
+ "privatises": "privatizes",
1249
+ "privatising": "privatizing",
1250
+ "professionalisation": "professionalization",
1251
+ "professionalise": "professionalize",
1252
+ "professionalised": "professionalized",
1253
+ "professionalises": "professionalizes",
1254
+ "professionalising": "professionalizing",
1255
+ "programme": "program",
1256
+ "programmes": "programs",
1257
+ "prologue": "prolog",
1258
+ "prologues": "prologs",
1259
+ "propagandise": "propagandize",
1260
+ "propagandised": "propagandized",
1261
+ "propagandises": "propagandizes",
1262
+ "propagandising": "propagandizing",
1263
+ "proselytise": "proselytize",
1264
+ "proselytised": "proselytized",
1265
+ "proselytiser": "proselytizer",
1266
+ "proselytisers": "proselytizers",
1267
+ "proselytises": "proselytizes",
1268
+ "proselytising": "proselytizing",
1269
+ "psychoanalyse": "psychoanalyze",
1270
+ "psychoanalysed": "psychoanalyzed",
1271
+ "psychoanalyses": "psychoanalyzes",
1272
+ "psychoanalysing": "psychoanalyzing",
1273
+ "publicise": "publicize",
1274
+ "publicised": "publicized",
1275
+ "publicises": "publicizes",
1276
+ "publicising": "publicizing",
1277
+ "pulverisation": "pulverization",
1278
+ "pulverise": "pulverize",
1279
+ "pulverised": "pulverized",
1280
+ "pulverises": "pulverizes",
1281
+ "pulverising": "pulverizing",
1282
+ "pummelled": "pummel",
1283
+ "pummelling": "pummeled",
1284
+ "pyjama": "pajama",
1285
+ "pyjamas": "pajamas",
1286
+ "pzazz": "pizzazz",
1287
+ "quarrelled": "quarreled",
1288
+ "quarrelling": "quarreling",
1289
+ "radicalise": "radicalize",
1290
+ "radicalised": "radicalized",
1291
+ "radicalises": "radicalizes",
1292
+ "radicalising": "radicalizing",
1293
+ "rancour": "rancor",
1294
+ "randomise": "randomize",
1295
+ "randomised": "randomized",
1296
+ "randomises": "randomizes",
1297
+ "randomising": "randomizing",
1298
+ "rationalisation": "rationalization",
1299
+ "rationalisations": "rationalizations",
1300
+ "rationalise": "rationalize",
1301
+ "rationalised": "rationalized",
1302
+ "rationalises": "rationalizes",
1303
+ "rationalising": "rationalizing",
1304
+ "ravelled": "raveled",
1305
+ "ravelling": "raveling",
1306
+ "realisable": "realizable",
1307
+ "realisation": "realization",
1308
+ "realisations": "realizations",
1309
+ "realise": "realize",
1310
+ "realised": "realized",
1311
+ "realises": "realizes",
1312
+ "realising": "realizing",
1313
+ "recognisable": "recognizable",
1314
+ "recognisably": "recognizably",
1315
+ "recognisance": "recognizance",
1316
+ "recognise": "recognize",
1317
+ "recognised": "recognized",
1318
+ "recognises": "recognizes",
1319
+ "recognising": "recognizing",
1320
+ "reconnoitre": "reconnoiter",
1321
+ "reconnoitred": "reconnoitered",
1322
+ "reconnoitres": "reconnoiters",
1323
+ "reconnoitring": "reconnoitering",
1324
+ "refuelled": "refueled",
1325
+ "refuelling": "refueling",
1326
+ "regularisation": "regularization",
1327
+ "regularise": "regularize",
1328
+ "regularised": "regularized",
1329
+ "regularises": "regularizes",
1330
+ "regularising": "regularizing",
1331
+ "remodelled": "remodeled",
1332
+ "remodelling": "remodeling",
1333
+ "remould": "remold",
1334
+ "remoulded": "remolded",
1335
+ "remoulding": "remolding",
1336
+ "remoulds": "remolds",
1337
+ "reorganisation": "reorganization",
1338
+ "reorganisations": "reorganizations",
1339
+ "reorganise": "reorganize",
1340
+ "reorganised": "reorganized",
1341
+ "reorganises": "reorganizes",
1342
+ "reorganising": "reorganizing",
1343
+ "revelled": "reveled",
1344
+ "reveller": "reveler",
1345
+ "revellers": "revelers",
1346
+ "revelling": "reveling",
1347
+ "revitalise": "revitalize",
1348
+ "revitalised": "revitalized",
1349
+ "revitalises": "revitalizes",
1350
+ "revitalising": "revitalizing",
1351
+ "revolutionise": "revolutionize",
1352
+ "revolutionised": "revolutionized",
1353
+ "revolutionises": "revolutionizes",
1354
+ "revolutionising": "revolutionizing",
1355
+ "rhapsodise": "rhapsodize",
1356
+ "rhapsodised": "rhapsodized",
1357
+ "rhapsodises": "rhapsodizes",
1358
+ "rhapsodising": "rhapsodizing",
1359
+ "rigour": "rigor",
1360
+ "rigours": "rigors",
1361
+ "ritualised": "ritualized",
1362
+ "rivalled": "rivaled",
1363
+ "rivalling": "rivaling",
1364
+ "romanticise": "romanticize",
1365
+ "romanticised": "romanticized",
1366
+ "romanticises": "romanticizes",
1367
+ "romanticising": "romanticizing",
1368
+ "rumour": "rumor",
1369
+ "rumoured": "rumored",
1370
+ "rumours": "rumors",
1371
+ "sabre": "saber",
1372
+ "sabres": "sabers",
1373
+ "saltpetre": "saltpeter",
1374
+ "sanitise": "sanitize",
1375
+ "sanitised": "sanitized",
1376
+ "sanitises": "sanitizes",
1377
+ "sanitising": "sanitizing",
1378
+ "satirise": "satirize",
1379
+ "satirised": "satirized",
1380
+ "satirises": "satirizes",
1381
+ "satirising": "satirizing",
1382
+ "saviour": "savior",
1383
+ "saviours": "saviors",
1384
+ "savour": "savor",
1385
+ "savoured": "savored",
1386
+ "savouries": "savories",
1387
+ "savouring": "savoring",
1388
+ "savours": "savors",
1389
+ "savoury": "savory",
1390
+ "scandalise": "scandalize",
1391
+ "scandalised": "scandalized",
1392
+ "scandalises": "scandalizes",
1393
+ "scandalising": "scandalizing",
1394
+ "sceptic": "skeptic",
1395
+ "sceptical": "skeptical",
1396
+ "sceptically": "skeptically",
1397
+ "scepticism": "skepticism",
1398
+ "sceptics": "skeptics",
1399
+ "sceptre": "scepter",
1400
+ "sceptres": "scepters",
1401
+ "scrutinise": "scrutinize",
1402
+ "scrutinised": "scrutinized",
1403
+ "scrutinises": "scrutinizes",
1404
+ "scrutinising": "scrutinizing",
1405
+ "secularisation": "secularization",
1406
+ "secularise": "secularize",
1407
+ "secularised": "secularized",
1408
+ "secularises": "secularizes",
1409
+ "secularising": "secularizing",
1410
+ "sensationalise": "sensationalize",
1411
+ "sensationalised": "sensationalized",
1412
+ "sensationalises": "sensationalizes",
1413
+ "sensationalising": "sensationalizing",
1414
+ "sensitise": "sensitize",
1415
+ "sensitised": "sensitized",
1416
+ "sensitises": "sensitizes",
1417
+ "sensitising": "sensitizing",
1418
+ "sentimentalise": "sentimentalize",
1419
+ "sentimentalised": "sentimentalized",
1420
+ "sentimentalises": "sentimentalizes",
1421
+ "sentimentalising": "sentimentalizing",
1422
+ "sepulchre": "sepulcher",
1423
+ "sepulchres": "sepulchers",
1424
+ "serialisation": "serialization",
1425
+ "serialisations": "serializations",
1426
+ "serialise": "serialize",
1427
+ "serialised": "serialized",
1428
+ "serialises": "serializes",
1429
+ "serialising": "serializing",
1430
+ "sermonise": "sermonize",
1431
+ "sermonised": "sermonized",
1432
+ "sermonises": "sermonizes",
1433
+ "sermonising": "sermonizing",
1434
+ "sheikh": "sheik",
1435
+ "shovelled": "shoveled",
1436
+ "shovelling": "shoveling",
1437
+ "shrivelled": "shriveled",
1438
+ "shrivelling": "shriveling",
1439
+ "signalise": "signalize",
1440
+ "signalised": "signalized",
1441
+ "signalises": "signalizes",
1442
+ "signalising": "signalizing",
1443
+ "signalled": "signaled",
1444
+ "signalling": "signaling",
1445
+ "smoulder": "smolder",
1446
+ "smouldered": "smoldered",
1447
+ "smouldering": "smoldering",
1448
+ "smoulders": "smolders",
1449
+ "snivelled": "sniveled",
1450
+ "snivelling": "sniveling",
1451
+ "snorkelled": "snorkeled",
1452
+ "snorkelling": "snorkeling",
1453
+ "snowplough": "snowplow",
1454
+ "snowploughs": "snowplow",
1455
+ "socialisation": "socialization",
1456
+ "socialise": "socialize",
1457
+ "socialised": "socialized",
1458
+ "socialises": "socializes",
1459
+ "socialising": "socializing",
1460
+ "sodomise": "sodomize",
1461
+ "sodomised": "sodomized",
1462
+ "sodomises": "sodomizes",
1463
+ "sodomising": "sodomizing",
1464
+ "solemnise": "solemnize",
1465
+ "solemnised": "solemnized",
1466
+ "solemnises": "solemnizes",
1467
+ "solemnising": "solemnizing",
1468
+ "sombre": "somber",
1469
+ "specialisation": "specialization",
1470
+ "specialisations": "specializations",
1471
+ "specialise": "specialize",
1472
+ "specialised": "specialized",
1473
+ "specialises": "specializes",
1474
+ "specialising": "specializing",
1475
+ "spectre": "specter",
1476
+ "spectres": "specters",
1477
+ "spiralled": "spiraled",
1478
+ "spiralling": "spiraling",
1479
+ "splendour": "splendor",
1480
+ "splendours": "splendors",
1481
+ "squirrelled": "squirreled",
1482
+ "squirrelling": "squirreling",
1483
+ "stabilisation": "stabilization",
1484
+ "stabilise": "stabilize",
1485
+ "stabilised": "stabilized",
1486
+ "stabiliser": "stabilizer",
1487
+ "stabilisers": "stabilizers",
1488
+ "stabilises": "stabilizes",
1489
+ "stabilising": "stabilizing",
1490
+ "standardisation": "standardization",
1491
+ "standardise": "standardize",
1492
+ "standardised": "standardized",
1493
+ "standardises": "standardizes",
1494
+ "standardising": "standardizing",
1495
+ "stencilled": "stenciled",
1496
+ "stencilling": "stenciling",
1497
+ "sterilisation": "sterilization",
1498
+ "sterilisations": "sterilizations",
1499
+ "sterilise": "sterilize",
1500
+ "sterilised": "sterilized",
1501
+ "steriliser": "sterilizer",
1502
+ "sterilisers": "sterilizers",
1503
+ "sterilises": "sterilizes",
1504
+ "sterilising": "sterilizing",
1505
+ "stigmatisation": "stigmatization",
1506
+ "stigmatise": "stigmatize",
1507
+ "stigmatised": "stigmatized",
1508
+ "stigmatises": "stigmatizes",
1509
+ "stigmatising": "stigmatizing",
1510
+ "storey": "story",
1511
+ "storeys": "stories",
1512
+ "subsidisation": "subsidization",
1513
+ "subsidise": "subsidize",
1514
+ "subsidised": "subsidized",
1515
+ "subsidiser": "subsidizer",
1516
+ "subsidisers": "subsidizers",
1517
+ "subsidises": "subsidizes",
1518
+ "subsidising": "subsidizing",
1519
+ "succour": "succor",
1520
+ "succoured": "succored",
1521
+ "succouring": "succoring",
1522
+ "succours": "succors",
1523
+ "sulphate": "sulfate",
1524
+ "sulphates": "sulfates",
1525
+ "sulphide": "sulfide",
1526
+ "sulphides": "sulfides",
1527
+ "sulphur": "sulfur",
1528
+ "sulphurous": "sulfurous",
1529
+ "summarise": "summarize",
1530
+ "summarised": "summarized",
1531
+ "summarises": "summarizes",
1532
+ "summarising": "summarizing",
1533
+ "swivelled": "swiveled",
1534
+ "swivelling": "swiveling",
1535
+ "symbolise": "symbolize",
1536
+ "symbolised": "symbolized",
1537
+ "symbolises": "symbolizes",
1538
+ "symbolising": "symbolizing",
1539
+ "sympathise": "sympathize",
1540
+ "sympathised": "sympathized",
1541
+ "sympathiser": "sympathizer",
1542
+ "sympathisers": "sympathizers",
1543
+ "sympathises": "sympathizes",
1544
+ "sympathising": "sympathizing",
1545
+ "synchronisation": "synchronization",
1546
+ "synchronise": "synchronize",
1547
+ "synchronised": "synchronized",
1548
+ "synchronises": "synchronizes",
1549
+ "synchronising": "synchronizing",
1550
+ "synthesise": "synthesize",
1551
+ "synthesised": "synthesized",
1552
+ "synthesiser": "synthesizer",
1553
+ "synthesisers": "synthesizers",
1554
+ "synthesises": "synthesizes",
1555
+ "synthesising": "synthesizing",
1556
+ "syphon": "siphon",
1557
+ "syphoned": "siphoned",
1558
+ "syphoning": "siphoning",
1559
+ "syphons": "siphons",
1560
+ "systematisation": "systematization",
1561
+ "systematise": "systematize",
1562
+ "systematised": "systematized",
1563
+ "systematises": "systematizes",
1564
+ "systematising": "systematizing",
1565
+ "tantalise": "tantalize",
1566
+ "tantalised": "tantalized",
1567
+ "tantalises": "tantalizes",
1568
+ "tantalising": "tantalizing",
1569
+ "tantalisingly": "tantalizingly",
1570
+ "tasselled": "tasseled",
1571
+ "technicolour": "technicolor",
1572
+ "temporise": "temporize",
1573
+ "temporised": "temporized",
1574
+ "temporises": "temporizes",
1575
+ "temporising": "temporizing",
1576
+ "tenderise": "tenderize",
1577
+ "tenderised": "tenderized",
1578
+ "tenderises": "tenderizes",
1579
+ "tenderising": "tenderizing",
1580
+ "terrorise": "terrorize",
1581
+ "terrorised": "terrorized",
1582
+ "terrorises": "terrorizes",
1583
+ "terrorising": "terrorizing",
1584
+ "theatre": "theater",
1585
+ "theatregoer": "theatergoer",
1586
+ "theatregoers": "theatergoers",
1587
+ "theatres": "theaters",
1588
+ "theorise": "theorize",
1589
+ "theorised": "theorized",
1590
+ "theorises": "theorizes",
1591
+ "theorising": "theorizing",
1592
+ "tonne": "ton",
1593
+ "tonnes": "tons",
1594
+ "towelled": "toweled",
1595
+ "towelling": "toweling",
1596
+ "toxaemia": "toxemia",
1597
+ "tranquillise": "tranquilize",
1598
+ "tranquillised": "tranquilized",
1599
+ "tranquilliser": "tranquilizer",
1600
+ "tranquillisers": "tranquilizers",
1601
+ "tranquillises": "tranquilizes",
1602
+ "tranquillising": "tranquilizing",
1603
+ "tranquillity": "tranquility",
1604
+ "tranquillize": "tranquilize",
1605
+ "tranquillized": "tranquilized",
1606
+ "tranquillizer": "tranquilizer",
1607
+ "tranquillizers": "tranquilizers",
1608
+ "tranquillizes": "tranquilizes",
1609
+ "tranquillizing": "tranquilizing",
1610
+ "tranquilly": "tranquility",
1611
+ "transistorised": "transistorized",
1612
+ "traumatise": "traumatize",
1613
+ "traumatised": "traumatized",
1614
+ "traumatises": "traumatizes",
1615
+ "traumatising": "traumatizing",
1616
+ "travelled": "traveled",
1617
+ "traveller": "traveler",
1618
+ "travellers": "travelers",
1619
+ "travelling": "traveling",
1620
+ "travelog": "travelogue",
1621
+ "travelogs": "travelogues",
1622
+ "trialled": "trialed",
1623
+ "trialling": "trialing",
1624
+ "tricolour": "tricolor",
1625
+ "tricolours": "tricolors",
1626
+ "trivialise": "trivialize",
1627
+ "trivialised": "trivialized",
1628
+ "trivialises": "trivializes",
1629
+ "trivialising": "trivializing",
1630
+ "tumour": "tumor",
1631
+ "tumours": "tumors",
1632
+ "tunnelled": "tunneled",
1633
+ "tunnelling": "tunneling",
1634
+ "tyrannise": "tyrannize",
1635
+ "tyrannised": "tyrannized",
1636
+ "tyrannises": "tyrannizes",
1637
+ "tyrannising": "tyrannizing",
1638
+ "tyre": "tire",
1639
+ "tyres": "tires",
1640
+ "unauthorised": "unauthorized",
1641
+ "uncivilised": "uncivilized",
1642
+ "underutilised": "underutilized",
1643
+ "unequalled": "unequaled",
1644
+ "unfavourable": "unfavorable",
1645
+ "unfavourably": "unfavorably",
1646
+ "unionisation": "unionization",
1647
+ "unionise": "unionize",
1648
+ "unionised": "unionized",
1649
+ "unionises": "unionizes",
1650
+ "unionising": "unionizing",
1651
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1652
+ "unravelled": "unraveled",
1653
+ "unravelling": "unraveling",
1654
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1655
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1656
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1657
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1658
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1659
+ "urbanisation": "urbanization",
1660
+ "urbanise": "urbanize",
1661
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1662
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1663
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1664
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1665
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1666
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1667
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1668
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1669
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1670
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1671
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1672
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1673
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1674
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1675
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1676
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1677
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1678
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1679
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1680
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1681
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1682
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1683
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1684
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1685
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1686
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1687
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1688
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1689
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1690
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1691
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1692
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1693
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1694
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1695
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1696
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1697
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1698
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1699
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1700
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1701
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1702
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1703
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1704
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1705
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1706
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1707
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1708
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1709
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1710
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1711
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1712
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1713
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1714
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1715
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1716
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1718
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1720
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1721
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1722
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1723
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1724
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1725
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1726
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1727
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1728
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1729
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1730
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1731
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1732
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1734
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1736
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1737
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1739
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