0308-185944-Detecting_input_language_automatically.

app.py

@@ -6,6 +6,7 @@ from time import time as ttime
 from my_utils import load_audio
 from transformers import pipeline
 from text.cleaner import clean_text
+from polyglot.detect import  Detector
 from feature_extractor import cnhubert
 from timeit import default_timer as timer
 from text import cleaned_text_to_sequence
@@ -29,7 +30,6 @@ logging.getLogger("multipart").setLevel(logging.WARNING)
 from download import *
 download()
 
-
 if "_CUDA_VISIBLE_DEVICES" in os.environ:
     os.environ["CUDA_VISIBLE_DEVICES"] = os.environ["_CUDA_VISIBLE_DEVICES"]
 tz = pytz.timezone('Asia/Singapore')
@@ -372,7 +372,12 @@ def get_tts_wav(ref_wav_path, prompt_text, prompt_language, text, text_language,
     tprint(f'🏕️LOADED GPT Model: {gpt_path}')
 
     prompt_language = dict_language[prompt_language]
-    text_language = dict_language[text_language]
+    try:
+        text_language = dict_language[text_language]
+    except KeyError as e:
+        wprint(f"Not supported language types/不支持此語言: {e}")
+        return None
+        
     prompt_text = prompt_text.strip("\n")
     if (prompt_text[-1] not in splits): prompt_text += "。" if prompt_language != "en" else "."
     text = text.strip("\n")
@@ -584,6 +589,8 @@ def custom_sort_key(s):
     parts = [int(part) if part.isdigit() else part for part in parts]
     return parts
 
+#==========custom functions============
+
 def tprint(text):
     now=datetime.now(tz).strftime('%H:%M:%S')
     print(f'UTC+8 - {now} - {text}')
@@ -592,7 +599,25 @@ def wprint(text):
     tprint(text)
     gr.Warning(text)
 
-#裁切文本
+def lang_detector(text):
+    min_chars = 5
+    if len(text) < min_chars:
+        return "Input text too short/输入文本太短"
+    try:
+        detector = Detector(text).language
+        lang_info = str(detector)
+        code = re.search(r"code: (\w+)", lang_info).group(1)
+        if code == 'ja':
+            return "日本語"
+        elif code == 'zh':
+            return "中文"
+        elif code == 'en':
+            return 'English'
+        else:
+            return re.search(r"name: (\w+)", lang_info).group(1)
+    except Exception as e:
+        return f"ERROR：{str(e)}"
+        
 def trim_text(text,language): 
     limit_cj = 120 #character
     limit_en = 60 #words  
@@ -755,15 +780,21 @@ with gr.Blocks(theme='Kasien/ali_theme_custom') as app:
         chinese_choice = gr.Radio(chinese_models, label="CN|中文模型",scale=2)
         japanese_choice = gr.Radio(japanese_models, label="JP|日本語モデル",scale=4)
 
-    plsh='Text must match the selected language option to prevent errors, for example, if English is input but Chinese is selected for generation.\n文字一定要和语言选项匹配，不然要报错，比如输入的是英文，生成语言选中文'
+    plsh='Input any text you like / 輸入任意文字'
     limit='Max 70 words. Excess will be ignored./单次最多处理120字左右，多余的会被忽略'
 
     gr.HTML('''
     <b>输入文字</b>''')
     with gr.Row():
-        model_name = gr.Textbox(label="Seleted Model/已选模型", value=default_model_name, scale=1) 
-        text = gr.Textbox(label="Input some text for voice generation/输入想要生成语音的文字", lines=5,scale=8,
+        with gr.Column(scale=2): 
+            model_name = gr.Textbox(label="Seleted Model/已选模型", value=default_model_name, scale=1) 
+            text_language = gr.Textbox(
+            label="Select language for input text/输入的文字对应语言",
+            info='Automatic detection of input language type.',scale=1,interactive=False
+            ) 
+        text = gr.Textbox(label="Input some text for voice generation/输入想要生成语音的文字", lines=5,scale=6,
         placeholder=plsh,info=limit)
+        text.change( lang_detector, text, text_language)
 
 
     with gr.Row():
@@ -773,15 +804,7 @@ with gr.Blocks(theme='Kasien/ali_theme_custom') as app:
             choices=["tone1","tone2","tone3"],
             value="tone1",
             info='Tone influences the emotional expression ',scale=1)
-            
-            text_language = gr.Radio(
-            label="Select language for input text/输入的文字对应语言",
-            choices=["中文","English","日本語"],
-            value=default_language,
-            info='Input text and language must match.',scale=1,
-            ) 
-        
-        tone_sample=gr.Audio(label="🔊Preview tone/试听语气 ", scale=5)
+        tone_sample=gr.Audio(label="🔊Preview tone/试听语气 ", scale=6)
 
 
     with gr.Accordion(label="prpt voice", open=False,visible=False):
@@ -789,8 +812,8 @@ with gr.Blocks(theme='Kasien/ali_theme_custom') as app:
             inp_ref = gr.Audio(label="Reference audio", type="filepath", value=default_voice_wav, scale=3)
             prompt_text = gr.Textbox(label="Reference text", value=default_voice_wav_words, scale=3)
             prompt_language = gr.Dropdown(label="Language of the reference audio", choices=["中文", "English", "日本語"], value=default_language, scale=1,interactive=False)
-
-    
+            dummy = gr.Radio(choices=["中文","English","日本語"],visible=False)
+     
     
     with gr.Accordion(label="Additional generation options/附加生成选项", open=False):
         how_to_cut = gr.Dropdown(
@@ -807,8 +830,8 @@ with gr.Blocks(theme='Kasien/ali_theme_custom') as app:
     gr.HTML('''
     <b>开始生成</b>''')
     with gr.Row():
-        main_button = gr.Button("✨Generate Voice", variant="primary", scale=1)
-        output = gr.Audio(label="💾Download it by clicking ⬇️", scale=3)
+        main_button = gr.Button("✨Generate Voice", variant="primary", scale=2)
+        output = gr.Audio(label="💾Download it by clicking ⬇️", scale=6)
         #info = gr.Textbox(label="INFO", visible=True, readonly=True, scale=1)
 
     gr.HTML('''
@@ -822,18 +845,20 @@ with gr.Blocks(theme='Kasien/ali_theme_custom') as app:
     
     with gr.Row():
         user_voice = gr.Audio(type="filepath", label="（3~10s）Upload or Record audio/上传或录制声音",scale=3)
-        user_lang = gr.Dropdown(label="Language/生成语言", choices=["中文", "English", "日本語"],scale=1,value='English')
-        user_text= gr.Textbox(label="Text for generation/输入想要生成语音的文字", lines=5,scale=5,
+        with gr.Column(scale=7): 
+            user_lang = gr.Textbox(label="Language/生成语言",info='Automatic detection of input language type.',interactive=False)
+            user_text= gr.Textbox(label="Text for generation/输入想要生成语音的文字", lines=5,
         placeholder=plsh,info=limit)
-  
+    user_text.change( lang_detector, user_text, user_lang)
+
     user_button = gr.Button("✨Clone Voice", variant="primary")
     user_output = gr.Audio(label="💾Download it by clicking ⬇️")
 
     gr.HTML('''<div align=center><img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.laobi.icu/badge?page_id=Ailyth/DLMP9" /></div>''')
     
-    english_choice.change(update_model, inputs=[english_choice], outputs=[inp_ref, prompt_text, prompt_language, text_language, model_name, tone_select, tone_sample])
-    chinese_choice.change(update_model, inputs=[chinese_choice], outputs=[inp_ref, prompt_text, prompt_language, text_language, model_name, tone_select, tone_sample])
-    japanese_choice.change(update_model, inputs=[japanese_choice], outputs=[inp_ref, prompt_text, prompt_language, text_language, model_name, tone_select, tone_sample])
+    english_choice.change(update_model, inputs=[english_choice], outputs=[inp_ref, prompt_text, prompt_language,dummy,model_name, tone_select, tone_sample])
+    chinese_choice.change(update_model, inputs=[chinese_choice], outputs=[inp_ref, prompt_text, prompt_language, dummy,model_name, tone_select, tone_sample])
+    japanese_choice.change(update_model, inputs=[japanese_choice], outputs=[inp_ref, prompt_text, prompt_language,dummy,model_name, tone_select, tone_sample])
     tone_select.change(update_tone, inputs=[model_name, tone_select], outputs=[inp_ref, prompt_text, tone_sample])
     
     main_button.click(

pretrained_models/whisper-small/README.md

@@ -1,452 +0,0 @@
----
-language: 
-- en
-- zh
-- de
-- es
-- ru
-- ko
-- fr
-- ja
-- pt
-- tr
-- pl
-- ca
-- nl
-- ar
-- sv
-- it
-- id
-- hi
-- fi
-- vi
-- he
-- uk
-- el
-- ms
-- cs
-- ro
-- da
-- hu
-- ta
-- no
-- th
-- ur
-- hr
-- bg
-- lt
-- la
-- mi
-- ml
-- cy
-- sk
-- te
-- fa
-- lv
-- bn
-- sr
-- az
-- sl
-- kn
-- et
-- mk
-- br
-- eu
-- is
-- hy
-- ne
-- mn
-- bs
-- kk
-- sq
-- sw
-- gl
-- mr
-- pa
-- si
-- km
-- sn
-- yo
-- so
-- af
-- oc
-- ka
-- be
-- tg
-- sd
-- gu
-- am
-- yi
-- lo
-- uz
-- fo
-- ht
-- ps
-- tk
-- nn
-- mt
-- sa
-- lb
-- my
-- bo
-- tl
-- mg
-- as
-- tt
-- haw
-- ln
-- ha
-- ba
-- jw
-- su
-tags:
-- audio
-- automatic-speech-recognition
-- hf-asr-leaderboard
-widget:
-- example_title: Librispeech sample 1
-  src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
-- example_title: Librispeech sample 2
-  src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
-model-index:
-- name: whisper-small
-  results:
-  - task:
-      name: Automatic Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: LibriSpeech (clean)
-      type: librispeech_asr
-      config: clean
-      split: test
-      args: 
-        language: en
-    metrics:
-    - name: Test WER
-      type: wer
-      value: 3.432213777886737
-  - task:
-      name: Automatic Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: LibriSpeech (other)
-      type: librispeech_asr
-      config: other
-      split: test
-      args: 
-        language: en
-    metrics:
-    - name: Test WER
-      type: wer
-      value: 7.628304527060248
-  - task:
-      name: Automatic Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: Common Voice 11.0
-      type: mozilla-foundation/common_voice_11_0
-      config: hi
-      split: test
-      args:
-        language: hi
-    metrics:
-    - name: Test WER
-      type: wer
-      value: 87.3
-  - task:
-      name: Automatic Speech Recognition
-      type: automatic-speech-recognition
-    dataset:
-      name: Common Voice 13.0
-      type: mozilla-foundation/common_voice_13_0
-      config: dv
-      split: test
-      args:
-        language: dv
-    metrics:
-    - name: Wer
-      type: wer
-      value: 125.69809089960707
-pipeline_tag: automatic-speech-recognition
-license: apache-2.0
----
-
-# Whisper
-
-Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours 
-of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need 
-for fine-tuning.
-
-Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356) 
-by Alec Radford et al from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper).
-
-**Disclaimer**: Content for this model card has partly been written by the Hugging Face team, and parts of it were 
-copied and pasted from the original model card.
-
-## Model details
-
-Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model. 
-It was trained on 680k hours of labelled speech data annotated using large-scale weak supervision. 
-
-The models were trained on either English-only data or multilingual data. The English-only models were trained 
-on the task of speech recognition. The multilingual models were trained on both speech recognition and speech 
-translation. For speech recognition, the model predicts transcriptions in the *same* language as the audio. 
-For speech translation, the model predicts transcriptions to a *different* language to the audio.
-
-Whisper checkpoints come in five configurations of varying model sizes.
-The smallest four are trained on either English-only or multilingual data.
-The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints 
-are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The 
-checkpoints are summarised in the following table with links to the models on the Hub:
-
-| Size     | Parameters | English-only                                         | Multilingual                                        |
-|----------|------------|------------------------------------------------------|-----------------------------------------------------|
-| tiny     | 39 M       | [✓](https://huggingface.co/openai/whisper-tiny.en)   | [✓](https://huggingface.co/openai/whisper-tiny)     |
-| base     | 74 M       | [✓](https://huggingface.co/openai/whisper-base.en)   | [✓](https://huggingface.co/openai/whisper-base)     |
-| small    | 244 M      | [✓](https://huggingface.co/openai/whisper-small.en)  | [✓](https://huggingface.co/openai/whisper-small)    |
-| medium   | 769 M      | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium)   |
-| large    | 1550 M     | x                                                    | [✓](https://huggingface.co/openai/whisper-large)    |
-| large-v2 | 1550 M     | x                                                    | [✓](https://huggingface.co/openai/whisper-large-v2) |
-
-# Usage
-
-To transcribe audio samples, the model has to be used alongside a [`WhisperProcessor`](https://huggingface.co/docs/transformers/model_doc/whisper#transformers.WhisperProcessor).
-
-The `WhisperProcessor` is used to:
-1. Pre-process the audio inputs (converting them to log-Mel spectrograms for the model)
-2. Post-process the model outputs (converting them from tokens to text)
-
-The model is informed of which task to perform (transcription or translation) by passing the appropriate "context tokens". These context tokens 
-are a sequence of tokens that are given to the decoder at the start of the decoding process, and take the following order:
-1. The transcription always starts with the `<|startoftranscript|>` token
-2. The second token is the language token (e.g. `<|en|>` for English)
-3. The third token is the "task token". It can take one of two values: `<|transcribe|>` for speech recognition or `<|translate|>` for speech translation
-4. In addition, a `<|notimestamps|>` token is added if the model should not include timestamp prediction
-
-Thus, a typical sequence of context tokens might look as follows:
-```
-<|startoftranscript|> <|en|> <|transcribe|> <|notimestamps|>
-```
-Which tells the model to decode in English, under the task of speech recognition, and not to predict timestamps.
-
-These tokens can either be forced or un-forced. If they are forced, the model is made to predict each token at 
-each position. This allows one to control the output language and task for the Whisper model. If they are un-forced, 
-the Whisper model will automatically predict the output langauge and task itself.
-
-The context tokens can be set accordingly:
-
-```python
-model.config.forced_decoder_ids = WhisperProcessor.get_decoder_prompt_ids(language="english", task="transcribe")
-```
-
-Which forces the model to predict in English under the task of speech recognition.
-
-## Transcription
-
-### English to English 
-In this example, the context tokens are 'unforced', meaning the model automatically predicts the output language
-(English) and task (transcribe).
-
-```python
->>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
->>> from datasets import load_dataset
-
->>> # load model and processor
->>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
->>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
->>> model.config.forced_decoder_ids = None
-
->>> # load dummy dataset and read audio files
->>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
->>> sample = ds[0]["audio"]
->>> input_features = processor(sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features 
-
->>> # generate token ids
->>> predicted_ids = model.generate(input_features)
->>> # decode token ids to text
->>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
-['<|startoftranscript|><|en|><|transcribe|><|notimestamps|> Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.<|endoftext|>']
-
->>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
-[' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.']
-```
-The context tokens can be removed from the start of the transcription by setting `skip_special_tokens=True`.
-
-### French to French 
-The following example demonstrates French to French transcription by setting the decoder ids appropriately. 
-
-```python
->>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
->>> from datasets import Audio, load_dataset
-
->>> # load model and processor
->>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
->>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
->>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="transcribe")
-
->>> # load streaming dataset and read first audio sample
->>> ds = load_dataset("common_voice", "fr", split="test", streaming=True)
->>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
->>> input_speech = next(iter(ds))["audio"]
->>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features
-
->>> # generate token ids
->>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
->>> # decode token ids to text
->>> transcription = processor.batch_decode(predicted_ids)
-['<|startoftranscript|><|fr|><|transcribe|><|notimestamps|> Un vrai travail intéressant va enfin être mené sur ce sujet.<|endoftext|>']
-
->>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
-[' Un vrai travail intéressant va enfin être mené sur ce sujet.']
-```
-
-## Translation 
-Setting the task to "translate" forces the Whisper model to perform speech translation.
-
-### French to English
-
-```python
->>> from transformers import WhisperProcessor, WhisperForConditionalGeneration
->>> from datasets import Audio, load_dataset
-
->>> # load model and processor
->>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
->>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small")
->>> forced_decoder_ids = processor.get_decoder_prompt_ids(language="french", task="translate")
-
->>> # load streaming dataset and read first audio sample
->>> ds = load_dataset("common_voice", "fr", split="test", streaming=True)
->>> ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
->>> input_speech = next(iter(ds))["audio"]
->>> input_features = processor(input_speech["array"], sampling_rate=input_speech["sampling_rate"], return_tensors="pt").input_features
-
->>> # generate token ids
->>> predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
->>> # decode token ids to text
->>> transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
-[' A very interesting work, we will finally be given on this subject.']
-```
-
-## Evaluation
-
-This code snippet shows how to evaluate Whisper Small on [LibriSpeech test-clean](https://huggingface.co/datasets/librispeech_asr):
- 
-```python
->>> from datasets import load_dataset
->>> from transformers import WhisperForConditionalGeneration, WhisperProcessor
->>> import torch
->>> from evaluate import load
-
->>> librispeech_test_clean = load_dataset("librispeech_asr", "clean", split="test")
-
->>> processor = WhisperProcessor.from_pretrained("openai/whisper-small")
->>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to("cuda")
-
->>> def map_to_pred(batch):
->>>     audio = batch["audio"]
->>>     input_features = processor(audio["array"], sampling_rate=audio["sampling_rate"], return_tensors="pt").input_features
->>>     batch["reference"] = processor.tokenizer._normalize(batch['text'])
->>> 
->>>     with torch.no_grad():
->>>         predicted_ids = model.generate(input_features.to("cuda"))[0]
->>>     transcription = processor.decode(predicted_ids)
->>>     batch["prediction"] = processor.tokenizer._normalize(transcription)
->>>     return batch
-
->>> result = librispeech_test_clean.map(map_to_pred)
-
->>> wer = load("wer")
->>> print(100 * wer.compute(references=result["reference"], predictions=result["prediction"]))
-3.432213777886737
-```
-
-## Long-Form Transcription
-
-The Whisper model is intrinsically designed to work on audio samples of up to 30s in duration. However, by using a chunking 
-algorithm, it can be used to transcribe audio samples of up to arbitrary length. This is possible through Transformers 
-[`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline) 
-method. Chunking is enabled by setting `chunk_length_s=30` when instantiating the pipeline. With chunking enabled, the pipeline 
-can be run with batched inference. It can also be extended to predict sequence level timestamps by passing `return_timestamps=True`:
-
-```python
->>> import torch
->>> from transformers import pipeline
->>> from datasets import load_dataset
-
->>> device = "cuda:0" if torch.cuda.is_available() else "cpu"
-
->>> pipe = pipeline(
->>>   "automatic-speech-recognition",
->>>   model="openai/whisper-small",
->>>   chunk_length_s=30,
->>>   device=device,
->>> )
-
->>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
->>> sample = ds[0]["audio"]
-
->>> prediction = pipe(sample.copy(), batch_size=8)["text"]
-" Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
-
->>> # we can also return timestamps for the predictions
->>> prediction = pipe(sample.copy(), batch_size=8, return_timestamps=True)["chunks"]
-[{'text': ' Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel.',
-  'timestamp': (0.0, 5.44)}]
-```
-
-Refer to the blog post [ASR Chunking](https://huggingface.co/blog/asr-chunking) for more details on the chunking algorithm.
-
-## Fine-Tuning
-
-The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However, 
-its predictive capabilities can be improved further for certain languages and tasks through *fine-tuning*. The blog 
-post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step 
-guide to fine-tuning the Whisper model with as little as 5 hours of labelled data.
-
-### Evaluated Use
-
-The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research.
-
-The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them.
-
-In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes.
-
-
-## Training Data
-
-The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. 
-
-As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language.
-
-
-## Performance and Limitations
-
-Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. 
-
-However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself.
-
-Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). 
-
-In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages.
-
-
-## Broader Implications
-
-We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications.
-
-There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects.
-
-
-### BibTeX entry and citation info
-```bibtex
-@misc{radford2022whisper,
-  doi = {10.48550/ARXIV.2212.04356},
-  url = {https://arxiv.org/abs/2212.04356},
-  author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
-  title = {Robust Speech Recognition via Large-Scale Weak Supervision},
-  publisher = {arXiv},
-  year = {2022},
-  copyright = {arXiv.org perpetual, non-exclusive license}
-}
-```
-

pretrained_models/whisper-small/added_tokens.json

@@ -1,1609 +0,0 @@
-{
-  "<|0.00|>": 50364,
-  "<|0.02|>": 50365,
-  "<|0.04|>": 50366,
-  "<|0.06|>": 50367,
-  "<|0.08|>": 50368,
-  "<|0.10|>": 50369,
-  "<|0.12|>": 50370,
-  "<|0.14|>": 50371,
-  "<|0.16|>": 50372,
-  "<|0.18|>": 50373,
-  "<|0.20|>": 50374,
-  "<|0.22|>": 50375,
-  "<|0.24|>": 50376,
-  "<|0.26|>": 50377,
-  "<|0.28|>": 50378,
-  "<|0.30|>": 50379,
-  "<|0.32|>": 50380,
-  "<|0.34|>": 50381,
-  "<|0.36|>": 50382,
-  "<|0.38|>": 50383,
-  "<|0.40|>": 50384,
-  "<|0.42|>": 50385,
-  "<|0.44|>": 50386,
-  "<|0.46|>": 50387,
-  "<|0.48|>": 50388,
-  "<|0.50|>": 50389,
-  "<|0.52|>": 50390,
-  "<|0.54|>": 50391,
-  "<|0.56|>": 50392,
-  "<|0.58|>": 50393,
-  "<|0.60|>": 50394,
-  "<|0.62|>": 50395,
-  "<|0.64|>": 50396,
-  "<|0.66|>": 50397,
-  "<|0.68|>": 50398,
-  "<|0.70|>": 50399,
-  "<|0.72|>": 50400,
-  "<|0.74|>": 50401,
-  "<|0.76|>": 50402,
-  "<|0.78|>": 50403,
-  "<|0.80|>": 50404,
-  "<|0.82|>": 50405,
-  "<|0.84|>": 50406,
-  "<|0.86|>": 50407,
-  "<|0.88|>": 50408,
-  "<|0.90|>": 50409,
-  "<|0.92|>": 50410,
-  "<|0.94|>": 50411,
-  "<|0.96|>": 50412,
-  "<|0.98|>": 50413,
-  "<|1.00|>": 50414,
-  "<|1.02|>": 50415,
-  "<|1.04|>": 50416,
-  "<|1.06|>": 50417,
-  "<|1.08|>": 50418,
-  "<|1.10|>": 50419,
-  "<|1.12|>": 50420,
-  "<|1.14|>": 50421,
-  "<|1.16|>": 50422,
-  "<|1.18|>": 50423,
-  "<|1.20|>": 50424,
-  "<|1.22|>": 50425,
-  "<|1.24|>": 50426,
-  "<|1.26|>": 50427,
-  "<|1.28|>": 50428,
-  "<|1.30|>": 50429,
-  "<|1.32|>": 50430,
-  "<|1.34|>": 50431,
-  "<|1.36|>": 50432,
-  "<|1.38|>": 50433,
-  "<|1.40|>": 50434,
-  "<|1.42|>": 50435,
-  "<|1.44|>": 50436,
-  "<|1.46|>": 50437,
-  "<|1.48|>": 50438,
-  "<|1.50|>": 50439,
-  "<|1.52|>": 50440,
-  "<|1.54|>": 50441,
-  "<|1.56|>": 50442,
-  "<|1.58|>": 50443,
-  "<|1.60|>": 50444,
-  "<|1.62|>": 50445,
-  "<|1.64|>": 50446,
-  "<|1.66|>": 50447,
-  "<|1.68|>": 50448,
-  "<|1.70|>": 50449,
-  "<|1.72|>": 50450,
-  "<|1.74|>": 50451,
-  "<|1.76|>": 50452,
-  "<|1.78|>": 50453,
-  "<|1.80|>": 50454,
-  "<|1.82|>": 50455,
-  "<|1.84|>": 50456,
-  "<|1.86|>": 50457,
-  "<|1.88|>": 50458,
-  "<|1.90|>": 50459,
-  "<|1.92|>": 50460,
-  "<|1.94|>": 50461,
-  "<|1.96|>": 50462,
-  "<|1.98|>": 50463,
-  "<|10.00|>": 50864,
-  "<|10.02|>": 50865,
-  "<|10.04|>": 50866,
-  "<|10.06|>": 50867,
-  "<|10.08|>": 50868,
-  "<|10.10|>": 50869,
-  "<|10.12|>": 50870,
-  "<|10.14|>": 50871,
-  "<|10.16|>": 50872,
-  "<|10.18|>": 50873,
-  "<|10.20|>": 50874,
-  "<|10.22|>": 50875,
-  "<|10.24|>": 50876,
-  "<|10.26|>": 50877,
-  "<|10.28|>": 50878,
-  "<|10.30|>": 50879,
-  "<|10.32|>": 50880,
-  "<|10.34|>": 50881,
-  "<|10.36|>": 50882,
-  "<|10.38|>": 50883,
-  "<|10.40|>": 50884,
-  "<|10.42|>": 50885,
-  "<|10.44|>": 50886,
-  "<|10.46|>": 50887,
-  "<|10.48|>": 50888,
-  "<|10.50|>": 50889,
-  "<|10.52|>": 50890,
-  "<|10.54|>": 50891,
-  "<|10.56|>": 50892,
-  "<|10.58|>": 50893,
-  "<|10.60|>": 50894,
-  "<|10.62|>": 50895,
-  "<|10.64|>": 50896,
-  "<|10.66|>": 50897,
-  "<|10.68|>": 50898,
-  "<|10.70|>": 50899,
-  "<|10.72|>": 50900,
-  "<|10.74|>": 50901,
-  "<|10.76|>": 50902,
-  "<|10.78|>": 50903,
-  "<|10.80|>": 50904,
-  "<|10.82|>": 50905,
-  "<|10.84|>": 50906,
-  "<|10.86|>": 50907,
-  "<|10.88|>": 50908,
-  "<|10.90|>": 50909,
-  "<|10.92|>": 50910,
-  "<|10.94|>": 50911,
-  "<|10.96|>": 50912,
-  "<|10.98|>": 50913,
-  "<|11.00|>": 50914,
-  "<|11.02|>": 50915,
-  "<|11.04|>": 50916,
-  "<|11.06|>": 50917,
-  "<|11.08|>": 50918,
-  "<|11.10|>": 50919,
-  "<|11.12|>": 50920,
-  "<|11.14|>": 50921,
-  "<|11.16|>": 50922,
-  "<|11.18|>": 50923,
-  "<|11.20|>": 50924,
-  "<|11.22|>": 50925,
-  "<|11.24|>": 50926,
-  "<|11.26|>": 50927,
-  "<|11.28|>": 50928,
-  "<|11.30|>": 50929,
-  "<|11.32|>": 50930,
-  "<|11.34|>": 50931,
-  "<|11.36|>": 50932,
-  "<|11.38|>": 50933,
-  "<|11.40|>": 50934,
-  "<|11.42|>": 50935,
-  "<|11.44|>": 50936,
-  "<|11.46|>": 50937,
-  "<|11.48|>": 50938,
-  "<|11.50|>": 50939,
-  "<|11.52|>": 50940,
-  "<|11.54|>": 50941,
-  "<|11.56|>": 50942,
-  "<|11.58|>": 50943,
-  "<|11.60|>": 50944,
-  "<|11.62|>": 50945,
-  "<|11.64|>": 50946,
-  "<|11.66|>": 50947,
-  "<|11.68|>": 50948,
-  "<|11.70|>": 50949,
-  "<|11.72|>": 50950,
-  "<|11.74|>": 50951,
-  "<|11.76|>": 50952,
-  "<|11.78|>": 50953,
-  "<|11.80|>": 50954,
-  "<|11.82|>": 50955,
-  "<|11.84|>": 50956,
-  "<|11.86|>": 50957,
-  "<|11.88|>": 50958,
-  "<|11.90|>": 50959,
-  "<|11.92|>": 50960,
-  "<|11.94|>": 50961,
-  "<|11.96|>": 50962,
-  "<|11.98|>": 50963,
-  "<|12.00|>": 50964,
-  "<|12.02|>": 50965,
-  "<|12.04|>": 50966,
-  "<|12.06|>": 50967,
-  "<|12.08|>": 50968,
-  "<|12.10|>": 50969,
-  "<|12.12|>": 50970,
-  "<|12.14|>": 50971,
-  "<|12.16|>": 50972,
-  "<|12.18|>": 50973,
-  "<|12.20|>": 50974,
-  "<|12.22|>": 50975,
-  "<|12.24|>": 50976,
-  "<|12.26|>": 50977,
-  "<|12.28|>": 50978,
-  "<|12.30|>": 50979,
-  "<|12.32|>": 50980,
-  "<|12.34|>": 50981,
-  "<|12.36|>": 50982,
-  "<|12.38|>": 50983,
-  "<|12.40|>": 50984,
-  "<|12.42|>": 50985,
-  "<|12.44|>": 50986,
-  "<|12.46|>": 50987,
-  "<|12.48|>": 50988,
-  "<|12.50|>": 50989,
-  "<|12.52|>": 50990,
-  "<|12.54|>": 50991,
-  "<|12.56|>": 50992,
-  "<|12.58|>": 50993,
-  "<|12.60|>": 50994,
-  "<|12.62|>": 50995,
-  "<|12.64|>": 50996,
-  "<|12.66|>": 50997,
-  "<|12.68|>": 50998,
-  "<|12.70|>": 50999,
-  "<|12.72|>": 51000,
-  "<|12.74|>": 51001,
-  "<|12.76|>": 51002,
-  "<|12.78|>": 51003,
-  "<|12.80|>": 51004,
-  "<|12.82|>": 51005,
-  "<|12.84|>": 51006,
-  "<|12.86|>": 51007,
-  "<|12.88|>": 51008,
-  "<|12.90|>": 51009,
-  "<|12.92|>": 51010,
-  "<|12.94|>": 51011,
-  "<|12.96|>": 51012,
-  "<|12.98|>": 51013,
-  "<|13.00|>": 51014,
-  "<|13.02|>": 51015,
-  "<|13.04|>": 51016,
-  "<|13.06|>": 51017,
-  "<|13.08|>": 51018,
-  "<|13.10|>": 51019,
-  "<|13.12|>": 51020,
-  "<|13.14|>": 51021,
-  "<|13.16|>": 51022,
-  "<|13.18|>": 51023,
-  "<|13.20|>": 51024,
-  "<|13.22|>": 51025,
-  "<|13.24|>": 51026,
-  "<|13.26|>": 51027,
-  "<|13.28|>": 51028,
-  "<|13.30|>": 51029,
-  "<|13.32|>": 51030,
-  "<|13.34|>": 51031,
-  "<|13.36|>": 51032,
-  "<|13.38|>": 51033,
-  "<|13.40|>": 51034,
-  "<|13.42|>": 51035,
-  "<|13.44|>": 51036,
-  "<|13.46|>": 51037,
-  "<|13.48|>": 51038,
-  "<|13.50|>": 51039,
-  "<|13.52|>": 51040,
-  "<|13.54|>": 51041,
-  "<|13.56|>": 51042,
-  "<|13.58|>": 51043,
-  "<|13.60|>": 51044,
-  "<|13.62|>": 51045,
-  "<|13.64|>": 51046,
-  "<|13.66|>": 51047,
-  "<|13.68|>": 51048,
-  "<|13.70|>": 51049,
-  "<|13.72|>": 51050,
-  "<|13.74|>": 51051,
-  "<|13.76|>": 51052,
-  "<|13.78|>": 51053,
-  "<|13.80|>": 51054,
-  "<|13.82|>": 51055,
-  "<|13.84|>": 51056,
-  "<|13.86|>": 51057,
-  "<|13.88|>": 51058,
-  "<|13.90|>": 51059,
-  "<|13.92|>": 51060,
-  "<|13.94|>": 51061,
-  "<|13.96|>": 51062,
-  "<|13.98|>": 51063,
-  "<|14.00|>": 51064,
-  "<|14.02|>": 51065,
-  "<|14.04|>": 51066,
-  "<|14.06|>": 51067,
-  "<|14.08|>": 51068,
-  "<|14.10|>": 51069,
-  "<|14.12|>": 51070,
-  "<|14.14|>": 51071,
-  "<|14.16|>": 51072,
-  "<|14.18|>": 51073,
-  "<|14.20|>": 51074,
-  "<|14.22|>": 51075,
-  "<|14.24|>": 51076,
-  "<|14.26|>": 51077,
-  "<|14.28|>": 51078,
-  "<|14.30|>": 51079,
-  "<|14.32|>": 51080,
-  "<|14.34|>": 51081,
-  "<|14.36|>": 51082,
-  "<|14.38|>": 51083,
-  "<|14.40|>": 51084,
-  "<|14.42|>": 51085,
-  "<|14.44|>": 51086,
-  "<|14.46|>": 51087,
-  "<|14.48|>": 51088,
-  "<|14.50|>": 51089,
-  "<|14.52|>": 51090,
-  "<|14.54|>": 51091,
-  "<|14.56|>": 51092,
-  "<|14.58|>": 51093,
-  "<|14.60|>": 51094,
-  "<|14.62|>": 51095,
-  "<|14.64|>": 51096,
-  "<|14.66|>": 51097,
-  "<|14.68|>": 51098,
-  "<|14.70|>": 51099,
-  "<|14.72|>": 51100,
-  "<|14.74|>": 51101,
-  "<|14.76|>": 51102,
-  "<|14.78|>": 51103,
-  "<|14.80|>": 51104,
-  "<|14.82|>": 51105,
-  "<|14.84|>": 51106,
-  "<|14.86|>": 51107,
-  "<|14.88|>": 51108,
-  "<|14.90|>": 51109,
-  "<|14.92|>": 51110,
-  "<|14.94|>": 51111,
-  "<|14.96|>": 51112,
-  "<|14.98|>": 51113,
-  "<|15.00|>": 51114,
-  "<|15.02|>": 51115,
-  "<|15.04|>": 51116,
-  "<|15.06|>": 51117,
-  "<|15.08|>": 51118,
-  "<|15.10|>": 51119,
-  "<|15.12|>": 51120,
-  "<|15.14|>": 51121,
-  "<|15.16|>": 51122,
-  "<|15.18|>": 51123,
-  "<|15.20|>": 51124,
-  "<|15.22|>": 51125,
-  "<|15.24|>": 51126,
-  "<|15.26|>": 51127,
-  "<|15.28|>": 51128,
-  "<|15.30|>": 51129,
-  "<|15.32|>": 51130,
-  "<|15.34|>": 51131,
-  "<|15.36|>": 51132,
-  "<|15.38|>": 51133,
-  "<|15.40|>": 51134,
-  "<|15.42|>": 51135,
-  "<|15.44|>": 51136,
-  "<|15.46|>": 51137,
-  "<|15.48|>": 51138,
-  "<|15.50|>": 51139,
-  "<|15.52|>": 51140,
-  "<|15.54|>": 51141,
-  "<|15.56|>": 51142,
-  "<|15.58|>": 51143,
-  "<|15.60|>": 51144,
-  "<|15.62|>": 51145,
-  "<|15.64|>": 51146,
-  "<|15.66|>": 51147,
-  "<|15.68|>": 51148,
-  "<|15.70|>": 51149,
-  "<|15.72|>": 51150,
-  "<|15.74|>": 51151,
-  "<|15.76|>": 51152,
-  "<|15.78|>": 51153,
-  "<|15.80|>": 51154,
-  "<|15.82|>": 51155,
-  "<|15.84|>": 51156,
-  "<|15.86|>": 51157,
-  "<|15.88|>": 51158,
-  "<|15.90|>": 51159,
-  "<|15.92|>": 51160,
-  "<|15.94|>": 51161,
-  "<|15.96|>": 51162,
-  "<|15.98|>": 51163,
-  "<|16.00|>": 51164,
-  "<|16.02|>": 51165,
-  "<|16.04|>": 51166,
-  "<|16.06|>": 51167,
-  "<|16.08|>": 51168,
-  "<|16.10|>": 51169,
-  "<|16.12|>": 51170,
-  "<|16.14|>": 51171,
-  "<|16.16|>": 51172,
-  "<|16.18|>": 51173,
-  "<|16.20|>": 51174,
-  "<|16.22|>": 51175,
-  "<|16.24|>": 51176,
-  "<|16.26|>": 51177,
-  "<|16.28|>": 51178,
-  "<|16.30|>": 51179,
-  "<|16.32|>": 51180,
-  "<|16.34|>": 51181,
-  "<|16.36|>": 51182,
-  "<|16.38|>": 51183,
-  "<|16.40|>": 51184,
-  "<|16.42|>": 51185,
-  "<|16.44|>": 51186,
-  "<|16.46|>": 51187,
-  "<|16.48|>": 51188,
-  "<|16.50|>": 51189,
-  "<|16.52|>": 51190,
-  "<|16.54|>": 51191,
-  "<|16.56|>": 51192,
-  "<|16.58|>": 51193,
-  "<|16.60|>": 51194,
-  "<|16.62|>": 51195,
-  "<|16.64|>": 51196,
-  "<|16.66|>": 51197,
-  "<|16.68|>": 51198,
-  "<|16.70|>": 51199,
-  "<|16.72|>": 51200,
-  "<|16.74|>": 51201,
-  "<|16.76|>": 51202,
-  "<|16.78|>": 51203,
-  "<|16.80|>": 51204,
-  "<|16.82|>": 51205,
-  "<|16.84|>": 51206,
-  "<|16.86|>": 51207,
-  "<|16.88|>": 51208,
-  "<|16.90|>": 51209,
-  "<|16.92|>": 51210,
-  "<|16.94|>": 51211,
-  "<|16.96|>": 51212,
-  "<|16.98|>": 51213,
-  "<|17.00|>": 51214,
-  "<|17.02|>": 51215,
-  "<|17.04|>": 51216,
-  "<|17.06|>": 51217,
-  "<|17.08|>": 51218,
-  "<|17.10|>": 51219,
-  "<|17.12|>": 51220,
-  "<|17.14|>": 51221,
-  "<|17.16|>": 51222,
-  "<|17.18|>": 51223,
-  "<|17.20|>": 51224,
-  "<|17.22|>": 51225,
-  "<|17.24|>": 51226,
-  "<|17.26|>": 51227,
-  "<|17.28|>": 51228,
-  "<|17.30|>": 51229,
-  "<|17.32|>": 51230,
-  "<|17.34|>": 51231,
-  "<|17.36|>": 51232,
-  "<|17.38|>": 51233,
-  "<|17.40|>": 51234,
-  "<|17.42|>": 51235,
-  "<|17.44|>": 51236,
-  "<|17.46|>": 51237,
-  "<|17.48|>": 51238,
-  "<|17.50|>": 51239,
-  "<|17.52|>": 51240,
-  "<|17.54|>": 51241,
-  "<|17.56|>": 51242,
-  "<|17.58|>": 51243,
-  "<|17.60|>": 51244,
-  "<|17.62|>": 51245,
-  "<|17.64|>": 51246,
-  "<|17.66|>": 51247,
-  "<|17.68|>": 51248,
-  "<|17.70|>": 51249,
-  "<|17.72|>": 51250,
-  "<|17.74|>": 51251,
-  "<|17.76|>": 51252,
-  "<|17.78|>": 51253,
-  "<|17.80|>": 51254,
-  "<|17.82|>": 51255,
-  "<|17.84|>": 51256,
-  "<|17.86|>": 51257,
-  "<|17.88|>": 51258,
-  "<|17.90|>": 51259,
-  "<|17.92|>": 51260,
-  "<|17.94|>": 51261,
-  "<|17.96|>": 51262,
-  "<|17.98|>": 51263,
-  "<|18.00|>": 51264,
-  "<|18.02|>": 51265,
-  "<|18.04|>": 51266,
-  "<|18.06|>": 51267,
-  "<|18.08|>": 51268,
-  "<|18.10|>": 51269,
-  "<|18.12|>": 51270,
-  "<|18.14|>": 51271,
-  "<|18.16|>": 51272,
-  "<|18.18|>": 51273,
-  "<|18.20|>": 51274,
-  "<|18.22|>": 51275,
-  "<|18.24|>": 51276,
-  "<|18.26|>": 51277,
-  "<|18.28|>": 51278,
-  "<|18.30|>": 51279,
-  "<|18.32|>": 51280,
-  "<|18.34|>": 51281,
-  "<|18.36|>": 51282,
-  "<|18.38|>": 51283,
-  "<|18.40|>": 51284,
-  "<|18.42|>": 51285,
-  "<|18.44|>": 51286,
-  "<|18.46|>": 51287,
-  "<|18.48|>": 51288,
-  "<|18.50|>": 51289,
-  "<|18.52|>": 51290,
-  "<|18.54|>": 51291,
-  "<|18.56|>": 51292,
-  "<|18.58|>": 51293,
-  "<|18.60|>": 51294,
-  "<|18.62|>": 51295,
-  "<|18.64|>": 51296,
-  "<|18.66|>": 51297,
-  "<|18.68|>": 51298,
-  "<|18.70|>": 51299,
-  "<|18.72|>": 51300,
-  "<|18.74|>": 51301,
-  "<|18.76|>": 51302,
-  "<|18.78|>": 51303,
-  "<|18.80|>": 51304,
-  "<|18.82|>": 51305,
-  "<|18.84|>": 51306,
-  "<|18.86|>": 51307,
-  "<|18.88|>": 51308,
-  "<|18.90|>": 51309,
-  "<|18.92|>": 51310,
-  "<|18.94|>": 51311,
-  "<|18.96|>": 51312,
-  "<|18.98|>": 51313,
-  "<|19.00|>": 51314,
-  "<|19.02|>": 51315,
-  "<|19.04|>": 51316,
-  "<|19.06|>": 51317,
-  "<|19.08|>": 51318,
-  "<|19.10|>": 51319,
-  "<|19.12|>": 51320,
-  "<|19.14|>": 51321,
-  "<|19.16|>": 51322,
-  "<|19.18|>": 51323,
-  "<|19.20|>": 51324,
-  "<|19.22|>": 51325,
-  "<|19.24|>": 51326,
-  "<|19.26|>": 51327,
-  "<|19.28|>": 51328,
-  "<|19.30|>": 51329,
-  "<|19.32|>": 51330,
-  "<|19.34|>": 51331,
-  "<|19.36|>": 51332,
-  "<|19.38|>": 51333,
-  "<|19.40|>": 51334,
-  "<|19.42|>": 51335,
-  "<|19.44|>": 51336,
-  "<|19.46|>": 51337,
-  "<|19.48|>": 51338,
-  "<|19.50|>": 51339,
-  "<|19.52|>": 51340,
-  "<|19.54|>": 51341,
-  "<|19.56|>": 51342,
-  "<|19.58|>": 51343,
-  "<|19.60|>": 51344,
-  "<|19.62|>": 51345,
-  "<|19.64|>": 51346,
-  "<|19.66|>": 51347,
-  "<|19.68|>": 51348,
-  "<|19.70|>": 51349,
-  "<|19.72|>": 51350,
-  "<|19.74|>": 51351,
-  "<|19.76|>": 51352,
-  "<|19.78|>": 51353,
-  "<|19.80|>": 51354,
-  "<|19.82|>": 51355,
-  "<|19.84|>": 51356,
-  "<|19.86|>": 51357,
-  "<|19.88|>": 51358,
-  "<|19.90|>": 51359,
-  "<|19.92|>": 51360,
-  "<|19.94|>": 51361,
-  "<|19.96|>": 51362,
-  "<|19.98|>": 51363,
-  "<|2.00|>": 50464,
-  "<|2.02|>": 50465,
-  "<|2.04|>": 50466,
-  "<|2.06|>": 50467,
-  "<|2.08|>": 50468,
-  "<|2.10|>": 50469,
-  "<|2.12|>": 50470,
-  "<|2.14|>": 50471,
-  "<|2.16|>": 50472,
-  "<|2.18|>": 50473,
-  "<|2.20|>": 50474,
-  "<|2.22|>": 50475,
-  "<|2.24|>": 50476,
-  "<|2.26|>": 50477,
-  "<|2.28|>": 50478,
-  "<|2.30|>": 50479,
-  "<|2.32|>": 50480,
-  "<|2.34|>": 50481,
-  "<|2.36|>": 50482,
-  "<|2.38|>": 50483,
-  "<|2.40|>": 50484,
-  "<|2.42|>": 50485,
-  "<|2.44|>": 50486,
-  "<|2.46|>": 50487,
-  "<|2.48|>": 50488,
-  "<|2.50|>": 50489,
-  "<|2.52|>": 50490,
-  "<|2.54|>": 50491,
-  "<|2.56|>": 50492,
-  "<|2.58|>": 50493,
-  "<|2.60|>": 50494,
-  "<|2.62|>": 50495,
-  "<|2.64|>": 50496,
-  "<|2.66|>": 50497,
-  "<|2.68|>": 50498,
-  "<|2.70|>": 50499,
-  "<|2.72|>": 50500,
-  "<|2.74|>": 50501,
-  "<|2.76|>": 50502,
-  "<|2.78|>": 50503,
-  "<|2.80|>": 50504,
-  "<|2.82|>": 50505,
-  "<|2.84|>": 50506,
-  "<|2.86|>": 50507,
-  "<|2.88|>": 50508,
-  "<|2.90|>": 50509,
-  "<|2.92|>": 50510,
-  "<|2.94|>": 50511,
-  "<|2.96|>": 50512,
-  "<|2.98|>": 50513,
-  "<|20.00|>": 51364,
-  "<|20.02|>": 51365,
-  "<|20.04|>": 51366,
-  "<|20.06|>": 51367,
-  "<|20.08|>": 51368,
-  "<|20.10|>": 51369,
-  "<|20.12|>": 51370,
-  "<|20.14|>": 51371,
-  "<|20.16|>": 51372,
-  "<|20.18|>": 51373,
-  "<|20.20|>": 51374,
-  "<|20.22|>": 51375,
-  "<|20.24|>": 51376,
-  "<|20.26|>": 51377,
-  "<|20.28|>": 51378,
-  "<|20.30|>": 51379,
-  "<|20.32|>": 51380,
-  "<|20.34|>": 51381,
-  "<|20.36|>": 51382,
-  "<|20.38|>": 51383,
-  "<|20.40|>": 51384,
-  "<|20.42|>": 51385,
-  "<|20.44|>": 51386,
-  "<|20.46|>": 51387,
-  "<|20.48|>": 51388,
-  "<|20.50|>": 51389,
-  "<|20.52|>": 51390,
-  "<|20.54|>": 51391,
-  "<|20.56|>": 51392,
-  "<|20.58|>": 51393,
-  "<|20.60|>": 51394,
-  "<|20.62|>": 51395,
-  "<|20.64|>": 51396,
-  "<|20.66|>": 51397,
-  "<|20.68|>": 51398,
-  "<|20.70|>": 51399,
-  "<|20.72|>": 51400,
-  "<|20.74|>": 51401,
-  "<|20.76|>": 51402,
-  "<|20.78|>": 51403,
-  "<|20.80|>": 51404,
-  "<|20.82|>": 51405,
-  "<|20.84|>": 51406,
-  "<|20.86|>": 51407,
-  "<|20.88|>": 51408,
-  "<|20.90|>": 51409,
-  "<|20.92|>": 51410,
-  "<|20.94|>": 51411,
-  "<|20.96|>": 51412,
-  "<|20.98|>": 51413,
-  "<|21.00|>": 51414,
-  "<|21.02|>": 51415,
-  "<|21.04|>": 51416,
-  "<|21.06|>": 51417,
-  "<|21.08|>": 51418,
-  "<|21.10|>": 51419,
-  "<|21.12|>": 51420,
-  "<|21.14|>": 51421,
-  "<|21.16|>": 51422,
-  "<|21.18|>": 51423,
-  "<|21.20|>": 51424,
-  "<|21.22|>": 51425,
-  "<|21.24|>": 51426,
-  "<|21.26|>": 51427,
-  "<|21.28|>": 51428,
-  "<|21.30|>": 51429,
-  "<|21.32|>": 51430,
-  "<|21.34|>": 51431,
-  "<|21.36|>": 51432,
-  "<|21.38|>": 51433,
-  "<|21.40|>": 51434,
-  "<|21.42|>": 51435,
-  "<|21.44|>": 51436,
-  "<|21.46|>": 51437,
-  "<|21.48|>": 51438,
-  "<|21.50|>": 51439,
-  "<|21.52|>": 51440,
-  "<|21.54|>": 51441,
-  "<|21.56|>": 51442,
-  "<|21.58|>": 51443,
-  "<|21.60|>": 51444,
-  "<|21.62|>": 51445,
-  "<|21.64|>": 51446,
-  "<|21.66|>": 51447,
-  "<|21.68|>": 51448,
-  "<|21.70|>": 51449,
-  "<|21.72|>": 51450,
-  "<|21.74|>": 51451,
-  "<|21.76|>": 51452,
-  "<|21.78|>": 51453,
-  "<|21.80|>": 51454,
-  "<|21.82|>": 51455,
-  "<|21.84|>": 51456,
-  "<|21.86|>": 51457,
-  "<|21.88|>": 51458,
-  "<|21.90|>": 51459,
-  "<|21.92|>": 51460,
-  "<|21.94|>": 51461,
-  "<|21.96|>": 51462,
-  "<|21.98|>": 51463,
-  "<|22.00|>": 51464,
-  "<|22.02|>": 51465,
-  "<|22.04|>": 51466,
-  "<|22.06|>": 51467,
-  "<|22.08|>": 51468,
-  "<|22.10|>": 51469,
-  "<|22.12|>": 51470,
-  "<|22.14|>": 51471,
-  "<|22.16|>": 51472,
-  "<|22.18|>": 51473,
-  "<|22.20|>": 51474,
-  "<|22.22|>": 51475,
-  "<|22.24|>": 51476,
-  "<|22.26|>": 51477,
-  "<|22.28|>": 51478,
-  "<|22.30|>": 51479,
-  "<|22.32|>": 51480,
-  "<|22.34|>": 51481,
-  "<|22.36|>": 51482,
-  "<|22.38|>": 51483,
-  "<|22.40|>": 51484,
-  "<|22.42|>": 51485,
-  "<|22.44|>": 51486,
-  "<|22.46|>": 51487,
-  "<|22.48|>": 51488,
-  "<|22.50|>": 51489,
-  "<|22.52|>": 51490,
-  "<|22.54|>": 51491,
-  "<|22.56|>": 51492,
-  "<|22.58|>": 51493,
-  "<|22.60|>": 51494,
-  "<|22.62|>": 51495,
-  "<|22.64|>": 51496,
-  "<|22.66|>": 51497,
-  "<|22.68|>": 51498,
-  "<|22.70|>": 51499,
-  "<|22.72|>": 51500,
-  "<|22.74|>": 51501,
-  "<|22.76|>": 51502,
-  "<|22.78|>": 51503,
-  "<|22.80|>": 51504,
-  "<|22.82|>": 51505,
-  "<|22.84|>": 51506,
-  "<|22.86|>": 51507,
-  "<|22.88|>": 51508,
-  "<|22.90|>": 51509,
-  "<|22.92|>": 51510,
-  "<|22.94|>": 51511,
-  "<|22.96|>": 51512,
-  "<|22.98|>": 51513,
-  "<|23.00|>": 51514,
-  "<|23.02|>": 51515,
-  "<|23.04|>": 51516,
-  "<|23.06|>": 51517,
-  "<|23.08|>": 51518,
-  "<|23.10|>": 51519,
-  "<|23.12|>": 51520,
-  "<|23.14|>": 51521,
-  "<|23.16|>": 51522,
-  "<|23.18|>": 51523,
-  "<|23.20|>": 51524,
-  "<|23.22|>": 51525,
-  "<|23.24|>": 51526,
-  "<|23.26|>": 51527,
-  "<|23.28|>": 51528,
-  "<|23.30|>": 51529,
-  "<|23.32|>": 51530,
-  "<|23.34|>": 51531,
-  "<|23.36|>": 51532,
-  "<|23.38|>": 51533,
-  "<|23.40|>": 51534,
-  "<|23.42|>": 51535,
-  "<|23.44|>": 51536,
-  "<|23.46|>": 51537,
-  "<|23.48|>": 51538,
-  "<|23.50|>": 51539,
-  "<|23.52|>": 51540,
-  "<|23.54|>": 51541,
-  "<|23.56|>": 51542,
-  "<|23.58|>": 51543,
-  "<|23.60|>": 51544,
-  "<|23.62|>": 51545,
-  "<|23.64|>": 51546,
-  "<|23.66|>": 51547,
-  "<|23.68|>": 51548,
-  "<|23.70|>": 51549,
-  "<|23.72|>": 51550,
-  "<|23.74|>": 51551,
-  "<|23.76|>": 51552,
-  "<|23.78|>": 51553,
-  "<|23.80|>": 51554,
-  "<|23.82|>": 51555,
-  "<|23.84|>": 51556,
-  "<|23.86|>": 51557,
-  "<|23.88|>": 51558,
-  "<|23.90|>": 51559,
-  "<|23.92|>": 51560,
-  "<|23.94|>": 51561,
-  "<|23.96|>": 51562,
-  "<|23.98|>": 51563,
-  "<|24.00|>": 51564,
-  "<|24.02|>": 51565,
-  "<|24.04|>": 51566,
-  "<|24.06|>": 51567,
-  "<|24.08|>": 51568,
-  "<|24.10|>": 51569,
-  "<|24.12|>": 51570,
-  "<|24.14|>": 51571,
-  "<|24.16|>": 51572,
-  "<|24.18|>": 51573,
-  "<|24.20|>": 51574,
-  "<|24.22|>": 51575,
-  "<|24.24|>": 51576,
-  "<|24.26|>": 51577,
-  "<|24.28|>": 51578,
-  "<|24.30|>": 51579,
-  "<|24.32|>": 51580,
-  "<|24.34|>": 51581,
-  "<|24.36|>": 51582,
-  "<|24.38|>": 51583,
-  "<|24.40|>": 51584,
-  "<|24.42|>": 51585,
-  "<|24.44|>": 51586,
-  "<|24.46|>": 51587,
-  "<|24.48|>": 51588,
-  "<|24.50|>": 51589,
-  "<|24.52|>": 51590,
-  "<|24.54|>": 51591,
-  "<|24.56|>": 51592,
-  "<|24.58|>": 51593,
-  "<|24.60|>": 51594,
-  "<|24.62|>": 51595,
-  "<|24.64|>": 51596,
-  "<|24.66|>": 51597,
-  "<|24.68|>": 51598,
-  "<|24.70|>": 51599,
-  "<|24.72|>": 51600,
-  "<|24.74|>": 51601,
-  "<|24.76|>": 51602,
-  "<|24.78|>": 51603,
-  "<|24.80|>": 51604,
-  "<|24.82|>": 51605,
-  "<|24.84|>": 51606,
-  "<|24.86|>": 51607,
-  "<|24.88|>": 51608,
-  "<|24.90|>": 51609,
-  "<|24.92|>": 51610,
-  "<|24.94|>": 51611,
-  "<|24.96|>": 51612,
-  "<|24.98|>": 51613,
-  "<|25.00|>": 51614,
-  "<|25.02|>": 51615,
-  "<|25.04|>": 51616,
-  "<|25.06|>": 51617,
-  "<|25.08|>": 51618,
-  "<|25.10|>": 51619,
-  "<|25.12|>": 51620,
-  "<|25.14|>": 51621,
-  "<|25.16|>": 51622,
-  "<|25.18|>": 51623,
-  "<|25.20|>": 51624,
-  "<|25.22|>": 51625,
-  "<|25.24|>": 51626,
-  "<|25.26|>": 51627,
-  "<|25.28|>": 51628,
-  "<|25.30|>": 51629,
-  "<|25.32|>": 51630,
-  "<|25.34|>": 51631,
-  "<|25.36|>": 51632,
-  "<|25.38|>": 51633,
-  "<|25.40|>": 51634,
-  "<|25.42|>": 51635,
-  "<|25.44|>": 51636,
-  "<|25.46|>": 51637,
-  "<|25.48|>": 51638,
-  "<|25.50|>": 51639,
-  "<|25.52|>": 51640,
-  "<|25.54|>": 51641,
-  "<|25.56|>": 51642,
-  "<|25.58|>": 51643,
-  "<|25.60|>": 51644,
-  "<|25.62|>": 51645,
-  "<|25.64|>": 51646,
-  "<|25.66|>": 51647,
-  "<|25.68|>": 51648,
-  "<|25.70|>": 51649,
-  "<|25.72|>": 51650,
-  "<|25.74|>": 51651,
-  "<|25.76|>": 51652,
-  "<|25.78|>": 51653,
-  "<|25.80|>": 51654,
-  "<|25.82|>": 51655,
-  "<|25.84|>": 51656,
-  "<|25.86|>": 51657,
-  "<|25.88|>": 51658,
-  "<|25.90|>": 51659,
-  "<|25.92|>": 51660,
-  "<|25.94|>": 51661,
-  "<|25.96|>": 51662,
-  "<|25.98|>": 51663,
-  "<|26.00|>": 51664,
-  "<|26.02|>": 51665,
-  "<|26.04|>": 51666,
-  "<|26.06|>": 51667,
-  "<|26.08|>": 51668,
-  "<|26.10|>": 51669,
-  "<|26.12|>": 51670,
-  "<|26.14|>": 51671,
-  "<|26.16|>": 51672,
-  "<|26.18|>": 51673,
-  "<|26.20|>": 51674,
-  "<|26.22|>": 51675,
-  "<|26.24|>": 51676,
-  "<|26.26|>": 51677,
-  "<|26.28|>": 51678,
-  "<|26.30|>": 51679,
-  "<|26.32|>": 51680,
-  "<|26.34|>": 51681,
-  "<|26.36|>": 51682,
-  "<|26.38|>": 51683,
-  "<|26.40|>": 51684,
-  "<|26.42|>": 51685,
-  "<|26.44|>": 51686,
-  "<|26.46|>": 51687,
-  "<|26.48|>": 51688,
-  "<|26.50|>": 51689,
-  "<|26.52|>": 51690,
-  "<|26.54|>": 51691,
-  "<|26.56|>": 51692,
-  "<|26.58|>": 51693,
-  "<|26.60|>": 51694,
-  "<|26.62|>": 51695,
-  "<|26.64|>": 51696,
-  "<|26.66|>": 51697,
-  "<|26.68|>": 51698,
-  "<|26.70|>": 51699,
-  "<|26.72|>": 51700,
-  "<|26.74|>": 51701,
-  "<|26.76|>": 51702,
-  "<|26.78|>": 51703,
-  "<|26.80|>": 51704,
-  "<|26.82|>": 51705,
-  "<|26.84|>": 51706,
-  "<|26.86|>": 51707,
-  "<|26.88|>": 51708,
-  "<|26.90|>": 51709,
-  "<|26.92|>": 51710,
-  "<|26.94|>": 51711,
-  "<|26.96|>": 51712,
-  "<|26.98|>": 51713,
-  "<|27.00|>": 51714,
-  "<|27.02|>": 51715,
-  "<|27.04|>": 51716,
-  "<|27.06|>": 51717,
-  "<|27.08|>": 51718,
-  "<|27.10|>": 51719,
-  "<|27.12|>": 51720,
-  "<|27.14|>": 51721,
-  "<|27.16|>": 51722,
-  "<|27.18|>": 51723,
-  "<|27.20|>": 51724,
-  "<|27.22|>": 51725,
-  "<|27.24|>": 51726,
-  "<|27.26|>": 51727,
-  "<|27.28|>": 51728,
-  "<|27.30|>": 51729,
-  "<|27.32|>": 51730,
-  "<|27.34|>": 51731,
-  "<|27.36|>": 51732,
-  "<|27.38|>": 51733,
-  "<|27.40|>": 51734,
-  "<|27.42|>": 51735,
-  "<|27.44|>": 51736,
-  "<|27.46|>": 51737,
-  "<|27.48|>": 51738,
-  "<|27.50|>": 51739,
-  "<|27.52|>": 51740,
-  "<|27.54|>": 51741,
-  "<|27.56|>": 51742,
-  "<|27.58|>": 51743,
-  "<|27.60|>": 51744,
-  "<|27.62|>": 51745,
-  "<|27.64|>": 51746,
-  "<|27.66|>": 51747,
-  "<|27.68|>": 51748,
-  "<|27.70|>": 51749,
-  "<|27.72|>": 51750,
-  "<|27.74|>": 51751,
-  "<|27.76|>": 51752,
-  "<|27.78|>": 51753,
-  "<|27.80|>": 51754,
-  "<|27.82|>": 51755,
-  "<|27.84|>": 51756,
-  "<|27.86|>": 51757,
-  "<|27.88|>": 51758,
-  "<|27.90|>": 51759,
-  "<|27.92|>": 51760,
-  "<|27.94|>": 51761,
-  "<|27.96|>": 51762,
-  "<|27.98|>": 51763,
-  "<|28.00|>": 51764,
-  "<|28.02|>": 51765,
-  "<|28.04|>": 51766,
-  "<|28.06|>": 51767,
-  "<|28.08|>": 51768,
-  "<|28.10|>": 51769,
-  "<|28.12|>": 51770,
-  "<|28.14|>": 51771,
-  "<|28.16|>": 51772,
-  "<|28.18|>": 51773,
-  "<|28.20|>": 51774,
-  "<|28.22|>": 51775,
-  "<|28.24|>": 51776,
-  "<|28.26|>": 51777,
-  "<|28.28|>": 51778,
-  "<|28.30|>": 51779,
-  "<|28.32|>": 51780,
-  "<|28.34|>": 51781,
-  "<|28.36|>": 51782,
-  "<|28.38|>": 51783,
-  "<|28.40|>": 51784,
-  "<|28.42|>": 51785,
-  "<|28.44|>": 51786,
-  "<|28.46|>": 51787,
-  "<|28.48|>": 51788,
-  "<|28.50|>": 51789,
-  "<|28.52|>": 51790,
-  "<|28.54|>": 51791,
-  "<|28.56|>": 51792,
-  "<|28.58|>": 51793,
-  "<|28.60|>": 51794,
-  "<|28.62|>": 51795,
-  "<|28.64|>": 51796,
-  "<|28.66|>": 51797,
-  "<|28.68|>": 51798,
-  "<|28.70|>": 51799,
-  "<|28.72|>": 51800,
-  "<|28.74|>": 51801,
-  "<|28.76|>": 51802,
-  "<|28.78|>": 51803,
-  "<|28.80|>": 51804,
-  "<|28.82|>": 51805,
-  "<|28.84|>": 51806,
-  "<|28.86|>": 51807,
-  "<|28.88|>": 51808,
-  "<|28.90|>": 51809,
-  "<|28.92|>": 51810,
-  "<|28.94|>": 51811,
-  "<|28.96|>": 51812,
-  "<|28.98|>": 51813,
-  "<|29.00|>": 51814,
-  "<|29.02|>": 51815,
-  "<|29.04|>": 51816,
-  "<|29.06|>": 51817,
-  "<|29.08|>": 51818,
-  "<|29.10|>": 51819,
-  "<|29.12|>": 51820,
-  "<|29.14|>": 51821,
-  "<|29.16|>": 51822,
-  "<|29.18|>": 51823,
-  "<|29.20|>": 51824,
-  "<|29.22|>": 51825,
-  "<|29.24|>": 51826,
-  "<|29.26|>": 51827,
-  "<|29.28|>": 51828,
-  "<|29.30|>": 51829,
-  "<|29.32|>": 51830,
-  "<|29.34|>": 51831,
-  "<|29.36|>": 51832,
-  "<|29.38|>": 51833,
-  "<|29.40|>": 51834,
-  "<|29.42|>": 51835,
-  "<|29.44|>": 51836,
-  "<|29.46|>": 51837,
-  "<|29.48|>": 51838,
-  "<|29.50|>": 51839,
-  "<|29.52|>": 51840,
-  "<|29.54|>": 51841,
-  "<|29.56|>": 51842,
-  "<|29.58|>": 51843,
-  "<|29.60|>": 51844,
-  "<|29.62|>": 51845,
-  "<|29.64|>": 51846,
-  "<|29.66|>": 51847,
-  "<|29.68|>": 51848,
-  "<|29.70|>": 51849,
-  "<|29.72|>": 51850,
-  "<|29.74|>": 51851,
-  "<|29.76|>": 51852,
-  "<|29.78|>": 51853,
-  "<|29.80|>": 51854,
-  "<|29.82|>": 51855,
-  "<|29.84|>": 51856,
-  "<|29.86|>": 51857,
-  "<|29.88|>": 51858,
-  "<|29.90|>": 51859,
-  "<|29.92|>": 51860,
-  "<|29.94|>": 51861,
-  "<|29.96|>": 51862,
-  "<|29.98|>": 51863,
-  "<|3.00|>": 50514,
-  "<|3.02|>": 50515,
-  "<|3.04|>": 50516,
-  "<|3.06|>": 50517,
-  "<|3.08|>": 50518,
-  "<|3.10|>": 50519,
-  "<|3.12|>": 50520,
-  "<|3.14|>": 50521,
-  "<|3.16|>": 50522,
-  "<|3.18|>": 50523,
-  "<|3.20|>": 50524,
-  "<|3.22|>": 50525,
-  "<|3.24|>": 50526,
-  "<|3.26|>": 50527,
-  "<|3.28|>": 50528,
-  "<|3.30|>": 50529,
-  "<|3.32|>": 50530,
-  "<|3.34|>": 50531,
-  "<|3.36|>": 50532,
-  "<|3.38|>": 50533,
-  "<|3.40|>": 50534,
-  "<|3.42|>": 50535,
-  "<|3.44|>": 50536,
-  "<|3.46|>": 50537,
-  "<|3.48|>": 50538,
-  "<|3.50|>": 50539,
-  "<|3.52|>": 50540,
-  "<|3.54|>": 50541,
-  "<|3.56|>": 50542,
-  "<|3.58|>": 50543,
-  "<|3.60|>": 50544,
-  "<|3.62|>": 50545,
-  "<|3.64|>": 50546,
-  "<|3.66|>": 50547,
-  "<|3.68|>": 50548,
-  "<|3.70|>": 50549,
-  "<|3.72|>": 50550,
-  "<|3.74|>": 50551,
-  "<|3.76|>": 50552,
-  "<|3.78|>": 50553,
-  "<|3.80|>": 50554,
-  "<|3.82|>": 50555,
-  "<|3.84|>": 50556,
-  "<|3.86|>": 50557,
-  "<|3.88|>": 50558,
-  "<|3.90|>": 50559,
-  "<|3.92|>": 50560,
-  "<|3.94|>": 50561,
-  "<|3.96|>": 50562,
-  "<|3.98|>": 50563,
-  "<|30.00|>": 51864,
-  "<|4.00|>": 50564,
-  "<|4.02|>": 50565,
-  "<|4.04|>": 50566,
-  "<|4.06|>": 50567,
-  "<|4.08|>": 50568,
-  "<|4.10|>": 50569,
-  "<|4.12|>": 50570,
-  "<|4.14|>": 50571,
-  "<|4.16|>": 50572,
-  "<|4.18|>": 50573,
-  "<|4.20|>": 50574,
-  "<|4.22|>": 50575,
-  "<|4.24|>": 50576,
-  "<|4.26|>": 50577,
-  "<|4.28|>": 50578,
-  "<|4.30|>": 50579,
-  "<|4.32|>": 50580,
-  "<|4.34|>": 50581,
-  "<|4.36|>": 50582,
-  "<|4.38|>": 50583,
-  "<|4.40|>": 50584,
-  "<|4.42|>": 50585,
-  "<|4.44|>": 50586,
-  "<|4.46|>": 50587,
-  "<|4.48|>": 50588,
-  "<|4.50|>": 50589,
-  "<|4.52|>": 50590,
-  "<|4.54|>": 50591,
-  "<|4.56|>": 50592,
-  "<|4.58|>": 50593,
-  "<|4.60|>": 50594,
-  "<|4.62|>": 50595,
-  "<|4.64|>": 50596,
-  "<|4.66|>": 50597,
-  "<|4.68|>": 50598,
-  "<|4.70|>": 50599,
-  "<|4.72|>": 50600,
-  "<|4.74|>": 50601,
-  "<|4.76|>": 50602,
-  "<|4.78|>": 50603,
-  "<|4.80|>": 50604,
-  "<|4.82|>": 50605,
-  "<|4.84|>": 50606,
-  "<|4.86|>": 50607,
-  "<|4.88|>": 50608,
-  "<|4.90|>": 50609,
-  "<|4.92|>": 50610,
-  "<|4.94|>": 50611,
-  "<|4.96|>": 50612,
-  "<|4.98|>": 50613,
-  "<|5.00|>": 50614,
-  "<|5.02|>": 50615,
-  "<|5.04|>": 50616,
-  "<|5.06|>": 50617,
-  "<|5.08|>": 50618,
-  "<|5.10|>": 50619,
-  "<|5.12|>": 50620,
-  "<|5.14|>": 50621,
-  "<|5.16|>": 50622,
-  "<|5.18|>": 50623,
-  "<|5.20|>": 50624,
-  "<|5.22|>": 50625,
-  "<|5.24|>": 50626,
-  "<|5.26|>": 50627,
-  "<|5.28|>": 50628,
-  "<|5.30|>": 50629,
-  "<|5.32|>": 50630,
-  "<|5.34|>": 50631,
-  "<|5.36|>": 50632,
-  "<|5.38|>": 50633,
-  "<|5.40|>": 50634,
-  "<|5.42|>": 50635,
-  "<|5.44|>": 50636,
-  "<|5.46|>": 50637,
-  "<|5.48|>": 50638,
-  "<|5.50|>": 50639,
-  "<|5.52|>": 50640,
-  "<|5.54|>": 50641,
-  "<|5.56|>": 50642,
-  "<|5.58|>": 50643,
-  "<|5.60|>": 50644,
-  "<|5.62|>": 50645,
-  "<|5.64|>": 50646,
-  "<|5.66|>": 50647,
-  "<|5.68|>": 50648,
-  "<|5.70|>": 50649,
-  "<|5.72|>": 50650,
-  "<|5.74|>": 50651,
-  "<|5.76|>": 50652,
-  "<|5.78|>": 50653,
-  "<|5.80|>": 50654,
-  "<|5.82|>": 50655,
-  "<|5.84|>": 50656,
-  "<|5.86|>": 50657,
-  "<|5.88|>": 50658,
-  "<|5.90|>": 50659,
-  "<|5.92|>": 50660,
-  "<|5.94|>": 50661,
-  "<|5.96|>": 50662,
-  "<|5.98|>": 50663,
-  "<|6.00|>": 50664,
-  "<|6.02|>": 50665,
-  "<|6.04|>": 50666,
-  "<|6.06|>": 50667,
-  "<|6.08|>": 50668,
-  "<|6.10|>": 50669,
-  "<|6.12|>": 50670,
-  "<|6.14|>": 50671,
-  "<|6.16|>": 50672,
-  "<|6.18|>": 50673,
-  "<|6.20|>": 50674,
-  "<|6.22|>": 50675,
-  "<|6.24|>": 50676,
-  "<|6.26|>": 50677,
-  "<|6.28|>": 50678,
-  "<|6.30|>": 50679,
-  "<|6.32|>": 50680,
-  "<|6.34|>": 50681,
-  "<|6.36|>": 50682,
-  "<|6.38|>": 50683,
-  "<|6.40|>": 50684,
-  "<|6.42|>": 50685,
-  "<|6.44|>": 50686,
-  "<|6.46|>": 50687,
-  "<|6.48|>": 50688,
-  "<|6.50|>": 50689,
-  "<|6.52|>": 50690,
-  "<|6.54|>": 50691,
-  "<|6.56|>": 50692,
-  "<|6.58|>": 50693,
-  "<|6.60|>": 50694,
-  "<|6.62|>": 50695,
-  "<|6.64|>": 50696,
-  "<|6.66|>": 50697,
-  "<|6.68|>": 50698,
-  "<|6.70|>": 50699,
-  "<|6.72|>": 50700,
-  "<|6.74|>": 50701,
-  "<|6.76|>": 50702,
-  "<|6.78|>": 50703,
-  "<|6.80|>": 50704,
-  "<|6.82|>": 50705,
-  "<|6.84|>": 50706,
-  "<|6.86|>": 50707,
-  "<|6.88|>": 50708,
-  "<|6.90|>": 50709,
-  "<|6.92|>": 50710,
-  "<|6.94|>": 50711,
-  "<|6.96|>": 50712,
-  "<|6.98|>": 50713,
-  "<|7.00|>": 50714,
-  "<|7.02|>": 50715,
-  "<|7.04|>": 50716,
-  "<|7.06|>": 50717,
-  "<|7.08|>": 50718,
-  "<|7.10|>": 50719,
-  "<|7.12|>": 50720,
-  "<|7.14|>": 50721,
-  "<|7.16|>": 50722,
-  "<|7.18|>": 50723,
-  "<|7.20|>": 50724,
-  "<|7.22|>": 50725,
-  "<|7.24|>": 50726,
-  "<|7.26|>": 50727,
-  "<|7.28|>": 50728,
-  "<|7.30|>": 50729,
-  "<|7.32|>": 50730,
-  "<|7.34|>": 50731,
-  "<|7.36|>": 50732,
-  "<|7.38|>": 50733,
-  "<|7.40|>": 50734,
-  "<|7.42|>": 50735,
-  "<|7.44|>": 50736,
-  "<|7.46|>": 50737,
-  "<|7.48|>": 50738,
-  "<|7.50|>": 50739,
-  "<|7.52|>": 50740,
-  "<|7.54|>": 50741,
-  "<|7.56|>": 50742,
-  "<|7.58|>": 50743,
-  "<|7.60|>": 50744,
-  "<|7.62|>": 50745,
-  "<|7.64|>": 50746,
-  "<|7.66|>": 50747,
-  "<|7.68|>": 50748,
-  "<|7.70|>": 50749,
-  "<|7.72|>": 50750,
-  "<|7.74|>": 50751,
-  "<|7.76|>": 50752,
-  "<|7.78|>": 50753,
-  "<|7.80|>": 50754,
-  "<|7.82|>": 50755,
-  "<|7.84|>": 50756,
-  "<|7.86|>": 50757,
-  "<|7.88|>": 50758,
-  "<|7.90|>": 50759,
-  "<|7.92|>": 50760,
-  "<|7.94|>": 50761,
-  "<|7.96|>": 50762,
-  "<|7.98|>": 50763,
-  "<|8.00|>": 50764,
-  "<|8.02|>": 50765,
-  "<|8.04|>": 50766,
-  "<|8.06|>": 50767,
-  "<|8.08|>": 50768,
-  "<|8.10|>": 50769,
-  "<|8.12|>": 50770,
-  "<|8.14|>": 50771,
-  "<|8.16|>": 50772,
-  "<|8.18|>": 50773,
-  "<|8.20|>": 50774,
-  "<|8.22|>": 50775,
-  "<|8.24|>": 50776,
-  "<|8.26|>": 50777,
-  "<|8.28|>": 50778,
-  "<|8.30|>": 50779,
-  "<|8.32|>": 50780,
-  "<|8.34|>": 50781,
-  "<|8.36|>": 50782,
-  "<|8.38|>": 50783,
-  "<|8.40|>": 50784,
-  "<|8.42|>": 50785,
-  "<|8.44|>": 50786,
-  "<|8.46|>": 50787,
-  "<|8.48|>": 50788,
-  "<|8.50|>": 50789,
-  "<|8.52|>": 50790,
-  "<|8.54|>": 50791,
-  "<|8.56|>": 50792,
-  "<|8.58|>": 50793,
-  "<|8.60|>": 50794,
-  "<|8.62|>": 50795,
-  "<|8.64|>": 50796,
-  "<|8.66|>": 50797,
-  "<|8.68|>": 50798,
-  "<|8.70|>": 50799,
-  "<|8.72|>": 50800,
-  "<|8.74|>": 50801,
-  "<|8.76|>": 50802,
-  "<|8.78|>": 50803,
-  "<|8.80|>": 50804,
-  "<|8.82|>": 50805,
-  "<|8.84|>": 50806,
-  "<|8.86|>": 50807,
-  "<|8.88|>": 50808,
-  "<|8.90|>": 50809,
-  "<|8.92|>": 50810,
-  "<|8.94|>": 50811,
-  "<|8.96|>": 50812,
-  "<|8.98|>": 50813,
-  "<|9.00|>": 50814,
-  "<|9.02|>": 50815,
-  "<|9.04|>": 50816,
-  "<|9.06|>": 50817,
-  "<|9.08|>": 50818,
-  "<|9.10|>": 50819,
-  "<|9.12|>": 50820,
-  "<|9.14|>": 50821,
-  "<|9.16|>": 50822,
-  "<|9.18|>": 50823,
-  "<|9.20|>": 50824,
-  "<|9.22|>": 50825,
-  "<|9.24|>": 50826,
-  "<|9.26|>": 50827,
-  "<|9.28|>": 50828,
-  "<|9.30|>": 50829,
-  "<|9.32|>": 50830,
-  "<|9.34|>": 50831,
-  "<|9.36|>": 50832,
-  "<|9.38|>": 50833,
-  "<|9.40|>": 50834,
-  "<|9.42|>": 50835,
-  "<|9.44|>": 50836,
-  "<|9.46|>": 50837,
-  "<|9.48|>": 50838,
-  "<|9.50|>": 50839,
-  "<|9.52|>": 50840,
-  "<|9.54|>": 50841,
-  "<|9.56|>": 50842,
-  "<|9.58|>": 50843,
-  "<|9.60|>": 50844,
-  "<|9.62|>": 50845,
-  "<|9.64|>": 50846,
-  "<|9.66|>": 50847,
-  "<|9.68|>": 50848,
-  "<|9.70|>": 50849,
-  "<|9.72|>": 50850,
-  "<|9.74|>": 50851,
-  "<|9.76|>": 50852,
-  "<|9.78|>": 50853,
-  "<|9.80|>": 50854,
-  "<|9.82|>": 50855,
-  "<|9.84|>": 50856,
-  "<|9.86|>": 50857,
-  "<|9.88|>": 50858,
-  "<|9.90|>": 50859,
-  "<|9.92|>": 50860,
-  "<|9.94|>": 50861,
-  "<|9.96|>": 50862,
-  "<|9.98|>": 50863,
-  "<|af|>": 50327,
-  "<|am|>": 50334,
-  "<|ar|>": 50272,
-  "<|as|>": 50350,
-  "<|az|>": 50304,
-  "<|ba|>": 50355,
-  "<|be|>": 50330,
-  "<|bg|>": 50292,
-  "<|bn|>": 50302,
-  "<|bo|>": 50347,
-  "<|br|>": 50309,
-  "<|bs|>": 50315,
-  "<|ca|>": 50270,
-  "<|cs|>": 50283,
-  "<|cy|>": 50297,
-  "<|da|>": 50285,
-  "<|de|>": 50261,
-  "<|el|>": 50281,
-  "<|en|>": 50259,
-  "<|es|>": 50262,
-  "<|et|>": 50307,
-  "<|eu|>": 50310,
-  "<|fa|>": 50300,
-  "<|fi|>": 50277,
-  "<|fo|>": 50338,
-  "<|fr|>": 50265,
-  "<|gl|>": 50319,
-  "<|gu|>": 50333,
-  "<|haw|>": 50352,
-  "<|ha|>": 50354,
-  "<|he|>": 50279,
-  "<|hi|>": 50276,
-  "<|hr|>": 50291,
-  "<|ht|>": 50339,
-  "<|hu|>": 50286,
-  "<|hy|>": 50312,
-  "<|id|>": 50275,
-  "<|is|>": 50311,
-  "<|it|>": 50274,
-  "<|ja|>": 50266,
-  "<|jw|>": 50356,
-  "<|ka|>": 50329,
-  "<|kk|>": 50316,
-  "<|km|>": 50323,
-  "<|kn|>": 50306,
-  "<|ko|>": 50264,
-  "<|la|>": 50294,
-  "<|lb|>": 50345,
-  "<|ln|>": 50353,
-  "<|lo|>": 50336,
-  "<|lt|>": 50293,
-  "<|lv|>": 50301,
-  "<|mg|>": 50349,
-  "<|mi|>": 50295,
-  "<|mk|>": 50308,
-  "<|ml|>": 50296,
-  "<|mn|>": 50314,
-  "<|mr|>": 50320,
-  "<|ms|>": 50282,
-  "<|mt|>": 50343,
-  "<|my|>": 50346,
-  "<|ne|>": 50313,
-  "<|nl|>": 50271,
-  "<|nn|>": 50342,
-  "<|nocaptions|>": 50362,
-  "<|notimestamps|>": 50363,
-  "<|no|>": 50288,
-  "<|oc|>": 50328,
-  "<|pa|>": 50321,
-  "<|pl|>": 50269,
-  "<|ps|>": 50340,
-  "<|pt|>": 50267,
-  "<|ro|>": 50284,
-  "<|ru|>": 50263,
-  "<|sa|>": 50344,
-  "<|sd|>": 50332,
-  "<|si|>": 50322,
-  "<|sk|>": 50298,
-  "<|sl|>": 50305,
-  "<|sn|>": 50324,
-  "<|so|>": 50326,
-  "<|sq|>": 50317,
-  "<|sr|>": 50303,
-  "<|startoflm|>": 50360,
-  "<|startofprev|>": 50361,
-  "<|startoftranscript|>": 50258,
-  "<|su|>": 50357,
-  "<|sv|>": 50273,
-  "<|sw|>": 50318,
-  "<|ta|>": 50287,
-  "<|te|>": 50299,
-  "<|tg|>": 50331,
-  "<|th|>": 50289,
-  "<|tk|>": 50341,
-  "<|tl|>": 50348,
-  "<|transcribe|>": 50359,
-  "<|translate|>": 50358,
-  "<|tr|>": 50268,
-  "<|tt|>": 50351,
-  "<|uk|>": 50280,
-  "<|ur|>": 50290,
-  "<|uz|>": 50337,
-  "<|vi|>": 50278,
-  "<|yi|>": 50335,
-  "<|yo|>": 50325,
-  "<|zh|>": 50260
-}

pretrained_models/whisper-small/config.json

@@ -1,142 +0,0 @@
-{
-  "_name_or_path": "openai/whisper-small",
-  "activation_dropout": 0.0,
-  "activation_function": "gelu",
-  "architectures": [
-    "WhisperForConditionalGeneration"
-  ],
-  "attention_dropout": 0.0,
-  "begin_suppress_tokens": [
-    220,
-    50257
-  ],
-  "bos_token_id": 50257,
-  "d_model": 768,
-  "decoder_attention_heads": 12,
-  "decoder_ffn_dim": 3072,
-  "decoder_layerdrop": 0.0,
-  "decoder_layers": 12,
-  "decoder_start_token_id": 50258,
-  "dropout": 0.0,
-  "encoder_attention_heads": 12,
-  "encoder_ffn_dim": 3072,
-  "encoder_layerdrop": 0.0,
-  "encoder_layers": 12,
-  "eos_token_id": 50257,
-  "forced_decoder_ids": [
-    [
-      1,
-      50259
-    ],
-    [
-      2,
-      50359
-    ],
-    [
-      3,
-      50363
-    ]
-  ],
-  "init_std": 0.02,
-  "is_encoder_decoder": true,
-  "max_length": 448,
-  "max_source_positions": 1500,
-  "max_target_positions": 448,
-  "model_type": "whisper",
-  "num_hidden_layers": 12,
-  "num_mel_bins": 80,
-  "pad_token_id": 50257,
-  "scale_embedding": false,
-  "suppress_tokens": [
-    1,
-    2,
-    7,
-    8,
-    9,
-    10,
-    14,
-    25,
-    26,
-    27,
-    28,
-    29,
-    31,
-    58,
-    59,
-    60,
-    61,
-    62,
-    63,
-    90,
-    91,
-    92,
-    93,
-    359,
-    503,
-    522,
-    542,
-    873,
-    893,
-    902,
-    918,
-    922,
-    931,
-    1350,
-    1853,
-    1982,
-    2460,
-    2627,
-    3246,
-    3253,
-    3268,
-    3536,
-    3846,
-    3961,
-    4183,
-    4667,
-    6585,
-    6647,
-    7273,
-    9061,
-    9383,
-    10428,
-    10929,
-    11938,
-    12033,
-    12331,
-    12562,
-    13793,
-    14157,
-    14635,
-    15265,
-    15618,
-    16553,
-    16604,
-    18362,
-    18956,
-    20075,
-    21675,
-    22520,
-    26130,
-    26161,
-    26435,
-    28279,
-    29464,
-    31650,
-    32302,
-    32470,
-    36865,
-    42863,
-    47425,
-    49870,
-    50254,
-    50258,
-    50360,
-    50361,
-    50362
-  ],
-  "torch_dtype": "float32",
-  "transformers_version": "4.27.0.dev0",
-  "use_cache": true,
-  "vocab_size": 51865
-}

pretrained_models/whisper-small/generation_config.json

@@ -1,264 +0,0 @@
-{
-  "alignment_heads": [
-    [
-      5,
-      3
-    ],
-    [
-      5,
-      9
-    ],
-    [
-      8,
-      0
-    ],
-    [
-      8,
-      4
-    ],
-    [
-      8,
-      7
-    ],
-    [
-      8,
-      8
-    ],
-    [
-      9,
-      0
-    ],
-    [
-      9,
-      7
-    ],
-    [
-      9,
-      9
-    ],
-    [
-      10,
-      5
-    ]
-  ],
-  "begin_suppress_tokens": [
-    220,
-    50257
-  ],
-  "bos_token_id": 50257,
-  "decoder_start_token_id": 50258,
-  "eos_token_id": 50257,
-  "forced_decoder_ids": [
-    [
-      1,
-      null
-    ],
-    [
-      2,
-      50359
-    ]
-  ],
-  "is_multilingual": true,
-  "lang_to_id": {
-    "<|af|>": 50327,
-    "<|am|>": 50334,
-    "<|ar|>": 50272,
-    "<|as|>": 50350,
-    "<|az|>": 50304,
-    "<|ba|>": 50355,
-    "<|be|>": 50330,
-    "<|bg|>": 50292,
-    "<|bn|>": 50302,
-    "<|bo|>": 50347,
-    "<|br|>": 50309,
-    "<|bs|>": 50315,
-    "<|ca|>": 50270,
-    "<|cs|>": 50283,
-    "<|cy|>": 50297,
-    "<|da|>": 50285,
-    "<|de|>": 50261,
-    "<|el|>": 50281,
-    "<|en|>": 50259,
-    "<|es|>": 50262,
-    "<|et|>": 50307,
-    "<|eu|>": 50310,
-    "<|fa|>": 50300,
-    "<|fi|>": 50277,
-    "<|fo|>": 50338,
-    "<|fr|>": 50265,
-    "<|gl|>": 50319,
-    "<|gu|>": 50333,
-    "<|haw|>": 50352,
-    "<|ha|>": 50354,
-    "<|he|>": 50279,
-    "<|hi|>": 50276,
-    "<|hr|>": 50291,
-    "<|ht|>": 50339,
-    "<|hu|>": 50286,
-    "<|hy|>": 50312,
-    "<|id|>": 50275,
-    "<|is|>": 50311,
-    "<|it|>": 50274,
-    "<|ja|>": 50266,
-    "<|jw|>": 50356,
-    "<|ka|>": 50329,
-    "<|kk|>": 50316,
-    "<|km|>": 50323,
-    "<|kn|>": 50306,
-    "<|ko|>": 50264,
-    "<|la|>": 50294,
-    "<|lb|>": 50345,
-    "<|ln|>": 50353,
-    "<|lo|>": 50336,
-    "<|lt|>": 50293,
-    "<|lv|>": 50301,
-    "<|mg|>": 50349,
-    "<|mi|>": 50295,
-    "<|mk|>": 50308,
-    "<|ml|>": 50296,
-    "<|mn|>": 50314,
-    "<|mr|>": 50320,
-    "<|ms|>": 50282,
-    "<|mt|>": 50343,
-    "<|my|>": 50346,
-    "<|ne|>": 50313,
-    "<|nl|>": 50271,
-    "<|nn|>": 50342,
-    "<|no|>": 50288,
-    "<|oc|>": 50328,
-    "<|pa|>": 50321,
-    "<|pl|>": 50269,
-    "<|ps|>": 50340,
-    "<|pt|>": 50267,
-    "<|ro|>": 50284,
-    "<|ru|>": 50263,
-    "<|sa|>": 50344,
-    "<|sd|>": 50332,
-    "<|si|>": 50322,
-    "<|sk|>": 50298,
-    "<|sl|>": 50305,
-    "<|sn|>": 50324,
-    "<|so|>": 50326,
-    "<|sq|>": 50317,
-    "<|sr|>": 50303,
-    "<|su|>": 50357,
-    "<|sv|>": 50273,
-    "<|sw|>": 50318,
-    "<|ta|>": 50287,
-    "<|te|>": 50299,
-    "<|tg|>": 50331,
-    "<|th|>": 50289,
-    "<|tk|>": 50341,
-    "<|tl|>": 50348,
-    "<|tr|>": 50268,
-    "<|tt|>": 50351,
-    "<|uk|>": 50280,
-    "<|ur|>": 50290,
-    "<|uz|>": 50337,
-    "<|vi|>": 50278,
-    "<|yi|>": 50335,
-    "<|yo|>": 50325,
-    "<|zh|>": 50260
-  },
-  "max_initial_timestamp_index": 50,
-  "max_length": 448,
-  "no_timestamps_token_id": 50363,
-  "pad_token_id": 50257,
-  "prev_sot_token_id": 50361,
-  "return_timestamps": false,
-  "suppress_tokens": [
-    1,
-    2,
-    7,
-    8,
-    9,
-    10,
-    14,
-    25,
-    26,
-    27,
-    28,
-    29,
-    31,
-    58,
-    59,
-    60,
-    61,
-    62,
-    63,
-    90,
-    91,
-    92,
-    93,
-    359,
-    503,
-    522,
-    542,
-    873,
-    893,
-    902,
-    918,
-    922,
-    931,
-    1350,
-    1853,
-    1982,
-    2460,
-    2627,
-    3246,
-    3253,
-    3268,
-    3536,
-    3846,
-    3961,
-    4183,
-    4667,
-    6585,
-    6647,
-    7273,
-    9061,
-    9383,
-    10428,
-    10929,
-    11938,
-    12033,
-    12331,
-    12562,
-    13793,
-    14157,
-    14635,
-    15265,
-    15618,
-    16553,
-    16604,
-    18362,
-    18956,
-    20075,
-    21675,
-    22520,
-    26130,
-    26161,
-    26435,
-    28279,
-    29464,
-    31650,
-    32302,
-    32470,
-    36865,
-    42863,
-    47425,
-    49870,
-    50254,
-    50258,
-    50358,
-    50359,
-    50360,
-    50361,
-    50362
-  ],
-  "task_to_id": {
-    "transcribe": 50359,
-    "translate": 50358
-  },
-  "transformers_version": "4.31.0.dev0"
-}

pretrained_models/whisper-small/model.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:1d7734884874f1a1513ed9aa760a4f8e97aaa02fd6d93a3a85d27b2ae9ca596b
-size 966995080

pretrained_models/whisper-small/normalizer.json

@@ -1,1742 +0,0 @@
-{
-  "accessorise": "accessorize",
-  "accessorised": "accessorized",
-  "accessorises": "accessorizes",
-  "accessorising": "accessorizing",
-  "acclimatisation": "acclimatization",
-  "acclimatise": "acclimatize",
-  "acclimatised": "acclimatized",
-  "acclimatises": "acclimatizes",
-  "acclimatising": "acclimatizing",
-  "accoutrements": "accouterments",
-  "aeon": "eon",
-  "aeons": "eons",
-  "aerogramme": "aerogram",
-  "aerogrammes": "aerograms",
-  "aeroplane": "airplane",
-  "aeroplanes": "airplanes",
-  "aesthete": "esthete",
-  "aesthetes": "esthetes",
-  "aesthetic": "esthetic",
-  "aesthetically": "esthetically",
-  "aesthetics": "esthetics",
-  "aetiology": "etiology",
-  "ageing": "aging",
-  "aggrandisement": "aggrandizement",
-  "agonise": "agonize",
-  "agonised": "agonized",
-  "agonises": "agonizes",
-  "agonising": "agonizing",
-  "agonisingly": "agonizingly",
-  "almanack": "almanac",
-  "almanacks": "almanacs",
-  "aluminium": "aluminum",
-  "amortisable": "amortizable",
-  "amortisation": "amortization",
-  "amortisations": "amortizations",
-  "amortise": "amortize",
-  "amortised": "amortized",
-  "amortises": "amortizes",
-  "amortising": "amortizing",
-  "amphitheatre": "amphitheater",
-  "amphitheatres": "amphitheaters",
-  "anaemia": "anemia",
-  "anaemic": "anemic",
-  "anaesthesia": "anesthesia",
-  "anaesthetic": "anesthetic",
-  "anaesthetics": "anesthetics",
-  "anaesthetise": "anesthetize",
-  "anaesthetised": "anesthetized",
-  "anaesthetises": "anesthetizes",
-  "anaesthetising": "anesthetizing",
-  "anaesthetist": "anesthetist",
-  "anaesthetists": "anesthetists",
-  "anaesthetize": "anesthetize",
-  "anaesthetized": "anesthetized",
-  "anaesthetizes": "anesthetizes",
-  "anaesthetizing": "anesthetizing",
-  "analogue": "analog",
-  "analogues": "analogs",
-  "analyse": "analyze",
-  "analysed": "analyzed",
-  "analyses": "analyzes",
-  "analysing": "analyzing",
-  "anglicise": "anglicize",
-  "anglicised": "anglicized",
-  "anglicises": "anglicizes",
-  "anglicising": "anglicizing",
-  "annualised": "annualized",
-  "antagonise": "antagonize",
-  "antagonised": "antagonized",
-  "antagonises": "antagonizes",
-  "antagonising": "antagonizing",
-  "apologise": "apologize",
-  "apologised": "apologized",
-  "apologises": "apologizes",
-  "apologising": "apologizing",
-  "appal": "appall",
-  "appals": "appalls",
-  "appetiser": "appetizer",
-  "appetisers": "appetizers",
-  "appetising": "appetizing",
-  "appetisingly": "appetizingly",
-  "arbour": "arbor",
-  "arbours": "arbors",
-  "archaeologically": "archeologically",
-  "archaeologist": "archeologist",
-  "archaeologists": "archeologists",
-  "archaeology": "archeology</span>",
-  "archeological": "archaeological",
-  "ardour": "ardor",
-  "armour": "armor",
-  "armoured": "armored",
-  "armourer": "armorer",
-  "armourers": "armorers",
-  "armouries": "armories",
-  "armoury": "armory",
-  "artefact": "artifact",
-  "artefacts": "artifacts",
-  "authorise": "authorize",
-  "authorised": "authorized",
-  "authorises": "authorizes",
-  "authorising": "authorizing",
-  "axe": "ax",
-  "backpedalled": "backpedaled",
-  "backpedalling": "backpedaling",
-  "bannister": "banister",
-  "bannisters": "banisters",
-  "baptise": "baptize",
-  "baptised": "baptized",
-  "baptises": "baptizes",
-  "baptising": "baptizing",
-  "bastardise": "bastardize",
-  "bastardised": "bastardized",
-  "bastardises": "bastardizes",
-  "bastardising": "bastardizing",
-  "battleax": "battleaxe",
-  "baulk": "balk",
-  "baulked": "balked",
-  "baulking": "balking",
-  "baulks": "balks",
-  "bedevilled": "bedeviled",
-  "bedevilling": "bedeviling",
-  "behaviour": "behavior",
-  "behavioural": "behavioral",
-  "behaviourism": "behaviorism",
-  "behaviourist": "behaviorist",
-  "behaviourists": "behaviorists",
-  "behaviours": "behaviors",
-  "behove": "behoove",
-  "behoved": "behooved",
-  "behoves": "behooves",
-  "bejewelled": "bejeweled",
-  "belabour": "belabor",
-  "belaboured": "belabored",
-  "belabouring": "belaboring",
-  "belabours": "belabors",
-  "bevelled": "beveled",
-  "bevvies": "bevies",
-  "bevvy": "bevy",
-  "biassed": "biased",
-  "biassing": "biasing",
-  "bingeing": "binging",
-  "bougainvillaea": "bougainvillea",
-  "bougainvillaeas": "bougainvilleas",
-  "bowdlerise": "bowdlerize",
-  "bowdlerised": "bowdlerized",
-  "bowdlerises": "bowdlerizes",
-  "bowdlerising": "bowdlerizing",
-  "breathalyse": "breathalyze",
-  "breathalysed": "breathalyzed",
-  "breathalyser": "breathalyzer",
-  "breathalysers": "breathalyzers",
-  "breathalyses": "breathalyzes",
-  "breathalysing": "breathalyzing",
-  "brutalise": "brutalize",
-  "brutalised": "brutalized",
-  "brutalises": "brutalizes",
-  "brutalising": "brutalizing",
-  "busses": "buses",
-  "bussing": "busing",
-  "caesarean": "cesarean",
-  "caesareans": "cesareans",
-  "calibre": "caliber",
-  "calibres": "calibers",
-  "calliper": "caliper",
-  "callipers": "calipers",
-  "callisthenics": "calisthenics",
-  "canalise": "canalize",
-  "canalised": "canalized",
-  "canalises": "canalizes",
-  "canalising": "canalizing",
-  "cancelation": "cancellation",
-  "cancelations": "cancellations",
-  "cancelled": "canceled",
-  "cancelling": "canceling",
-  "candour": "candor",
-  "cannibalise": "cannibalize",
-  "cannibalised": "cannibalized",
-  "cannibalises": "cannibalizes",
-  "cannibalising": "cannibalizing",
-  "canonise": "canonize",
-  "canonised": "canonized",
-  "canonises": "canonizes",
-  "canonising": "canonizing",
-  "capitalise": "capitalize",
-  "capitalised": "capitalized",
-  "capitalises": "capitalizes",
-  "capitalising": "capitalizing",
-  "caramelise": "caramelize",
-  "caramelised": "caramelized",
-  "caramelises": "caramelizes",
-  "caramelising": "caramelizing",
-  "carbonise": "carbonize",
-  "carbonised": "carbonized",
-  "carbonises": "carbonizes",
-  "carbonising": "carbonizing",
-  "carolled": "caroled",
-  "carolling": "caroling",
-  "catalogue": "catalog",
-  "catalogued": "cataloged",
-  "catalogues": "catalogs",
-  "cataloguing": "cataloging",
-  "catalyse": "catalyze",
-  "catalysed": "catalyzed",
-  "catalyses": "catalyzes",
-  "catalysing": "catalyzing",
-  "categorise": "categorize",
-  "categorised": "categorized",
-  "categorises": "categorizes",
-  "categorising": "categorizing",
-  "cauterise": "cauterize",
-  "cauterised": "cauterized",
-  "cauterises": "cauterizes",
-  "cauterising": "cauterizing",
-  "cavilled": "caviled",
-  "cavilling": "caviling",
-  "centigramme": "centigram",
-  "centigrammes": "centigrams",
-  "centilitre": "centiliter",
-  "centilitres": "centiliters",
-  "centimetre": "centimeter",
-  "centimetres": "centimeters",
-  "centralise": "centralize",
-  "centralised": "centralized",
-  "centralises": "centralizes",
-  "centralising": "centralizing",
-  "centre": "center",
-  "centred": "centered",
-  "centrefold": "centerfold",
-  "centrefolds": "centerfolds",
-  "centrepiece": "centerpiece",
-  "centrepieces": "centerpieces",
-  "centres": "centers",
-  "channelled": "channeled",
-  "channelling": "channeling",
-  "characterise": "characterize",
-  "characterised": "characterized",
-  "characterises": "characterizes",
-  "characterising": "characterizing",
-  "cheque": "check",
-  "chequebook": "checkbook",
-  "chequebooks": "checkbooks",
-  "chequered": "checkered",
-  "cheques": "checks",
-  "chilli": "chili",
-  "chimaera": "chimera",
-  "chimaeras": "chimeras",
-  "chiselled": "chiseled",
-  "chiselling": "chiseling",
-  "circularise": "circularize",
-  "circularised": "circularized",
-  "circularises": "circularizes",
-  "circularising": "circularizing",
-  "civilise": "civilize",
-  "civilised": "civilized",
-  "civilises": "civilizes",
-  "civilising": "civilizing",
-  "clamour": "clamor",
-  "clamoured": "clamored",
-  "clamouring": "clamoring",
-  "clamours": "clamors",
-  "clangour": "clangor",
-  "clarinettist": "clarinetist",
-  "clarinettists": "clarinetists",
-  "collectivise": "collectivize",
-  "collectivised": "collectivized",
-  "collectivises": "collectivizes",
-  "collectivising": "collectivizing",
-  "colonisation": "colonization",
-  "colonise": "colonize",
-  "colonised": "colonized",
-  "coloniser": "colonizer",
-  "colonisers": "colonizers",
-  "colonises": "colonizes",
-  "colonising": "colonizing",
-  "colour": "color",
-  "colourant": "colorant",
-  "colourants": "colorants",
-  "coloured": "colored",
-  "coloureds": "coloreds",
-  "colourful": "colorful",
-  "colourfully": "colorfully",
-  "colouring": "coloring",
-  "colourize": "colorize",
-  "colourized": "colorized",
-  "colourizes": "colorizes",
-  "colourizing": "colorizing",
-  "colourless": "colorless",
-  "colours": "colors",
-  "commercialise": "commercialize",
-  "commercialised": "commercialized",
-  "commercialises": "commercializes",
-  "commercialising": "commercializing",
-  "compartmentalise": "compartmentalize",
-  "compartmentalised": "compartmentalized",
-  "compartmentalises": "compartmentalizes",
-  "compartmentalising": "compartmentalizing",
-  "computerise": "computerize",
-  "computerised": "computerized",
-  "computerises": "computerizes",
-  "computerising": "computerizing",
-  "conceptualise": "conceptualize",
-  "conceptualised": "conceptualized",
-  "conceptualises": "conceptualizes",
-  "conceptualising": "conceptualizing",
-  "connexion": "connection",
-  "connexions": "connections",
-  "contextualise": "contextualize",
-  "contextualised": "contextualized",
-  "contextualises": "contextualizes",
-  "contextualising": "contextualizing",
-  "cosier": "cozier",
-  "cosies": "cozies",
-  "cosiest": "coziest",
-  "cosily": "cozily",
-  "cosiness": "coziness",
-  "cosy": "cozy",
-  "councillor": "councilor",
-  "councillors": "councilors",
-  "counselled": "counseled",
-  "counselling": "counseling",
-  "counsellor": "counselor",
-  "counsellors": "counselors",
-  "crenelated": "crenellated",
-  "criminalise": "criminalize",
-  "criminalised": "criminalized",
-  "criminalises": "criminalizes",
-  "criminalising": "criminalizing",
-  "criticise": "criticize",
-  "criticised": "criticized",
-  "criticises": "criticizes",
-  "criticising": "criticizing",
-  "crueller": "crueler",
-  "cruellest": "cruelest",
-  "crystallisation": "crystallization",
-  "crystallise": "crystallize",
-  "crystallised": "crystallized",
-  "crystallises": "crystallizes",
-  "crystallising": "crystallizing",
-  "cudgelled": "cudgeled",
-  "cudgelling": "cudgeling",
-  "customise": "customize",
-  "customised": "customized",
-  "customises": "customizes",
-  "customising": "customizing",
-  "cypher": "cipher",
-  "cyphers": "ciphers",
-  "decentralisation": "decentralization",
-  "decentralise": "decentralize",
-  "decentralised": "decentralized",
-  "decentralises": "decentralizes",
-  "decentralising": "decentralizing",
-  "decriminalisation": "decriminalization",
-  "decriminalise": "decriminalize",
-  "decriminalised": "decriminalized",
-  "decriminalises": "decriminalizes",
-  "decriminalising": "decriminalizing",
-  "defence": "defense",
-  "defenceless": "defenseless",
-  "defences": "defenses",
-  "dehumanisation": "dehumanization",
-  "dehumanise": "dehumanize",
-  "dehumanised": "dehumanized",
-  "dehumanises": "dehumanizes",
-  "dehumanising": "dehumanizing",
-  "demeanour": "demeanor",
-  "demilitarisation": "demilitarization",
-  "demilitarise": "demilitarize",
-  "demilitarised": "demilitarized",
-  "demilitarises": "demilitarizes",
-  "demilitarising": "demilitarizing",
-  "demobilisation": "demobilization",
-  "demobilise": "demobilize",
-  "demobilised": "demobilized",
-  "demobilises": "demobilizes",
-  "demobilising": "demobilizing",
-  "democratisation": "democratization",
-  "democratise": "democratize",
-  "democratised": "democratized",
-  "democratises": "democratizes",
-  "democratising": "democratizing",
-  "demonise": "demonize",
-  "demonised": "demonized",
-  "demonises": "demonizes",
-  "demonising": "demonizing",
-  "demoralisation": "demoralization",
-  "demoralise": "demoralize",
-  "demoralised": "demoralized",
-  "demoralises": "demoralizes",
-  "demoralising": "demoralizing",
-  "denationalisation": "denationalization",
-  "denationalise": "denationalize",
-  "denationalised": "denationalized",
-  "denationalises": "denationalizes",
-  "denationalising": "denationalizing",
-  "deodorise": "deodorize",
-  "deodorised": "deodorized",
-  "deodorises": "deodorizes",
-  "deodorising": "deodorizing",
-  "depersonalise": "depersonalize",
-  "depersonalised": "depersonalized",
-  "depersonalises": "depersonalizes",
-  "depersonalising": "depersonalizing",
-  "deputise": "deputize",
-  "deputised": "deputized",
-  "deputises": "deputizes",
-  "deputising": "deputizing",
-  "desensitisation": "desensitization",
-  "desensitise": "desensitize",
-  "desensitised": "desensitized",
-  "desensitises": "desensitizes",
-  "desensitising": "desensitizing",
-  "destabilisation": "destabilization",
-  "destabilise": "destabilize",
-  "destabilised": "destabilized",
-  "destabilises": "destabilizes",
-  "destabilising": "destabilizing",
-  "dialled": "dialed",
-  "dialling": "dialing",
-  "dialogue": "dialog",
-  "dialogues": "dialogs",
-  "diarrhoea": "diarrhea",
-  "digitise": "digitize",
-  "digitised": "digitized",
-  "digitises": "digitizes",
-  "digitising": "digitizing",
-  "disc": "disk",
-  "discolour": "discolor",
-  "discoloured": "discolored",
-  "discolouring": "discoloring",
-  "discolours": "discolors",
-  "discs": "disks",
-  "disembowelled": "disemboweled",
-  "disembowelling": "disemboweling",
-  "disfavour": "disfavor",
-  "dishevelled": "disheveled",
-  "dishonour": "dishonor",
-  "dishonourable": "dishonorable",
-  "dishonourably": "dishonorably",
-  "dishonoured": "dishonored",
-  "dishonouring": "dishonoring",
-  "dishonours": "dishonors",
-  "disorganisation": "disorganization",
-  "disorganised": "disorganized",
-  "distil": "distill",
-  "distils": "distills",
-  "dramatisation": "dramatization",
-  "dramatisations": "dramatizations",
-  "dramatise": "dramatize",
-  "dramatised": "dramatized",
-  "dramatises": "dramatizes",
-  "dramatising": "dramatizing",
-  "draught": "draft",
-  "draughtboard": "draftboard",
-  "draughtboards": "draftboards",
-  "draughtier": "draftier",
-  "draughtiest": "draftiest",
-  "draughts": "drafts",
-  "draughtsman": "draftsman",
-  "draughtsmanship": "draftsmanship",
-  "draughtsmen": "draftsmen",
-  "draughtswoman": "draftswoman",
-  "draughtswomen": "draftswomen",
-  "draughty": "drafty",
-  "drivelled": "driveled",
-  "drivelling": "driveling",
-  "duelled": "dueled",
-  "duelling": "dueling",
-  "economise": "economize",
-  "economised": "economized",
-  "economises": "economizes",
-  "economising": "economizing",
-  "editorialise": "editorialize",
-  "editorialised": "editorialized",
-  "editorialises": "editorializes",
-  "editorialising": "editorializing",
-  "edoema": "edema",
-  "empathise": "empathize",
-  "empathised": "empathized",
-  "empathises": "empathizes",
-  "empathising": "empathizing",
-  "emphasise": "emphasize",
-  "emphasised": "emphasized",
-  "emphasises": "emphasizes",
-  "emphasising": "emphasizing",
-  "enamelled": "enameled",
-  "enamelling": "enameling",
-  "enamoured": "enamored",
-  "encyclopaedia": "encyclopedia",
-  "encyclopaedias": "encyclopedias",
-  "encyclopaedic": "encyclopedic",
-  "endeavour": "endeavor",
-  "endeavoured": "endeavored",
-  "endeavouring": "endeavoring",
-  "endeavours": "endeavors",
-  "energise": "energize",
-  "energised": "energized",
-  "energises": "energizes",
-  "energising": "energizing",
-  "enrol": "enroll",
-  "enrols": "enrolls",
-  "enthral": "enthrall",
-  "enthrals": "enthralls",
-  "epaulette": "epaulet",
-  "epaulettes": "epaulets",
-  "epicentre": "epicenter",
-  "epicentres": "epicenters",
-  "epilogue": "epilog",
-  "epilogues": "epilogs",
-  "epitomise": "epitomize",
-  "epitomised": "epitomized",
-  "epitomises": "epitomizes",
-  "epitomising": "epitomizing",
-  "equalisation": "equalization",
-  "equalise": "equalize",
-  "equalised": "equalized",
-  "equaliser": "equalizer",
-  "equalisers": "equalizers",
-  "equalises": "equalizes",
-  "equalising": "equalizing",
-  "eulogise": "eulogize",
-  "eulogised": "eulogized",
-  "eulogises": "eulogizes",
-  "eulogising": "eulogizing",
-  "evangelise": "evangelize",
-  "evangelised": "evangelized",
-  "evangelises": "evangelizes",
-  "evangelising": "evangelizing",
-  "exorcise": "exorcize",
-  "exorcised": "exorcized",
-  "exorcises": "exorcizes",
-  "exorcising": "exorcizing",
-  "extemporisation": "extemporization",
-  "extemporise": "extemporize",
-  "extemporised": "extemporized",
-  "extemporises": "extemporizes",
-  "extemporising": "extemporizing",
-  "externalisation": "externalization",
-  "externalisations": "externalizations",
-  "externalise": "externalize",
-  "externalised": "externalized",
-  "externalises": "externalizes",
-  "externalising": "externalizing",
-  "factorise": "factorize",
-  "factorised": "factorized",
-  "factorises": "factorizes",
-  "factorising": "factorizing",
-  "faecal": "fecal",
-  "faeces": "feces",
-  "familiarisation": "familiarization",
-  "familiarise": "familiarize",
-  "familiarised": "familiarized",
-  "familiarises": "familiarizes",
-  "familiarising": "familiarizing",
-  "fantasise": "fantasize",
-  "fantasised": "fantasized",
-  "fantasises": "fantasizes",
-  "fantasising": "fantasizing",
-  "favour": "favor",
-  "favourable": "favorable",
-  "favourably": "favorably",
-  "favoured": "favored",
-  "favouring": "favoring",
-  "favourite": "favorite",
-  "favourites": "favorites",
-  "favouritism": "favoritism",
-  "favours": "favors",
-  "feminise": "feminize",
-  "feminised": "feminized",
-  "feminises": "feminizes",
-  "feminising": "feminizing",
-  "fertilisation": "fertilization",
-  "fertilise": "fertilize",
-  "fertilised": "fertilized",
-  "fertiliser": "fertilizer",
-  "fertilisers": "fertilizers",
-  "fertilises": "fertilizes",
-  "fertilising": "fertilizing",
-  "fervour": "fervor",
-  "fibre": "fiber",
-  "fibreglass": "fiberglass",
-  "fibres": "fibers",
-  "fictionalisation": "fictionalization",
-  "fictionalisations": "fictionalizations",
-  "fictionalise": "fictionalize",
-  "fictionalised": "fictionalized",
-  "fictionalises": "fictionalizes",
-  "fictionalising": "fictionalizing",
-  "fillet": "filet",
-  "filleted": "fileted",
-  "filleting": "fileting",
-  "fillets": "filets",
-  "finalisation": "finalization",
-  "finalise": "finalize",
-  "finalised": "finalized",
-  "finalises": "finalizes",
-  "finalising": "finalizing",
-  "flautist": "flutist",
-  "flautists": "flutists",
-  "flavour": "flavor",
-  "flavoured": "flavored",
-  "flavouring": "flavoring",
-  "flavourings": "flavorings",
-  "flavourless": "flavorless",
-  "flavours": "flavors",
-  "flavoursome": "flavorsome",
-  "flyer / flier": "flier / flyer",
-  "foetal": "fetal",
-  "foetid": "fetid",
-  "foetus": "fetus",
-  "foetuses": "fetuses",
-  "formalisation": "formalization",
-  "formalise": "formalize",
-  "formalised": "formalized",
-  "formalises": "formalizes",
-  "formalising": "formalizing",
-  "fossilisation": "fossilization",
-  "fossilise": "fossilize",
-  "fossilised": "fossilized",
-  "fossilises": "fossilizes",
-  "fossilising": "fossilizing",
-  "fraternisation": "fraternization",
-  "fraternise": "fraternize",
-  "fraternised": "fraternized",
-  "fraternises": "fraternizes",
-  "fraternising": "fraternizing",
-  "fulfil": "fulfill",
-  "fulfilment": "fulfillment",
-  "fulfils": "fulfills",
-  "funnelled": "funneled",
-  "funnelling": "funneling",
-  "gage": "gauge",
-  "gaged": "gauged",
-  "gages": "gauges",
-  "gaging": "gauging",
-  "galvanise": "galvanize",
-  "galvanised": "galvanized",
-  "galvanises": "galvanizes",
-  "galvanising": "galvanizing",
-  "gambolled": "gamboled",
-  "gambolling": "gamboling",
-  "gaol": "jail",
-  "gaolbird": "jailbird",
-  "gaolbirds": "jailbirds",
-  "gaolbreak": "jailbreak",
-  "gaolbreaks": "jailbreaks",
-  "gaoled": "jailed",
-  "gaoler": "jailer",
-  "gaolers": "jailers",
-  "gaoling": "jailing",
-  "gaols": "jails",
-  "gasses": "gases",
-  "generalisation": "generalization",
-  "generalisations": "generalizations",
-  "generalise": "generalize",
-  "generalised": "generalized",
-  "generalises": "generalizes",
-  "generalising": "generalizing",
-  "ghettoise": "ghettoize",
-  "ghettoised": "ghettoized",
-  "ghettoises": "ghettoizes",
-  "ghettoising": "ghettoizing",
-  "gipsies": "gypsies",
-  "glamor": "glamour",
-  "glamorise": "glamorize",
-  "glamorised": "glamorized",
-  "glamorises": "glamorizes",
-  "glamorising": "glamorizing",
-  "globalisation": "globalization",
-  "globalise": "globalize",
-  "globalised": "globalized",
-  "globalises": "globalizes",
-  "globalising": "globalizing",
-  "glueing": "gluing",
-  "goitre": "goiter",
-  "goitres": "goiters",
-  "gonorrhoea": "gonorrhea",
-  "gramme": "gram",
-  "grammes": "grams",
-  "gravelled": "graveled",
-  "grey": "gray",
-  "greyed": "grayed",
-  "greying": "graying",
-  "greyish": "grayish",
-  "greyness": "grayness",
-  "greys": "grays",
-  "grovelled": "groveled",
-  "grovelling": "groveling",
-  "groyne": "groin",
-  "groynes": "groins",
-  "gruelling": "grueling",
-  "gruellingly": "gruelingly",
-  "gryphon": "griffin",
-  "gryphons": "griffins",
-  "gynaecological": "gynecological",
-  "gynaecologist": "gynecologist",
-  "gynaecologists": "gynecologists",
-  "gynaecology": "gynecology",
-  "haematological": "hematological",
-  "haematologist": "hematologist",
-  "haematologists": "hematologists",
-  "haematology": "hematology",
-  "haemoglobin": "hemoglobin",
-  "haemophilia": "hemophilia",
-  "haemophiliac": "hemophiliac",
-  "haemophiliacs": "hemophiliacs",
-  "haemorrhage": "hemorrhage",
-  "haemorrhaged": "hemorrhaged",
-  "haemorrhages": "hemorrhages",
-  "haemorrhaging": "hemorrhaging",
-  "haemorrhoids": "hemorrhoids",
-  "harbour": "harbor",
-  "harboured": "harbored",
-  "harbouring": "harboring",
-  "harbours": "harbors",
-  "harmonisation": "harmonization",
-  "harmonise": "harmonize",
-  "harmonised": "harmonized",
-  "harmonises": "harmonizes",
-  "harmonising": "harmonizing",
-  "homoeopath": "homeopath",
-  "homoeopathic": "homeopathic",
-  "homoeopaths": "homeopaths",
-  "homoeopathy": "homeopathy",
-  "homogenise": "homogenize",
-  "homogenised": "homogenized",
-  "homogenises": "homogenizes",
-  "homogenising": "homogenizing",
-  "honour": "honor",
-  "honourable": "honorable",
-  "honourably": "honorably",
-  "honoured": "honored",
-  "honouring": "honoring",
-  "honours": "honors",
-  "hospitalisation": "hospitalization",
-  "hospitalise": "hospitalize",
-  "hospitalised": "hospitalized",
-  "hospitalises": "hospitalizes",
-  "hospitalising": "hospitalizing",
-  "humanise": "humanize",
-  "humanised": "humanized",
-  "humanises": "humanizes",
-  "humanising": "humanizing",
-  "humour": "humor",
-  "humoured": "humored",
-  "humouring": "humoring",
-  "humourless": "humorless",
-  "humours": "humors",
-  "hybridise": "hybridize",
-  "hybridised": "hybridized",
-  "hybridises": "hybridizes",
-  "hybridising": "hybridizing",
-  "hypnotise": "hypnotize",
-  "hypnotised": "hypnotized",
-  "hypnotises": "hypnotizes",
-  "hypnotising": "hypnotizing",
-  "hypothesise": "hypothesize",
-  "hypothesised": "hypothesized",
-  "hypothesises": "hypothesizes",
-  "hypothesising": "hypothesizing",
-  "idealisation": "idealization",
-  "idealise": "idealize",
-  "idealised": "idealized",
-  "idealises": "idealizes",
-  "idealising": "idealizing",
-  "idolise": "idolize",
-  "idolised": "idolized",
-  "idolises": "idolizes",
-  "idolising": "idolizing",
-  "immobilisation": "immobilization",
-  "immobilise": "immobilize",
-  "immobilised": "immobilized",
-  "immobiliser": "immobilizer",
-  "immobilisers": "immobilizers",
-  "immobilises": "immobilizes",
-  "immobilising": "immobilizing",
-  "immortalise": "immortalize",
-  "immortalised": "immortalized",
-  "immortalises": "immortalizes",
-  "immortalising": "immortalizing",
-  "immunisation": "immunization",
-  "immunise": "immunize",
-  "immunised": "immunized",
-  "immunises": "immunizes",
-  "immunising": "immunizing",
-  "impanelled": "impaneled",
-  "impanelling": "impaneling",
-  "imperilled": "imperiled",
-  "imperilling": "imperiling",
-  "individualise": "individualize",
-  "individualised": "individualized",
-  "individualises": "individualizes",
-  "individualising": "individualizing",
-  "industrialise": "industrialize",
-  "industrialised": "industrialized",
-  "industrialises": "industrializes",
-  "industrialising": "industrializing",
-  "inflexion": "inflection",
-  "inflexions": "inflections",
-  "initialise": "initialize",
-  "initialised": "initialized",
-  "initialises": "initializes",
-  "initialising": "initializing",
-  "initialled": "initialed",
-  "initialling": "initialing",
-  "instal": "install",
-  "instalment": "installment",
-  "instalments": "installments",
-  "instals": "installs",
-  "instil": "instill",
-  "instils": "instills",
-  "institutionalisation": "institutionalization",
-  "institutionalise": "institutionalize",
-  "institutionalised": "institutionalized",
-  "institutionalises": "institutionalizes",
-  "institutionalising": "institutionalizing",
-  "intellectualise": "intellectualize",
-  "intellectualised": "intellectualized",
-  "intellectualises": "intellectualizes",
-  "intellectualising": "intellectualizing",
-  "internalisation": "internalization",
-  "internalise": "internalize",
-  "internalised": "internalized",
-  "internalises": "internalizes",
-  "internalising": "internalizing",
-  "internationalisation": "internationalization",
-  "internationalise": "internationalize",
-  "internationalised": "internationalized",
-  "internationalises": "internationalizes",
-  "internationalising": "internationalizing",
-  "ionisation": "ionization",
-  "ionise": "ionize",
-  "ionised": "ionized",
-  "ioniser": "ionizer",
-  "ionisers": "ionizers",
-  "ionises": "ionizes",
-  "ionising": "ionizing",
-  "italicise": "italicize",
-  "italicised": "italicized",
-  "italicises": "italicizes",
-  "italicising": "italicizing",
-  "itemise": "itemize",
-  "itemised": "itemized",
-  "itemises": "itemizes",
-  "itemising": "itemizing",
-  "jeopardise": "jeopardize",
-  "jeopardised": "jeopardized",
-  "jeopardises": "jeopardizes",
-  "jeopardising": "jeopardizing",
-  "jewelled": "jeweled",
-  "jeweller": "jeweler",
-  "jewellers": "jewelers",
-  "jewellery": "jewelry",
-  "judgement": "judgment",
-  "kilogramme": "kilogram",
-  "kilogrammes": "kilograms",
-  "kilometre": "kilometer",
-  "kilometres": "kilometers",
-  "labelled": "labeled",
-  "labelling": "labeling",
-  "labour": "labor",
-  "laboured": "labored",
-  "labourer": "laborer",
-  "labourers": "laborers",
-  "labouring": "laboring",
-  "labours": "labors",
-  "lacklustre": "lackluster",
-  "legalisation": "legalization",
-  "legalise": "legalize",
-  "legalised": "legalized",
-  "legalises": "legalizes",
-  "legalising": "legalizing",
-  "legitimise": "legitimize",
-  "legitimised": "legitimized",
-  "legitimises": "legitimizes",
-  "legitimising": "legitimizing",
-  "leukaemia": "leukemia",
-  "levelled": "leveled",
-  "leveller": "leveler",
-  "levellers": "levelers",
-  "levelling": "leveling",
-  "libelled": "libeled",
-  "libelling": "libeling",
-  "libellous": "libelous",
-  "liberalisation": "liberalization",
-  "liberalise": "liberalize",
-  "liberalised": "liberalized",
-  "liberalises": "liberalizes",
-  "liberalising": "liberalizing",
-  "licence": "license",
-  "licenced": "licensed",
-  "licences": "licenses",
-  "licencing": "licensing",
-  "likeable": "likable",
-  "lionisation": "lionization",
-  "lionise": "lionize",
-  "lionised": "lionized",
-  "lionises": "lionizes",
-  "lionising": "lionizing",
-  "liquidise": "liquidize",
-  "liquidised": "liquidized",
-  "liquidiser": "liquidizer",
-  "liquidisers": "liquidizers",
-  "liquidises": "liquidizes",
-  "liquidising": "liquidizing",
-  "litre": "liter",
-  "litres": "liters",
-  "localise": "localize",
-  "localised": "localized",
-  "localises": "localizes",
-  "localising": "localizing",
-  "louvre": "louver",
-  "louvred": "louvered",
-  "louvres": "louvers",
-  "lustre": "luster",
-  "magnetise": "magnetize",
-  "magnetised": "magnetized",
-  "magnetises": "magnetizes",
-  "magnetising": "magnetizing",
-  "manoeuvrability": "maneuverability",
-  "manoeuvrable": "maneuverable",
-  "manoeuvre": "maneuver",
-  "manoeuvred": "maneuvered",
-  "manoeuvres": "maneuvers",
-  "manoeuvring": "maneuvering",
-  "manoeuvrings": "maneuverings",
-  "marginalisation": "marginalization",
-  "marginalise": "marginalize",
-  "marginalised": "marginalized",
-  "marginalises": "marginalizes",
-  "marginalising": "marginalizing",
-  "marshalled": "marshaled",
-  "marshalling": "marshaling",
-  "marvelled": "marveled",
-  "marvelling": "marveling",
-  "marvellous": "marvelous",
-  "marvellously": "marvelously",
-  "materialisation": "materialization",
-  "materialise": "materialize",
-  "materialised": "materialized",
-  "materialises": "materializes",
-  "materialising": "materializing",
-  "maximisation": "maximization",
-  "maximise": "maximize",
-  "maximised": "maximized",
-  "maximises": "maximizes",
-  "maximising": "maximizing",
-  "meagre": "meager",
-  "mechanisation": "mechanization",
-  "mechanise": "mechanize",
-  "mechanised": "mechanized",
-  "mechanises": "mechanizes",
-  "mechanising": "mechanizing",
-  "mediaeval": "medieval",
-  "memorialise": "memorialize",
-  "memorialised": "memorialized",
-  "memorialises": "memorializes",
-  "memorialising": "memorializing",
-  "memorise": "memorize",
-  "memorised": "memorized",
-  "memorises": "memorizes",
-  "memorising": "memorizing",
-  "mesmerise": "mesmerize",
-  "mesmerised": "mesmerized",
-  "mesmerises": "mesmerizes",
-  "mesmerising": "mesmerizing",
-  "metabolise": "metabolize",
-  "metabolised": "metabolized",
-  "metabolises": "metabolizes",
-  "metabolising": "metabolizing",
-  "metre": "meter",
-  "metres": "meters",
-  "mhm": "hmm",
-  "micrometre": "micrometer",
-  "micrometres": "micrometers",
-  "militarise": "militarize",
-  "militarised": "militarized",
-  "militarises": "militarizes",
-  "militarising": "militarizing",
-  "milligramme": "milligram",
-  "milligrammes": "milligrams",
-  "millilitre": "milliliter",
-  "millilitres": "milliliters",
-  "millimetre": "millimeter",
-  "millimetres": "millimeters",
-  "miniaturisation": "miniaturization",
-  "miniaturise": "miniaturize",
-  "miniaturised": "miniaturized",
-  "miniaturises": "miniaturizes",
-  "miniaturising": "miniaturizing",
-  "minibusses": "minibuses",
-  "minimise": "minimize",
-  "minimised": "minimized",
-  "minimises": "minimizes",
-  "minimising": "minimizing",
-  "misbehaviour": "misbehavior",
-  "misdemeanour": "misdemeanor",
-  "misdemeanours": "misdemeanors",
-  "misspelt": "misspelled",
-  "mitre": "miter",
-  "mitres": "miters",
-  "mm": "hmm",
-  "mmm": "hmm",
-  "mobilisation": "mobilization",
-  "mobilise": "mobilize",
-  "mobilised": "mobilized",
-  "mobilises": "mobilizes",
-  "mobilising": "mobilizing",
-  "modelled": "modeled",
-  "modeller": "modeler",
-  "modellers": "modelers",
-  "modelling": "modeling",
-  "modernise": "modernize",
-  "modernised": "modernized",
-  "modernises": "modernizes",
-  "modernising": "modernizing",
-  "moisturise": "moisturize",
-  "moisturised": "moisturized",
-  "moisturiser": "moisturizer",
-  "moisturisers": "moisturizers",
-  "moisturises": "moisturizes",
-  "moisturising": "moisturizing",
-  "monologue": "monolog",
-  "monologues": "monologs",
-  "monopolisation": "monopolization",
-  "monopolise": "monopolize",
-  "monopolised": "monopolized",
-  "monopolises": "monopolizes",
-  "monopolising": "monopolizing",
-  "moralise": "moralize",
-  "moralised": "moralized",
-  "moralises": "moralizes",
-  "moralising": "moralizing",
-  "motorised": "motorized",
-  "mould": "mold",
-  "moulded": "molded",
-  "moulder": "molder",
-  "mouldered": "moldered",
-  "mouldering": "moldering",
-  "moulders": "molders",
-  "mouldier": "moldier",
-  "mouldiest": "moldiest",
-  "moulding": "molding",
-  "mouldings": "moldings",
-  "moulds": "molds",
-  "mouldy": "moldy",
-  "moult": "molt",
-  "moulted": "molted",
-  "moulting": "molting",
-  "moults": "molts",
-  "moustache": "mustache",
-  "moustached": "mustached",
-  "moustaches": "mustaches",
-  "moustachioed": "mustachioed",
-  "multicoloured": "multicolored",
-  "nationalisation": "nationalization",
-  "nationalisations": "nationalizations",
-  "nationalise": "nationalize",
-  "nationalised": "nationalized",
-  "nationalises": "nationalizes",
-  "nationalising": "nationalizing",
-  "naturalisation": "naturalization",
-  "naturalise": "naturalize",
-  "naturalised": "naturalized",
-  "naturalises": "naturalizes",
-  "naturalising": "naturalizing",
-  "neighbour": "neighbor",
-  "neighbourhood": "neighborhood",
-  "neighbourhoods": "neighborhoods",
-  "neighbouring": "neighboring",
-  "neighbourliness": "neighborliness",
-  "neighbourly": "neighborly",
-  "neighbours": "neighbors",
-  "neutralisation": "neutralization",
-  "neutralise": "neutralize",
-  "neutralised": "neutralized",
-  "neutralises": "neutralizes",
-  "neutralising": "neutralizing",
-  "normalisation": "normalization",
-  "normalise": "normalize",
-  "normalised": "normalized",
-  "normalises": "normalizes",
-  "normalising": "normalizing",
-  "odour": "odor",
-  "odourless": "odorless",
-  "odours": "odors",
-  "oesophagus": "esophagus",
-  "oesophaguses": "esophaguses",
-  "oestrogen": "estrogen",
-  "offence": "offense",
-  "offences": "offenses",
-  "omelette": "omelet",
-  "omelettes": "omelets",
-  "optimise": "optimize",
-  "optimised": "optimized",
-  "optimises": "optimizes",
-  "optimising": "optimizing",
-  "organisation": "organization",
-  "organisational": "organizational",
-  "organisations": "organizations",
-  "organise": "organize",
-  "organised": "organized",
-  "organiser": "organizer",
-  "organisers": "organizers",
-  "organises": "organizes",
-  "organising": "organizing",
-  "orthopaedic": "orthopedic",
-  "orthopaedics": "orthopedics",
-  "ostracise": "ostracize",
-  "ostracised": "ostracized",
-  "ostracises": "ostracizes",
-  "ostracising": "ostracizing",
-  "outmanoeuvre": "outmaneuver",
-  "outmanoeuvred": "outmaneuvered",
-  "outmanoeuvres": "outmaneuvers",
-  "outmanoeuvring": "outmaneuvering",
-  "overemphasise": "overemphasize",
-  "overemphasised": "overemphasized",
-  "overemphasises": "overemphasizes",
-  "overemphasising": "overemphasizing",
-  "oxidisation": "oxidization",
-  "oxidise": "oxidize",
-  "oxidised": "oxidized",
-  "oxidises": "oxidizes",
-  "oxidising": "oxidizing",
-  "paederast": "pederast",
-  "paederasts": "pederasts",
-  "paediatric": "pediatric",
-  "paediatrician": "pediatrician",
-  "paediatricians": "pediatricians",
-  "paediatrics": "pediatrics",
-  "paedophile": "pedophile",
-  "paedophiles": "pedophiles",
-  "paedophilia": "pedophilia",
-  "palaeolithic": "paleolithic",
-  "palaeontologist": "paleontologist",
-  "palaeontologists": "paleontologists",
-  "palaeontology": "paleontology",
-  "panelled": "paneled",
-  "panelling": "paneling",
-  "panellist": "panelist",
-  "panellists": "panelists",
-  "paralyse": "paralyze",
-  "paralysed": "paralyzed",
-  "paralyses": "paralyzes",
-  "paralysing": "paralyzing",
-  "parcelled": "parceled",
-  "parcelling": "parceling",
-  "parlour": "parlor",
-  "parlours": "parlors",
-  "particularise": "particularize",
-  "particularised": "particularized",
-  "particularises": "particularizes",
-  "particularising": "particularizing",
-  "passivisation": "passivization",
-  "passivise": "passivize",
-  "passivised": "passivized",
-  "passivises": "passivizes",
-  "passivising": "passivizing",
-  "pasteurisation": "pasteurization",
-  "pasteurise": "pasteurize",
-  "pasteurised": "pasteurized",
-  "pasteurises": "pasteurizes",
-  "pasteurising": "pasteurizing",
-  "patronise": "patronize",
-  "patronised": "patronized",
-  "patronises": "patronizes",
-  "patronising": "patronizing",
-  "patronisingly": "patronizingly",
-  "pedalled": "pedaled",
-  "pedalling": "pedaling",
-  "pedestrianisation": "pedestrianization",
-  "pedestrianise": "pedestrianize",
-  "pedestrianised": "pedestrianized",
-  "pedestrianises": "pedestrianizes",
-  "pedestrianising": "pedestrianizing",
-  "penalise": "penalize",
-  "penalised": "penalized",
-  "penalises": "penalizes",
-  "penalising": "penalizing",
-  "pencilled": "penciled",
-  "pencilling": "penciling",
-  "personalise": "personalize",
-  "personalised": "personalized",
-  "personalises": "personalizes",
-  "personalising": "personalizing",
-  "pharmacopoeia": "pharmacopeia",
-  "pharmacopoeias": "pharmacopeias",
-  "philosophise": "philosophize",
-  "philosophised": "philosophized",
-  "philosophises": "philosophizes",
-  "philosophising": "philosophizing",
-  "philtre": "filter",
-  "philtres": "filters",
-  "phoney": "phony",
-  "plagiarise": "plagiarize",
-  "plagiarised": "plagiarized",
-  "plagiarises": "plagiarizes",
-  "plagiarising": "plagiarizing",
-  "plough": "plow",
-  "ploughed": "plowed",
-  "ploughing": "plowing",
-  "ploughman": "plowman",
-  "ploughmen": "plowmen",
-  "ploughs": "plows",
-  "ploughshare": "plowshare",
-  "ploughshares": "plowshares",
-  "polarisation": "polarization",
-  "polarise": "polarize",
-  "polarised": "polarized",
-  "polarises": "polarizes",
-  "polarising": "polarizing",
-  "politicisation": "politicization",
-  "politicise": "politicize",
-  "politicised": "politicized",
-  "politicises": "politicizes",
-  "politicising": "politicizing",
-  "popularisation": "popularization",
-  "popularise": "popularize",
-  "popularised": "popularized",
-  "popularises": "popularizes",
-  "popularising": "popularizing",
-  "pouffe": "pouf",
-  "pouffes": "poufs",
-  "practise": "practice",
-  "practised": "practiced",
-  "practises": "practices",
-  "practising": "practicing",
-  "praesidium": "presidium",
-  "praesidiums": "presidiums",
-  "pressurisation": "pressurization",
-  "pressurise": "pressurize",
-  "pressurised": "pressurized",
-  "pressurises": "pressurizes",
-  "pressurising": "pressurizing",
-  "pretence": "pretense",
-  "pretences": "pretenses",
-  "primaeval": "primeval",
-  "prioritisation": "prioritization",
-  "prioritise": "prioritize",
-  "prioritised": "prioritized",
-  "prioritises": "prioritizes",
-  "prioritising": "prioritizing",
-  "privatisation": "privatization",
-  "privatisations": "privatizations",
-  "privatise": "privatize",
-  "privatised": "privatized",
-  "privatises": "privatizes",
-  "privatising": "privatizing",
-  "professionalisation": "professionalization",
-  "professionalise": "professionalize",
-  "professionalised": "professionalized",
-  "professionalises": "professionalizes",
-  "professionalising": "professionalizing",
-  "programme": "program",
-  "programmes": "programs",
-  "prologue": "prolog",
-  "prologues": "prologs",
-  "propagandise": "propagandize",
-  "propagandised": "propagandized",
-  "propagandises": "propagandizes",
-  "propagandising": "propagandizing",
-  "proselytise": "proselytize",
-  "proselytised": "proselytized",
-  "proselytiser": "proselytizer",
-  "proselytisers": "proselytizers",
-  "proselytises": "proselytizes",
-  "proselytising": "proselytizing",
-  "psychoanalyse": "psychoanalyze",
-  "psychoanalysed": "psychoanalyzed",
-  "psychoanalyses": "psychoanalyzes",
-  "psychoanalysing": "psychoanalyzing",
-  "publicise": "publicize",
-  "publicised": "publicized",
-  "publicises": "publicizes",
-  "publicising": "publicizing",
-  "pulverisation": "pulverization",
-  "pulverise": "pulverize",
-  "pulverised": "pulverized",
-  "pulverises": "pulverizes",
-  "pulverising": "pulverizing",
-  "pummelled": "pummel",
-  "pummelling": "pummeled",
-  "pyjama": "pajama",
-  "pyjamas": "pajamas",
-  "pzazz": "pizzazz",
-  "quarrelled": "quarreled",
-  "quarrelling": "quarreling",
-  "radicalise": "radicalize",
-  "radicalised": "radicalized",
-  "radicalises": "radicalizes",
-  "radicalising": "radicalizing",
-  "rancour": "rancor",
-  "randomise": "randomize",
-  "randomised": "randomized",
-  "randomises": "randomizes",
-  "randomising": "randomizing",
-  "rationalisation": "rationalization",
-  "rationalisations": "rationalizations",
-  "rationalise": "rationalize",
-  "rationalised": "rationalized",
-  "rationalises": "rationalizes",
-  "rationalising": "rationalizing",
-  "ravelled": "raveled",
-  "ravelling": "raveling",
-  "realisable": "realizable",
-  "realisation": "realization",
-  "realisations": "realizations",
-  "realise": "realize",
-  "realised": "realized",
-  "realises": "realizes",
-  "realising": "realizing",
-  "recognisable": "recognizable",
-  "recognisably": "recognizably",
-  "recognisance": "recognizance",
-  "recognise": "recognize",
-  "recognised": "recognized",
-  "recognises": "recognizes",
-  "recognising": "recognizing",
-  "reconnoitre": "reconnoiter",
-  "reconnoitred": "reconnoitered",
-  "reconnoitres": "reconnoiters",
-  "reconnoitring": "reconnoitering",
-  "refuelled": "refueled",
-  "refuelling": "refueling",
-  "regularisation": "regularization",
-  "regularise": "regularize",
-  "regularised": "regularized",
-  "regularises": "regularizes",
-  "regularising": "regularizing",
-  "remodelled": "remodeled",
-  "remodelling": "remodeling",
-  "remould": "remold",
-  "remoulded": "remolded",
-  "remoulding": "remolding",
-  "remoulds": "remolds",
-  "reorganisation": "reorganization",
-  "reorganisations": "reorganizations",
-  "reorganise": "reorganize",
-  "reorganised": "reorganized",
-  "reorganises": "reorganizes",
-  "reorganising": "reorganizing",
-  "revelled": "reveled",
-  "reveller": "reveler",
-  "revellers": "revelers",
-  "revelling": "reveling",
-  "revitalise": "revitalize",
-  "revitalised": "revitalized",
-  "revitalises": "revitalizes",
-  "revitalising": "revitalizing",
-  "revolutionise": "revolutionize",
-  "revolutionised": "revolutionized",
-  "revolutionises": "revolutionizes",
-  "revolutionising": "revolutionizing",
-  "rhapsodise": "rhapsodize",
-  "rhapsodised": "rhapsodized",
-  "rhapsodises": "rhapsodizes",
-  "rhapsodising": "rhapsodizing",
-  "rigour": "rigor",
-  "rigours": "rigors",
-  "ritualised": "ritualized",
-  "rivalled": "rivaled",
-  "rivalling": "rivaling",
-  "romanticise": "romanticize",
-  "romanticised": "romanticized",
-  "romanticises": "romanticizes",
-  "romanticising": "romanticizing",
-  "rumour": "rumor",
-  "rumoured": "rumored",
-  "rumours": "rumors",
-  "sabre": "saber",
-  "sabres": "sabers",
-  "saltpetre": "saltpeter",
-  "sanitise": "sanitize",
-  "sanitised": "sanitized",
-  "sanitises": "sanitizes",
-  "sanitising": "sanitizing",
-  "satirise": "satirize",
-  "satirised": "satirized",
-  "satirises": "satirizes",
-  "satirising": "satirizing",
-  "saviour": "savior",
-  "saviours": "saviors",
-  "savour": "savor",
-  "savoured": "savored",
-  "savouries": "savories",
-  "savouring": "savoring",
-  "savours": "savors",
-  "savoury": "savory",
-  "scandalise": "scandalize",
-  "scandalised": "scandalized",
-  "scandalises": "scandalizes",
-  "scandalising": "scandalizing",
-  "sceptic": "skeptic",
-  "sceptical": "skeptical",
-  "sceptically": "skeptically",
-  "scepticism": "skepticism",
-  "sceptics": "skeptics",
-  "sceptre": "scepter",
-  "sceptres": "scepters",
-  "scrutinise": "scrutinize",
-  "scrutinised": "scrutinized",
-  "scrutinises": "scrutinizes",
-  "scrutinising": "scrutinizing",
-  "secularisation": "secularization",
-  "secularise": "secularize",
-  "secularised": "secularized",
-  "secularises": "secularizes",
-  "secularising": "secularizing",
-  "sensationalise": "sensationalize",
-  "sensationalised": "sensationalized",
-  "sensationalises": "sensationalizes",
-  "sensationalising": "sensationalizing",
-  "sensitise": "sensitize",
-  "sensitised": "sensitized",
-  "sensitises": "sensitizes",
-  "sensitising": "sensitizing",
-  "sentimentalise": "sentimentalize",
-  "sentimentalised": "sentimentalized",
-  "sentimentalises": "sentimentalizes",
-  "sentimentalising": "sentimentalizing",
-  "sepulchre": "sepulcher",
-  "sepulchres": "sepulchers",
-  "serialisation": "serialization",
-  "serialisations": "serializations",
-  "serialise": "serialize",
-  "serialised": "serialized",
-  "serialises": "serializes",
-  "serialising": "serializing",
-  "sermonise": "sermonize",
-  "sermonised": "sermonized",
-  "sermonises": "sermonizes",
-  "sermonising": "sermonizing",
-  "sheikh": "sheik",
-  "shovelled": "shoveled",
-  "shovelling": "shoveling",
-  "shrivelled": "shriveled",
-  "shrivelling": "shriveling",
-  "signalise": "signalize",
-  "signalised": "signalized",
-  "signalises": "signalizes",
-  "signalising": "signalizing",
-  "signalled": "signaled",
-  "signalling": "signaling",
-  "smoulder": "smolder",
-  "smouldered": "smoldered",
-  "smouldering": "smoldering",
-  "smoulders": "smolders",
-  "snivelled": "sniveled",
-  "snivelling": "sniveling",
-  "snorkelled": "snorkeled",
-  "snorkelling": "snorkeling",
-  "snowplough": "snowplow",
-  "snowploughs": "snowplow",
-  "socialisation": "socialization",
-  "socialise": "socialize",
-  "socialised": "socialized",
-  "socialises": "socializes",
-  "socialising": "socializing",
-  "sodomise": "sodomize",
-  "sodomised": "sodomized",
-  "sodomises": "sodomizes",
-  "sodomising": "sodomizing",
-  "solemnise": "solemnize",
-  "solemnised": "solemnized",
-  "solemnises": "solemnizes",
-  "solemnising": "solemnizing",
-  "sombre": "somber",
-  "specialisation": "specialization",
-  "specialisations": "specializations",
-  "specialise": "specialize",
-  "specialised": "specialized",
-  "specialises": "specializes",
-  "specialising": "specializing",
-  "spectre": "specter",
-  "spectres": "specters",
-  "spiralled": "spiraled",
-  "spiralling": "spiraling",
-  "splendour": "splendor",
-  "splendours": "splendors",
-  "squirrelled": "squirreled",
-  "squirrelling": "squirreling",
-  "stabilisation": "stabilization",
-  "stabilise": "stabilize",
-  "stabilised": "stabilized",
-  "stabiliser": "stabilizer",
-  "stabilisers": "stabilizers",
-  "stabilises": "stabilizes",
-  "stabilising": "stabilizing",
-  "standardisation": "standardization",
-  "standardise": "standardize",
-  "standardised": "standardized",
-  "standardises": "standardizes",
-  "standardising": "standardizing",
-  "stencilled": "stenciled",
-  "stencilling": "stenciling",
-  "sterilisation": "sterilization",
-  "sterilisations": "sterilizations",
-  "sterilise": "sterilize",
-  "sterilised": "sterilized",
-  "steriliser": "sterilizer",
-  "sterilisers": "sterilizers",
-  "sterilises": "sterilizes",
-  "sterilising": "sterilizing",
-  "stigmatisation": "stigmatization",
-  "stigmatise": "stigmatize",
-  "stigmatised": "stigmatized",
-  "stigmatises": "stigmatizes",
-  "stigmatising": "stigmatizing",
-  "storey": "story",
-  "storeys": "stories",
-  "subsidisation": "subsidization",
-  "subsidise": "subsidize",
-  "subsidised": "subsidized",
-  "subsidiser": "subsidizer",
-  "subsidisers": "subsidizers",
-  "subsidises": "subsidizes",
-  "subsidising": "subsidizing",
-  "succour": "succor",
-  "succoured": "succored",
-  "succouring": "succoring",
-  "succours": "succors",
-  "sulphate": "sulfate",
-  "sulphates": "sulfates",
-  "sulphide": "sulfide",
-  "sulphides": "sulfides",
-  "sulphur": "sulfur",
-  "sulphurous": "sulfurous",
-  "summarise": "summarize",
-  "summarised": "summarized",
-  "summarises": "summarizes",
-  "summarising": "summarizing",
-  "swivelled": "swiveled",
-  "swivelling": "swiveling",
-  "symbolise": "symbolize",
-  "symbolised": "symbolized",
-  "symbolises": "symbolizes",
-  "symbolising": "symbolizing",
-  "sympathise": "sympathize",
-  "sympathised": "sympathized",
-  "sympathiser": "sympathizer",
-  "sympathisers": "sympathizers",
-  "sympathises": "sympathizes",
-  "sympathising": "sympathizing",
-  "synchronisation": "synchronization",
-  "synchronise": "synchronize",
-  "synchronised": "synchronized",
-  "synchronises": "synchronizes",
-  "synchronising": "synchronizing",
-  "synthesise": "synthesize",
-  "synthesised": "synthesized",
-  "synthesiser": "synthesizer",
-  "synthesisers": "synthesizers",
-  "synthesises": "synthesizes",
-  "synthesising": "synthesizing",
-  "syphon": "siphon",
-  "syphoned": "siphoned",
-  "syphoning": "siphoning",
-  "syphons": "siphons",
-  "systematisation": "systematization",
-  "systematise": "systematize",
-  "systematised": "systematized",
-  "systematises": "systematizes",
-  "systematising": "systematizing",
-  "tantalise": "tantalize",
-  "tantalised": "tantalized",
-  "tantalises": "tantalizes",
-  "tantalising": "tantalizing",
-  "tantalisingly": "tantalizingly",
-  "tasselled": "tasseled",
-  "technicolour": "technicolor",
-  "temporise": "temporize",
-  "temporised": "temporized",
-  "temporises": "temporizes",
-  "temporising": "temporizing",
-  "tenderise": "tenderize",
-  "tenderised": "tenderized",
-  "tenderises": "tenderizes",
-  "tenderising": "tenderizing",
-  "terrorise": "terrorize",
-  "terrorised": "terrorized",
-  "terrorises": "terrorizes",
-  "terrorising": "terrorizing",
-  "theatre": "theater",
-  "theatregoer": "theatergoer",
-  "theatregoers": "theatergoers",
-  "theatres": "theaters",
-  "theorise": "theorize",
-  "theorised": "theorized",
-  "theorises": "theorizes",
-  "theorising": "theorizing",
-  "tonne": "ton",
-  "tonnes": "tons",
-  "towelled": "toweled",
-  "towelling": "toweling",
-  "toxaemia": "toxemia",
-  "tranquillise": "tranquilize",
-  "tranquillised": "tranquilized",
-  "tranquilliser": "tranquilizer",
-  "tranquillisers": "tranquilizers",
-  "tranquillises": "tranquilizes",
-  "tranquillising": "tranquilizing",
-  "tranquillity": "tranquility",
-  "tranquillize": "tranquilize",
-  "tranquillized": "tranquilized",
-  "tranquillizer": "tranquilizer",
-  "tranquillizers": "tranquilizers",
-  "tranquillizes": "tranquilizes",
-  "tranquillizing": "tranquilizing",
-  "tranquilly": "tranquility",
-  "transistorised": "transistorized",
-  "traumatise": "traumatize",
-  "traumatised": "traumatized",
-  "traumatises": "traumatizes",
-  "traumatising": "traumatizing",
-  "travelled": "traveled",
-  "traveller": "traveler",
-  "travellers": "travelers",
-  "travelling": "traveling",
-  "travelog": "travelogue",
-  "travelogs": "travelogues",
-  "trialled": "trialed",
-  "trialling": "trialing",
-  "tricolour": "tricolor",
-  "tricolours": "tricolors",
-  "trivialise": "trivialize",
-  "trivialised": "trivialized",
-  "trivialises": "trivializes",
-  "trivialising": "trivializing",
-  "tumour": "tumor",
-  "tumours": "tumors",
-  "tunnelled": "tunneled",
-  "tunnelling": "tunneling",
-  "tyrannise": "tyrannize",
-  "tyrannised": "tyrannized",
-  "tyrannises": "tyrannizes",
-  "tyrannising": "tyrannizing",
-  "tyre": "tire",
-  "tyres": "tires",
-  "unauthorised": "unauthorized",
-  "uncivilised": "uncivilized",
-  "underutilised": "underutilized",
-  "unequalled": "unequaled",
-  "unfavourable": "unfavorable",
-  "unfavourably": "unfavorably",
-  "unionisation": "unionization",
-  "unionise": "unionize",
-  "unionised": "unionized",
-  "unionises": "unionizes",
-  "unionising": "unionizing",
-  "unorganised": "unorganized",
-  "unravelled": "unraveled",
-  "unravelling": "unraveling",
-  "unrecognisable": "unrecognizable",
-  "unrecognised": "unrecognized",
-  "unrivalled": "unrivaled",
-  "unsavoury": "unsavory",
-  "untrammelled": "untrammeled",
-  "urbanisation": "urbanization",
-  "urbanise": "urbanize",
-  "urbanised": "urbanized",
-  "urbanises": "urbanizes",
-  "urbanising": "urbanizing",
-  "utilisable": "utilizable",
-  "utilisation": "utilization",
-  "utilise": "utilize",
-  "utilised": "utilized",
-  "utilises": "utilizes",
-  "utilising": "utilizing",
-  "valour": "valor",
-  "vandalise": "vandalize",
-  "vandalised": "vandalized",
-  "vandalises": "vandalizes",
-  "vandalising": "vandalizing",
-  "vaporisation": "vaporization",
-  "vaporise": "vaporize",
-  "vaporised": "vaporized",
-  "vaporises": "vaporizes",
-  "vaporising": "vaporizing",
-  "vapour": "vapor",
-  "vapours": "vapors",
-  "verbalise": "verbalize",
-  "verbalised": "verbalized",
-  "verbalises": "verbalizes",
-  "verbalising": "verbalizing",
-  "victimisation": "victimization",
-  "victimise": "victimize",
-  "victimised": "victimized",
-  "victimises": "victimizes",
-  "victimising": "victimizing",
-  "videodisc": "videodisk",
-  "videodiscs": "videodisks",
-  "vigour": "vigor",
-  "visualisation": "visualization",
-  "visualisations": "visualizations",
-  "visualise": "visualize",
-  "visualised": "visualized",
-  "visualises": "visualizes",
-  "visualising": "visualizing",
-  "vocalisation": "vocalization",
-  "vocalisations": "vocalizations",
-  "vocalise": "vocalize",
-  "vocalised": "vocalized",
-  "vocalises": "vocalizes",
-  "vocalising": "vocalizing",
-  "vulcanised": "vulcanized",
-  "vulgarisation": "vulgarization",
-  "vulgarise": "vulgarize",
-  "vulgarised": "vulgarized",
-  "vulgarises": "vulgarizes",
-  "vulgarising": "vulgarizing",
-  "waggon": "wagon",
-  "waggons": "wagons",
-  "watercolour": "watercolor",
-  "watercolours": "watercolors",
-  "weaselled": "weaseled",
-  "weaselling": "weaseling",
-  "westernisation": "westernization",
-  "westernise": "westernize",
-  "westernised": "westernized",
-  "westernises": "westernizes",
-  "westernising": "westernizing",
-  "womanise": "womanize",
-  "womanised": "womanized",
-  "womaniser": "womanizer",
-  "womanisers": "womanizers",
-  "womanises": "womanizes",
-  "womanising": "womanizing",
-  "woollen": "woolen",
-  "woollens": "woolens",
-  "woollies": "woolies",
-  "woolly": "wooly",
-  "worshipped": "worshiped",
-  "worshipper": "worshiper",
-  "worshipping": "worshiping",
-  "yodelled": "yodeled",
-  "yodelling": "yodeling",
-  "yoghourt": "yogurt",
-  "yoghourts": "yogurts",
-  "yoghurt": "yogurt",
-  "yoghurts": "yogurts"
-}

pretrained_models/whisper-small/special_tokens_map.json

@@ -1,133 +0,0 @@
-{
-  "additional_special_tokens": [
-    "<|endoftext|>",
-    "<|startoftranscript|>",
-    "<|en|>",
-    "<|zh|>",
-    "<|de|>",
-    "<|es|>",
-    "<|ru|>",
-    "<|ko|>",
-    "<|fr|>",
-    "<|ja|>",
-    "<|pt|>",
-    "<|tr|>",
-    "<|pl|>",
-    "<|ca|>",
-    "<|nl|>",
-    "<|ar|>",
-    "<|sv|>",
-    "<|it|>",
-    "<|id|>",
-    "<|hi|>",
-    "<|fi|>",
-    "<|vi|>",
-    "<|he|>",
-    "<|uk|>",
-    "<|el|>",
-    "<|ms|>",
-    "<|cs|>",
-    "<|ro|>",
-    "<|da|>",
-    "<|hu|>",
-    "<|ta|>",
-    "<|no|>",
-    "<|th|>",
-    "<|ur|>",
-    "<|hr|>",
-    "<|bg|>",
-    "<|lt|>",
-    "<|la|>",
-    "<|mi|>",
-    "<|ml|>",
-    "<|cy|>",
-    "<|sk|>",
-    "<|te|>",
-    "<|fa|>",
-    "<|lv|>",
-    "<|bn|>",
-    "<|sr|>",
-    "<|az|>",
-    "<|sl|>",
-    "<|kn|>",
-    "<|et|>",
-    "<|mk|>",
-    "<|br|>",
-    "<|eu|>",
-    "<|is|>",
-    "<|hy|>",
-    "<|ne|>",
-    "<|mn|>",
-    "<|bs|>",
-    "<|kk|>",
-    "<|sq|>",
-    "<|sw|>",
-    "<|gl|>",
-    "<|mr|>",
-    "<|pa|>",
-    "<|si|>",
-    "<|km|>",
-    "<|sn|>",
-    "<|yo|>",
-    "<|so|>",
-    "<|af|>",
-    "<|oc|>",
-    "<|ka|>",
-    "<|be|>",
-    "<|tg|>",
-    "<|sd|>",
-    "<|gu|>",
-    "<|am|>",
-    "<|yi|>",
-    "<|lo|>",
-    "<|uz|>",
-    "<|fo|>",
-    "<|ht|>",
-    "<|ps|>",
-    "<|tk|>",
-    "<|nn|>",
-    "<|mt|>",
-    "<|sa|>",
-    "<|lb|>",
-    "<|my|>",
-    "<|bo|>",
-    "<|tl|>",
-    "<|mg|>",
-    "<|as|>",
-    "<|tt|>",
-    "<|haw|>",
-    "<|ln|>",
-    "<|ha|>",
-    "<|ba|>",
-    "<|jw|>",
-    "<|su|>",
-    "<|translate|>",
-    "<|transcribe|>",
-    "<|startoflm|>",
-    "<|startofprev|>",
-    "<|nocaptions|>",
-    "<|notimestamps|>"
-  ],
-  "bos_token": {
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "eos_token": {
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "pad_token": "<|endoftext|>",
-  "unk_token": {
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  }
-}

pretrained_models/whisper-small/tokenizer_config.json

@@ -1,35 +0,0 @@
-{
-  "add_bos_token": false,
-  "add_prefix_space": false,
-  "bos_token": {
-    "__type": "AddedToken",
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "clean_up_tokenization_spaces": true,
-  "eos_token": {
-    "__type": "AddedToken",
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
-  "errors": "replace",
-  "model_max_length": 1024,
-  "pad_token": null,
-  "processor_class": "WhisperProcessor",
-  "return_attention_mask": false,
-  "tokenizer_class": "WhisperTokenizer",
-  "unk_token": {
-    "__type": "AddedToken",
-    "content": "<|endoftext|>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  }
-}