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Release NepTrans v4 with verified weights, model card, offline usage and evaluation provenance

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+ NepTrans v4, shared by DontHash, 2026.
2
+
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+ This release adapts openai/whisper-large-v3 at revision
4
+ 06f233fe06e710322aca913c1bc4249a0d71fce1. The pinned Hugging Face
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+ model card declares Apache-2.0. The original Whisper project is
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+ Copyright (c) 2022 OpenAI and distributes its code and weights under MIT;
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+ the original MIT notice is retained in WHISPER_LICENSE. Changes consist
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+ of NepTrans LoRA fine-tuning and, for the CT2 package, adapter merging
9
+ and CTranslate2 int8_float16 conversion. Upstream notices are retained.
10
+
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+ Model artifacts: Apache License 2.0 (LICENSE).
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+ Included helper/protocol code: MIT (CODE_LICENSE).
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+
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+ Training data attribution:
15
+ Large Nepali ASR training data set, OpenSLR-54.
16
+ Copyright 2016, 2017, 2018 Google, Inc.
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+ Source: https://www.openslr.org/54/
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+ License: Creative Commons Attribution-ShareAlike 4.0 International.
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+ The model used a curated approximately 15-hour subset of this corpus.
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+ The dataset license is preserved in OPENSLR54_LICENSE.
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+ See the model card for the requested Kjartansson et al. (2018) citation.
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+ No dataset audio, transcript, or private manifest is redistributed here.
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+
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+ Runtime libraries are separate dependencies with their own licenses:
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+ faster-whisper (MIT), CTranslate2 (MIT), Transformers (Apache-2.0),
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+ and PEFT (Apache-2.0). They are not vendored in these model repositories.
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README.md ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - ne
4
+ - en
5
+ license: apache-2.0
6
+ library_name: ctranslate2
7
+ pipeline_tag: automatic-speech-recognition
8
+ base_model: openai/whisper-large-v3
9
+ base_model_relation: finetune
10
+ tags:
11
+ - whisper
12
+ - nepali
13
+ - english
14
+ - code-switching
15
+ - automatic-speech-recognition
16
+ - lora
17
+ - faster-whisper
18
+ - ctranslate2
19
+ - int8
20
+ ---
21
+
22
+ # NepTrans v4 — Nepali–English Speech Recognition (CTranslate2)
23
+
24
+ **Run NepTrans on your own hardware with faster-whisper.** This is the complete, merged CTranslate2 export of the NepTrans v4 LoRA fine-tune, stored with `int8_float16` quantization. The main weights file is approximately **1.55 GB** (1.45 GiB). No separate base-model download, PEFT setup, or training is required.
25
+
26
+ | Release | Choose it when |
27
+ |---|---|
28
+ | **[CTranslate2 model](https://huggingface.co/DontHash/neptrans-v4-ct2)** — this repository | You want straightforward local transcription or to build a self-hosted application. |
29
+ | [LoRA adapter](https://huggingface.co/DontHash/neptrans-v4-lora) | You want the original adapter for Transformers/PEFT experiments or further adaptation. |
30
+
31
+ ## Quick start
32
+
33
+ Use Python 3.10 or newer in a virtual environment:
34
+
35
+ ```bash
36
+ python -m pip install "faster-whisper==1.2.1" "ctranslate2==4.8.1"
37
+ ```
38
+
39
+ ### NVIDIA GPU
40
+
41
+ Install the CUDA 12 and cuDNN 9 runtime dependencies required by CTranslate2; see the [faster-whisper GPU installation guide](https://github.com/SYSTRAN/faster-whisper#gpu). Installing the Python packages alone may not supply the required NVIDIA libraries.
42
+
43
+ ```python
44
+ from faster_whisper import WhisperModel
45
+
46
+ model = WhisperModel(
47
+ "DontHash/neptrans-v4-ct2",
48
+ revision="v4.0.0",
49
+ device="cuda",
50
+ compute_type="int8_float16",
51
+ )
52
+ segments, info = model.transcribe(
53
+ "recording.wav",
54
+ language=None, # automatic detection; try "ne" for Nepali-led recordings
55
+ task="transcribe",
56
+ beam_size=1,
57
+ condition_on_previous_text=False,
58
+ )
59
+ for segment in segments: # inference executes as the iterator is consumed
60
+ print(f"[{segment.start:.2f}s -> {segment.end:.2f}s] {segment.text}")
61
+ ```
62
+
63
+ ### CPU
64
+
65
+ Use the same code with `device="cpu", compute_type="int8"`. CTranslate2 converts the stored representation as needed. CPU inference needs sufficient system RAM and is generally slower; no universal RAM, latency, or real-time guarantee is made.
66
+
67
+ Release verification used Windows, an NVIDIA RTX 2050 with 4 GiB VRAM, GPU `int8_float16`, and CPU `int8`. This is a short-clip functional check, not a minimum-VRAM guarantee for all workloads. Batch size, beam size, recording length, concurrency, and other software affect resource use. The tested package versions are included in the release manifest.
68
+
69
+ ### Download once and run offline
70
+
71
+ ```bash
72
+ python -m pip install huggingface_hub
73
+ hf download DontHash/neptrans-v4-ct2 --revision v4.0.0 --local-dir ./neptrans-v4-ct2
74
+ ```
75
+
76
+ Then load the downloaded directory explicitly:
77
+
78
+ ```python
79
+ model = WhisperModel(
80
+ "./neptrans-v4-ct2",
81
+ device="cpu",
82
+ compute_type="int8",
83
+ local_files_only=True,
84
+ )
85
+ ```
86
+
87
+ Keep `model.bin`, `config.json`, `tokenizer.json`, `preprocessor_config.json`, and the accompanying vocabulary files together. The repository also includes a small transcription helper:
88
+
89
+ ```bash
90
+ python neptrans-v4-ct2/transcribe.py recording.wav --model ./neptrans-v4-ct2 --device cpu --local-files-only --output transcript.txt
91
+ ```
92
+
93
+ Add `--json-output segments.json` for segment timestamps, `--language ne` to specify Nepali, or `--device cuda --compute-type int8_float16` for NVIDIA GPU inference. With this standard faster-whisper call, automatic language detection uses the first 30 seconds; it is not word-level code-switch detection. Choosing a language can change script and recognition behavior. Start with these settings, then evaluate any decoding or VAD changes against your own recordings.
94
+
95
+ The model can be loaded inside your own API or application process. Network access, authentication, storage, and concurrency controls belong to the application you host; this release creates no paid endpoint or hosted inference service.
96
+
97
+ ## Model overview
98
+
99
+ NepTrans v4 is a Nepali–English automatic speech recognition model, fine-tuned from **OpenAI Whisper large-v3** using expanded LoRA adaptation. It is intended for Nepali speech and conversations that switch between Nepali and English, including draft notes, interview transcripts, and subtitles that a person reviews before use.
100
+
101
+ The model is shared by **[DontHash](https://huggingface.co/DontHash)**. Download it once and run inference on your own computer or server. Audio processed locally stays within your chosen runtime; this model does not call a NepTrans transcription service. The initial model download requires internet access unless the files have already been transferred locally.
102
+
103
+ | Property | Value |
104
+ |---|---|
105
+ | Task | Automatic speech recognition / transcription |
106
+ | Target languages | Nepali (`ne`) and English (`en`), including code-switching |
107
+ | Architecture | Whisper large-v3 encoder–decoder, approximately 1.55B base parameters |
108
+ | Base checkpoint | `openai/whisper-large-v3` |
109
+ | Base revision | `06f233fe06e710322aca913c1bc4249a0d71fce1` |
110
+ | Fine-tuning method | LoRA, rank 16, alpha 32, dropout 0.05 |
111
+ | LoRA modules | `q_proj`, `k_proj`, `v_proj`, `out_proj`, `fc1`, `fc2` |
112
+ | Audio features | 16 kHz audio, 128 mel bins |
113
+ | Release | `v4.0.0`, 20 October 2026 |
114
+
115
+ ## Training data and method
116
+
117
+ The v4 training manifest contains **21,470 clips / 49.094 hours**:
118
+
119
+ | Training component | Clips | Hours |
120
+ |---|---:|---:|
121
+ | Resegmented conversational Nepali and Nepali–English speech | 8,075 | approximately 34.09 |
122
+ | Curated OpenSLR-54 Nepali read speech | 13,395 | approximately 15.00 |
123
+ | Total | 21,470 | 49.094 |
124
+
125
+ The training manifest has 14,473 Nepali-only, 6,613 mixed-language, and 384 English-only clips. Development uses 652 clips / 2.626 hours; the internal formal test uses 625 clips / 2.587 hours. These counts and durations were recomputed from the saved manifests when preparing this release.
126
+
127
+ The training recipe specifies three epochs, learning rate `5e-5`, batch size 1, gradient accumulation 16, BF16 mixed precision, linear learning-rate scheduling, 50 warmup steps, AdamW, and seed 42. The saved final trainer state records three completed epochs and **4,026 optimizer steps**, selecting checkpoint 4,026 using the lowest development target-weighted WER. The recipe and saved state are distinct sources: intermediate evaluation and save intervals changed during the run. The reported training runtime is approximately 7.89 hours; carbon emissions were not measured.
128
+
129
+ Training audio, transcripts, per-recording predictions, and private manifests are **not distributed** in these repositories. OpenSLR-54 is credited below. The conversational component is a project-curated corpus; this release does not claim that every label was independently human-verified or that the corpus represents all Nepali speakers, accents, or recording conditions. The full training dataset is not publicly downloadable from this model page.
130
+
131
+ ## Evaluation
132
+
133
+ The following are **saved evaluation results from the v4 Transformers/PEFT training run**, not a new evaluation of the CTranslate2 export. Lower values are better.
134
+
135
+ | Split | Clips | WER (%) | CER (%) | Custom target-weighted WER (%) |
136
+ |---|---:|---:|---:|---:|
137
+ | Internal development, checkpoint 4,026 | 652 | 28.17 | 11.16 | 31.72 |
138
+ | Internal formal test | 625 | **36.47** | **19.24** | 44.15 |
139
+
140
+ WER is the corpus word error rate, `(substitutions + deletions + insertions) / reference words`; CER uses characters. The custom selection score is `0.60 × mixed WER + 0.20 × Nepali-only WER + 0.20 × English-only WER`. It is a workload-priority score and must not be interpreted as pooled WER or word accuracy.
141
+
142
+ The formal test composition is:
143
+
144
+ | Bucket | Clips | Reference words | Share of reference words |
145
+ |---|---:|---:|---:|
146
+ | Mixed Nepali–English | 360 | 17,137 | 62.56% |
147
+ | Nepali-only | 75 | 1,141 | 4.17% |
148
+ | English-only | 190 | 9,117 | 33.28% |
149
+
150
+ The saved evaluator uses `jiwer.wer` and `jiwer.cer` directly on reference and decoded text, without an additional custom punctuation, case, or spelling normalization pass. Generation uses automatic language selection, the transcription task, greedy decoding (`num_beams=1`), maximum sequence length 448, and no timestamp tokens. The included `evaluation/protocol.py` preserves the relevant scoring and generation logic without publishing any recordings or transcripts.
151
+
152
+ **Interpretation and evidence limits:**
153
+
154
+ - These are internal project splits, **not FLEURS, Common Voice, or public leaderboard results**. No state-of-the-art ranking is claimed.
155
+ - Split composition is uneven. Overall WER alone does not establish Nepali-only or mixed-language quality; per-bucket v4 error rates were not present in the saved aggregate evaluation files.
156
+ - The full evaluation was not repeated during packaging. The CT2 `int8_float16` export has separate functional smoke tests; its complete test-set WER/CER have not been established.
157
+ - The formal evaluation's adapter-file hash was not recorded in its metrics JSON. The release links the exported adapter and CT2 weights through saved export provenance and verified hashes; the metric-to-file linkage is at the recorded training-run level, not an independently reproduced benchmark.
158
+ - No independent source/speaker leakage audit, confidence intervals, or broad accent-fairness evaluation is asserted by this release.
159
+ - Runtime numbers from the training/evaluation environment do not establish streaming latency or performance on a recipient's machine.
160
+
161
+ Machine-readable copies of the saved metrics, manifest aggregates, and checkpoint selection are in [`evaluation/summary.json`](evaluation/summary.json).
162
+
163
+ ## Intended uses and limitations
164
+
165
+ Use the model for personal or research transcription, draft subtitles, and local applications with human correction. Evaluate it on representative recordings before relying on it in a workflow.
166
+
167
+ Expected failure modes include incorrect names, numbers, English technical words, code-switch boundaries, punctuation, and script choice. Short recordings can trigger wrong language detection. Whisper-family models can repeat or hallucinate text over silence, music, noise, or unclear speech. Overlapping speakers and distant microphones may reduce quality.
168
+
169
+ This is a transcription model: it does not provide speaker identity, diarization, source separation, or verified factual understanding. Timestamp output from an inference library is an alignment estimate, not manually confirmed timing. Neither translation quality nor retention of all other Whisper languages has been evaluated for this fine-tune.
170
+
171
+ Review transcripts before publication or consequential use. The reported error rates do not support unattended high-stakes transcription. Users are responsible for obtaining permission to process recordings and for securing any application they host.
172
+
173
+ ## Provenance, licenses, and attribution
174
+
175
+ The released model artifacts use **Apache-2.0**, following the pinned Hugging Face base checkpoint's declared license. See [`LICENSE`](LICENSE). The included helper code is MIT-licensed; see [`CODE_LICENSE`](CODE_LICENSE). Upstream Whisper's original MIT notice is preserved in [`WHISPER_LICENSE`](WHISPER_LICENSE), and the third-party sources are documented in [`NOTICE`](NOTICE).
176
+
177
+ The OpenSLR-54 training component is **Large Nepali ASR training data set**, Copyright 2016, 2017, 2018 Google, Inc., distributed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/). This fine-tune used a curated approximately 15-hour subset. Original dataset: [OpenSLR-54](https://www.openslr.org/54/). Its source license is preserved in [`OPENSLR54_LICENSE`](OPENSLR54_LICENSE); dataset licenses remain separate from the model and helper-code licenses. No training audio or text is included in this release.
178
+
179
+ [`release_manifest.json`](release_manifest.json) records SHA-256 checksums for the distributed files, the upstream revision, quantization where applicable, and package versions used for release verification. Export provenance excludes local account paths and private source locations.
180
+
181
+ ### Citation
182
+
183
+ ```bibtex
184
+ @misc{donthash2026neptransv4,
185
+ author = {DontHash},
186
+ title = {NepTrans v4: Whisper Large-v3 Adaptation for Nepali-English Transcription},
187
+ year = {2026},
188
+ url = {https://huggingface.co/DontHash/neptrans-v4-ct2},
189
+ note = {Version v4.0.0; companion LoRA adapter: DontHash/neptrans-v4-lora}
190
+ }
191
+
192
+ @inproceedings{kjartansson-etal-sltu2018,
193
+ title = {Crowd-Sourced Speech Corpora for Javanese, Sundanese, Sinhala, Nepali, and Bangladeshi Bengali},
194
+ author = {Kjartansson, Oddur and Sarin, Supheakmungkol and Pipatsrisawat, Knot and Jansche, Martin and Ha, Linne},
195
+ booktitle = {Proc. The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU)},
196
+ year = {2018},
197
+ pages = {52--55},
198
+ doi = {10.21437/SLTU.2018-11}
199
+ }
200
+ ```
201
+
202
+ For Whisper, cite [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356), Radford et al. For the inference runtime, see [faster-whisper](https://github.com/SYSTRAN/faster-whisper) and [CTranslate2](https://github.com/OpenNMT/CTranslate2). Report issues through this repository's Hugging Face Community tab, including runtime versions and a reproducible example you have permission to share.
WHISPER_LICENSE ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ MIT License
2
+
3
+ Copyright (c) 2022 OpenAI
4
+
5
+ Permission is hereby granted, free of charge, to any person obtaining a copy
6
+ of this software and associated documentation files (the "Software"), to deal
7
+ in the Software without restriction, including without limitation the rights
8
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9
+ copies of the Software, and to permit persons to whom the Software is
10
+ furnished to do so, subject to the following conditions:
11
+
12
+ The above copyright notice and this permission notice shall be included in all
13
+ copies or substantial portions of the Software.
14
+
15
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21
+ SOFTWARE.
added_tokens.json ADDED
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config.json ADDED
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+ }
evaluation/protocol.py ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # SPDX-License-Identifier: MIT
2
+ """Verbatim scoring function and reference generation snippet from the saved v4 recipe.
3
+
4
+ This documents the protocol; it does not bundle a benchmark dataset.
5
+ """
6
+ from jiwer import wer, cer
7
+
8
+ def score_predictions(references: list[dict], predictions: list[dict]) -> dict:
9
+ ref_by_id = {r["item_id"]: r for r in references}
10
+ by_id = {p["item_id"]: p["hypothesis"] for p in predictions}
11
+ metrics = {}
12
+
13
+ for bucket in ["mixed", "nepali", "english"]:
14
+ rows = [r for r in references if r.get("language_bucket") == bucket]
15
+ if not rows:
16
+ continue
17
+ refs = [r["text"] for r in rows]
18
+ hyps = [by_id.get(r["item_id"], "") for r in rows]
19
+
20
+ w = wer(refs, hyps) * 100.0
21
+ c = cer(refs, hyps) * 100.0
22
+ metrics[bucket] = {"wer": round(w, 2), "cer": round(c, 2), "clips": len(rows)}
23
+
24
+ all_refs = [r["text"] for r in references]
25
+ all_hyps = [by_id.get(r["item_id"], "") for r in references]
26
+ overall_w = wer(all_refs, all_hyps) * 100.0
27
+ overall_c = cer(all_refs, all_hyps) * 100.0
28
+
29
+ metrics["overall"] = {"wer": round(overall_w, 2), "cer": round(overall_c, 2), "clips": len(references)}
30
+
31
+ # Target weighted WER (60% mixed, 20% nepali, 20% english if present, else overall)
32
+ if "mixed" in metrics and "nepali" in metrics:
33
+ mix_w = metrics["mixed"]["wer"]
34
+ nep_w = metrics["nepali"]["wer"]
35
+ en_w = metrics.get("english", {}).get("wer", mix_w)
36
+ metrics["target_weighted_wer"] = round(0.6 * mix_w + 0.2 * nep_w + 0.2 * en_w, 2)
37
+ else:
38
+ metrics["target_weighted_wer"] = round(overall_w, 2)
39
+
40
+ return metrics
41
+
42
+
43
+ REFERENCE_GENERATION_SNIPPET = ' tokens = base.generate(\n input_features=inputs["input_features"],\n attention_mask=inputs.get("attention_mask"),\n language=None,\n task="transcribe",\n return_timestamps=False,\n max_length=self.args.generation_max_length,\n num_beams=1,\n use_cache=True,\n )\n'
evaluation/summary.json ADDED
@@ -0,0 +1,100 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "results_origin": "Saved v4 Transformers/PEFT training-run artifacts; not rerun during packaging",
3
+ "ct2_full_test_evaluation": "not performed",
4
+ "formal_test": {
5
+ "eval_loss": 0.40005552768707275,
6
+ "eval_model_preparation_time": 0.0482,
7
+ "eval_wer": 36.47,
8
+ "eval_cer": 19.24,
9
+ "eval_target_weighted_wer": 44.15,
10
+ "eval_runtime": 4492.7362,
11
+ "eval_samples_per_second": 0.139,
12
+ "eval_steps_per_second": 0.139,
13
+ "epoch": 0
14
+ },
15
+ "development_at_selected_checkpoint": {
16
+ "epoch": 3.0,
17
+ "eval_cer": 11.16,
18
+ "eval_loss": 0.258039653301239,
19
+ "eval_runtime": 6200.8067,
20
+ "eval_samples_per_second": 0.105,
21
+ "eval_steps_per_second": 0.105,
22
+ "eval_target_weighted_wer": 31.72,
23
+ "eval_wer": 28.17,
24
+ "step": 4026
25
+ },
26
+ "checkpoint_selection": {
27
+ "step": 4026,
28
+ "metric": "development target_weighted_wer",
29
+ "value_percent": 31.72,
30
+ "completed_epochs": 3.0
31
+ },
32
+ "training_runtime": {
33
+ "train_runtime": 28402.3288,
34
+ "train_samples_per_second": 0.86,
35
+ "train_steps_per_second": 0.054,
36
+ "total_flos": 2.231121230364672e+20,
37
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1
+ {
2
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3
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4
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5
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6
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7
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8
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9
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10
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13
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15
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16
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22
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23
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24
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25
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26
+ "agonise": "agonize",
27
+ "agonised": "agonized",
28
+ "agonises": "agonizes",
29
+ "agonising": "agonizing",
30
+ "agonisingly": "agonizingly",
31
+ "almanack": "almanac",
32
+ "almanacks": "almanacs",
33
+ "aluminium": "aluminum",
34
+ "amortisable": "amortizable",
35
+ "amortisation": "amortization",
36
+ "amortisations": "amortizations",
37
+ "amortise": "amortize",
38
+ "amortised": "amortized",
39
+ "amortises": "amortizes",
40
+ "amortising": "amortizing",
41
+ "amphitheatre": "amphitheater",
42
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43
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44
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45
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47
+ "anaesthetics": "anesthetics",
48
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49
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50
+ "anaesthetises": "anesthetizes",
51
+ "anaesthetising": "anesthetizing",
52
+ "anaesthetist": "anesthetist",
53
+ "anaesthetists": "anesthetists",
54
+ "anaesthetize": "anesthetize",
55
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56
+ "anaesthetizes": "anesthetizes",
57
+ "anaesthetizing": "anesthetizing",
58
+ "analogue": "analog",
59
+ "analogues": "analogs",
60
+ "analyse": "analyze",
61
+ "analysed": "analyzed",
62
+ "analyses": "analyzes",
63
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64
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65
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66
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67
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68
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69
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70
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71
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72
+ "antagonising": "antagonizing",
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75
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76
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79
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80
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82
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83
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85
+ "archaeologically": "archeologically",
86
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87
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88
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89
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90
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91
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93
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95
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102
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104
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106
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107
+ "bannisters": "banisters",
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+ "baptised": "baptized",
110
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111
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112
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115
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129
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+ "behoved": "behooved",
131
+ "behoves": "behooves",
132
+ "bejewelled": "bejeweled",
133
+ "belabour": "belabor",
134
+ "belaboured": "belabored",
135
+ "belabouring": "belaboring",
136
+ "belabours": "belabors",
137
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138
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139
+ "bevvy": "bevy",
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141
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+ "bingeing": "binging",
143
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144
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146
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147
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148
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+ "breathalyse": "breathalyze",
150
+ "breathalysed": "breathalyzed",
151
+ "breathalyser": "breathalyzer",
152
+ "breathalysers": "breathalyzers",
153
+ "breathalyses": "breathalyzes",
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+ "breathalysing": "breathalyzing",
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+ "brutalise": "brutalize",
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157
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158
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159
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160
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161
+ "caesarean": "cesarean",
162
+ "caesareans": "cesareans",
163
+ "calibre": "caliber",
164
+ "calibres": "calibers",
165
+ "calliper": "caliper",
166
+ "callipers": "calipers",
167
+ "callisthenics": "calisthenics",
168
+ "canalise": "canalize",
169
+ "canalised": "canalized",
170
+ "canalises": "canalizes",
171
+ "canalising": "canalizing",
172
+ "cancelation": "cancellation",
173
+ "cancelations": "cancellations",
174
+ "cancelled": "canceled",
175
+ "cancelling": "canceling",
176
+ "candour": "candor",
177
+ "cannibalise": "cannibalize",
178
+ "cannibalised": "cannibalized",
179
+ "cannibalises": "cannibalizes",
180
+ "cannibalising": "cannibalizing",
181
+ "canonise": "canonize",
182
+ "canonised": "canonized",
183
+ "canonises": "canonizes",
184
+ "canonising": "canonizing",
185
+ "capitalise": "capitalize",
186
+ "capitalised": "capitalized",
187
+ "capitalises": "capitalizes",
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+ "capitalising": "capitalizing",
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+ "caramelise": "caramelize",
190
+ "caramelised": "caramelized",
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+ "caramelises": "caramelizes",
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+ "caramelising": "caramelizing",
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+ "carbonise": "carbonize",
194
+ "carbonised": "carbonized",
195
+ "carbonises": "carbonizes",
196
+ "carbonising": "carbonizing",
197
+ "carolled": "caroled",
198
+ "carolling": "caroling",
199
+ "catalogue": "catalog",
200
+ "catalogued": "cataloged",
201
+ "catalogues": "catalogs",
202
+ "cataloguing": "cataloging",
203
+ "catalyse": "catalyze",
204
+ "catalysed": "catalyzed",
205
+ "catalyses": "catalyzes",
206
+ "catalysing": "catalyzing",
207
+ "categorise": "categorize",
208
+ "categorised": "categorized",
209
+ "categorises": "categorizes",
210
+ "categorising": "categorizing",
211
+ "cauterise": "cauterize",
212
+ "cauterised": "cauterized",
213
+ "cauterises": "cauterizes",
214
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215
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216
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218
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+ "centilitre": "centiliter",
220
+ "centilitres": "centiliters",
221
+ "centimetre": "centimeter",
222
+ "centimetres": "centimeters",
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224
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+ "centralises": "centralizes",
226
+ "centralising": "centralizing",
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+ "centre": "center",
228
+ "centred": "centered",
229
+ "centrefold": "centerfold",
230
+ "centrefolds": "centerfolds",
231
+ "centrepiece": "centerpiece",
232
+ "centrepieces": "centerpieces",
233
+ "centres": "centers",
234
+ "channelled": "channeled",
235
+ "channelling": "channeling",
236
+ "characterise": "characterize",
237
+ "characterised": "characterized",
238
+ "characterises": "characterizes",
239
+ "characterising": "characterizing",
240
+ "cheque": "check",
241
+ "chequebook": "checkbook",
242
+ "chequebooks": "checkbooks",
243
+ "chequered": "checkered",
244
+ "cheques": "checks",
245
+ "chilli": "chili",
246
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247
+ "chimaeras": "chimeras",
248
+ "chiselled": "chiseled",
249
+ "chiselling": "chiseling",
250
+ "circularise": "circularize",
251
+ "circularised": "circularized",
252
+ "circularises": "circularizes",
253
+ "circularising": "circularizing",
254
+ "civilise": "civilize",
255
+ "civilised": "civilized",
256
+ "civilises": "civilizes",
257
+ "civilising": "civilizing",
258
+ "clamour": "clamor",
259
+ "clamoured": "clamored",
260
+ "clamouring": "clamoring",
261
+ "clamours": "clamors",
262
+ "clangour": "clangor",
263
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264
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265
+ "collectivise": "collectivize",
266
+ "collectivised": "collectivized",
267
+ "collectivises": "collectivizes",
268
+ "collectivising": "collectivizing",
269
+ "colonisation": "colonization",
270
+ "colonise": "colonize",
271
+ "colonised": "colonized",
272
+ "coloniser": "colonizer",
273
+ "colonisers": "colonizers",
274
+ "colonises": "colonizes",
275
+ "colonising": "colonizing",
276
+ "colour": "color",
277
+ "colourant": "colorant",
278
+ "colourants": "colorants",
279
+ "coloured": "colored",
280
+ "coloureds": "coloreds",
281
+ "colourful": "colorful",
282
+ "colourfully": "colorfully",
283
+ "colouring": "coloring",
284
+ "colourize": "colorize",
285
+ "colourized": "colorized",
286
+ "colourizes": "colorizes",
287
+ "colourizing": "colorizing",
288
+ "colourless": "colorless",
289
+ "colours": "colors",
290
+ "commercialise": "commercialize",
291
+ "commercialised": "commercialized",
292
+ "commercialises": "commercializes",
293
+ "commercialising": "commercializing",
294
+ "compartmentalise": "compartmentalize",
295
+ "compartmentalised": "compartmentalized",
296
+ "compartmentalises": "compartmentalizes",
297
+ "compartmentalising": "compartmentalizing",
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+ "computerise": "computerize",
299
+ "computerised": "computerized",
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+ "computerises": "computerizes",
301
+ "computerising": "computerizing",
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+ "conceptualise": "conceptualize",
303
+ "conceptualised": "conceptualized",
304
+ "conceptualises": "conceptualizes",
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+ "conceptualising": "conceptualizing",
306
+ "connexion": "connection",
307
+ "connexions": "connections",
308
+ "contextualise": "contextualize",
309
+ "contextualised": "contextualized",
310
+ "contextualises": "contextualizes",
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+ "contextualising": "contextualizing",
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+ "cosier": "cozier",
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+ "cosies": "cozies",
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+ "cosiest": "coziest",
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+ "cosily": "cozily",
316
+ "cosiness": "coziness",
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+ "cosy": "cozy",
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+ "councillor": "councilor",
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+ "councillors": "councilors",
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+ "counselled": "counseled",
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+ "counsellor": "counselor",
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+ "criminalise": "criminalize",
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+ "criticising": "criticizing",
333
+ "crueller": "crueler",
334
+ "cruellest": "cruelest",
335
+ "crystallisation": "crystallization",
336
+ "crystallise": "crystallize",
337
+ "crystallised": "crystallized",
338
+ "crystallises": "crystallizes",
339
+ "crystallising": "crystallizing",
340
+ "cudgelled": "cudgeled",
341
+ "cudgelling": "cudgeling",
342
+ "customise": "customize",
343
+ "customised": "customized",
344
+ "customises": "customizes",
345
+ "customising": "customizing",
346
+ "cypher": "cipher",
347
+ "cyphers": "ciphers",
348
+ "decentralisation": "decentralization",
349
+ "decentralise": "decentralize",
350
+ "decentralised": "decentralized",
351
+ "decentralises": "decentralizes",
352
+ "decentralising": "decentralizing",
353
+ "decriminalisation": "decriminalization",
354
+ "decriminalise": "decriminalize",
355
+ "decriminalised": "decriminalized",
356
+ "decriminalises": "decriminalizes",
357
+ "decriminalising": "decriminalizing",
358
+ "defence": "defense",
359
+ "defenceless": "defenseless",
360
+ "defences": "defenses",
361
+ "dehumanisation": "dehumanization",
362
+ "dehumanise": "dehumanize",
363
+ "dehumanised": "dehumanized",
364
+ "dehumanises": "dehumanizes",
365
+ "dehumanising": "dehumanizing",
366
+ "demeanour": "demeanor",
367
+ "demilitarisation": "demilitarization",
368
+ "demilitarise": "demilitarize",
369
+ "demilitarised": "demilitarized",
370
+ "demilitarises": "demilitarizes",
371
+ "demilitarising": "demilitarizing",
372
+ "demobilisation": "demobilization",
373
+ "demobilise": "demobilize",
374
+ "demobilised": "demobilized",
375
+ "demobilises": "demobilizes",
376
+ "demobilising": "demobilizing",
377
+ "democratisation": "democratization",
378
+ "democratise": "democratize",
379
+ "democratised": "democratized",
380
+ "democratises": "democratizes",
381
+ "democratising": "democratizing",
382
+ "demonise": "demonize",
383
+ "demonised": "demonized",
384
+ "demonises": "demonizes",
385
+ "demonising": "demonizing",
386
+ "demoralisation": "demoralization",
387
+ "demoralise": "demoralize",
388
+ "demoralised": "demoralized",
389
+ "demoralises": "demoralizes",
390
+ "demoralising": "demoralizing",
391
+ "denationalisation": "denationalization",
392
+ "denationalise": "denationalize",
393
+ "denationalised": "denationalized",
394
+ "denationalises": "denationalizes",
395
+ "denationalising": "denationalizing",
396
+ "deodorise": "deodorize",
397
+ "deodorised": "deodorized",
398
+ "deodorises": "deodorizes",
399
+ "deodorising": "deodorizing",
400
+ "depersonalise": "depersonalize",
401
+ "depersonalised": "depersonalized",
402
+ "depersonalises": "depersonalizes",
403
+ "depersonalising": "depersonalizing",
404
+ "deputise": "deputize",
405
+ "deputised": "deputized",
406
+ "deputises": "deputizes",
407
+ "deputising": "deputizing",
408
+ "desensitisation": "desensitization",
409
+ "desensitise": "desensitize",
410
+ "desensitised": "desensitized",
411
+ "desensitises": "desensitizes",
412
+ "desensitising": "desensitizing",
413
+ "destabilisation": "destabilization",
414
+ "destabilise": "destabilize",
415
+ "destabilised": "destabilized",
416
+ "destabilises": "destabilizes",
417
+ "destabilising": "destabilizing",
418
+ "dialled": "dialed",
419
+ "dialling": "dialing",
420
+ "dialogue": "dialog",
421
+ "dialogues": "dialogs",
422
+ "diarrhoea": "diarrhea",
423
+ "digitise": "digitize",
424
+ "digitised": "digitized",
425
+ "digitises": "digitizes",
426
+ "digitising": "digitizing",
427
+ "disc": "disk",
428
+ "discolour": "discolor",
429
+ "discoloured": "discolored",
430
+ "discolouring": "discoloring",
431
+ "discolours": "discolors",
432
+ "discs": "disks",
433
+ "disembowelled": "disemboweled",
434
+ "disembowelling": "disemboweling",
435
+ "disfavour": "disfavor",
436
+ "dishevelled": "disheveled",
437
+ "dishonour": "dishonor",
438
+ "dishonourable": "dishonorable",
439
+ "dishonourably": "dishonorably",
440
+ "dishonoured": "dishonored",
441
+ "dishonouring": "dishonoring",
442
+ "dishonours": "dishonors",
443
+ "disorganisation": "disorganization",
444
+ "disorganised": "disorganized",
445
+ "distil": "distill",
446
+ "distils": "distills",
447
+ "dramatisation": "dramatization",
448
+ "dramatisations": "dramatizations",
449
+ "dramatise": "dramatize",
450
+ "dramatised": "dramatized",
451
+ "dramatises": "dramatizes",
452
+ "dramatising": "dramatizing",
453
+ "draught": "draft",
454
+ "draughtboard": "draftboard",
455
+ "draughtboards": "draftboards",
456
+ "draughtier": "draftier",
457
+ "draughtiest": "draftiest",
458
+ "draughts": "drafts",
459
+ "draughtsman": "draftsman",
460
+ "draughtsmanship": "draftsmanship",
461
+ "draughtsmen": "draftsmen",
462
+ "draughtswoman": "draftswoman",
463
+ "draughtswomen": "draftswomen",
464
+ "draughty": "drafty",
465
+ "drivelled": "driveled",
466
+ "drivelling": "driveling",
467
+ "duelled": "dueled",
468
+ "duelling": "dueling",
469
+ "economise": "economize",
470
+ "economised": "economized",
471
+ "economises": "economizes",
472
+ "economising": "economizing",
473
+ "editorialise": "editorialize",
474
+ "editorialised": "editorialized",
475
+ "editorialises": "editorializes",
476
+ "editorialising": "editorializing",
477
+ "edoema": "edema",
478
+ "empathise": "empathize",
479
+ "empathised": "empathized",
480
+ "empathises": "empathizes",
481
+ "empathising": "empathizing",
482
+ "emphasise": "emphasize",
483
+ "emphasised": "emphasized",
484
+ "emphasises": "emphasizes",
485
+ "emphasising": "emphasizing",
486
+ "enamelled": "enameled",
487
+ "enamelling": "enameling",
488
+ "enamoured": "enamored",
489
+ "encyclopaedia": "encyclopedia",
490
+ "encyclopaedias": "encyclopedias",
491
+ "encyclopaedic": "encyclopedic",
492
+ "endeavour": "endeavor",
493
+ "endeavoured": "endeavored",
494
+ "endeavouring": "endeavoring",
495
+ "endeavours": "endeavors",
496
+ "energise": "energize",
497
+ "energised": "energized",
498
+ "energises": "energizes",
499
+ "energising": "energizing",
500
+ "enrol": "enroll",
501
+ "enrols": "enrolls",
502
+ "enthral": "enthrall",
503
+ "enthrals": "enthralls",
504
+ "epaulette": "epaulet",
505
+ "epaulettes": "epaulets",
506
+ "epicentre": "epicenter",
507
+ "epicentres": "epicenters",
508
+ "epilogue": "epilog",
509
+ "epilogues": "epilogs",
510
+ "epitomise": "epitomize",
511
+ "epitomised": "epitomized",
512
+ "epitomises": "epitomizes",
513
+ "epitomising": "epitomizing",
514
+ "equalisation": "equalization",
515
+ "equalise": "equalize",
516
+ "equalised": "equalized",
517
+ "equaliser": "equalizer",
518
+ "equalisers": "equalizers",
519
+ "equalises": "equalizes",
520
+ "equalising": "equalizing",
521
+ "eulogise": "eulogize",
522
+ "eulogised": "eulogized",
523
+ "eulogises": "eulogizes",
524
+ "eulogising": "eulogizing",
525
+ "evangelise": "evangelize",
526
+ "evangelised": "evangelized",
527
+ "evangelises": "evangelizes",
528
+ "evangelising": "evangelizing",
529
+ "exorcise": "exorcize",
530
+ "exorcised": "exorcized",
531
+ "exorcises": "exorcizes",
532
+ "exorcising": "exorcizing",
533
+ "extemporisation": "extemporization",
534
+ "extemporise": "extemporize",
535
+ "extemporised": "extemporized",
536
+ "extemporises": "extemporizes",
537
+ "extemporising": "extemporizing",
538
+ "externalisation": "externalization",
539
+ "externalisations": "externalizations",
540
+ "externalise": "externalize",
541
+ "externalised": "externalized",
542
+ "externalises": "externalizes",
543
+ "externalising": "externalizing",
544
+ "factorise": "factorize",
545
+ "factorised": "factorized",
546
+ "factorises": "factorizes",
547
+ "factorising": "factorizing",
548
+ "faecal": "fecal",
549
+ "faeces": "feces",
550
+ "familiarisation": "familiarization",
551
+ "familiarise": "familiarize",
552
+ "familiarised": "familiarized",
553
+ "familiarises": "familiarizes",
554
+ "familiarising": "familiarizing",
555
+ "fantasise": "fantasize",
556
+ "fantasised": "fantasized",
557
+ "fantasises": "fantasizes",
558
+ "fantasising": "fantasizing",
559
+ "favour": "favor",
560
+ "favourable": "favorable",
561
+ "favourably": "favorably",
562
+ "favoured": "favored",
563
+ "favouring": "favoring",
564
+ "favourite": "favorite",
565
+ "favourites": "favorites",
566
+ "favouritism": "favoritism",
567
+ "favours": "favors",
568
+ "feminise": "feminize",
569
+ "feminised": "feminized",
570
+ "feminises": "feminizes",
571
+ "feminising": "feminizing",
572
+ "fertilisation": "fertilization",
573
+ "fertilise": "fertilize",
574
+ "fertilised": "fertilized",
575
+ "fertiliser": "fertilizer",
576
+ "fertilisers": "fertilizers",
577
+ "fertilises": "fertilizes",
578
+ "fertilising": "fertilizing",
579
+ "fervour": "fervor",
580
+ "fibre": "fiber",
581
+ "fibreglass": "fiberglass",
582
+ "fibres": "fibers",
583
+ "fictionalisation": "fictionalization",
584
+ "fictionalisations": "fictionalizations",
585
+ "fictionalise": "fictionalize",
586
+ "fictionalised": "fictionalized",
587
+ "fictionalises": "fictionalizes",
588
+ "fictionalising": "fictionalizing",
589
+ "fillet": "filet",
590
+ "filleted": "fileted",
591
+ "filleting": "fileting",
592
+ "fillets": "filets",
593
+ "finalisation": "finalization",
594
+ "finalise": "finalize",
595
+ "finalised": "finalized",
596
+ "finalises": "finalizes",
597
+ "finalising": "finalizing",
598
+ "flautist": "flutist",
599
+ "flautists": "flutists",
600
+ "flavour": "flavor",
601
+ "flavoured": "flavored",
602
+ "flavouring": "flavoring",
603
+ "flavourings": "flavorings",
604
+ "flavourless": "flavorless",
605
+ "flavours": "flavors",
606
+ "flavoursome": "flavorsome",
607
+ "flyer / flier": "flier / flyer",
608
+ "foetal": "fetal",
609
+ "foetid": "fetid",
610
+ "foetus": "fetus",
611
+ "foetuses": "fetuses",
612
+ "formalisation": "formalization",
613
+ "formalise": "formalize",
614
+ "formalised": "formalized",
615
+ "formalises": "formalizes",
616
+ "formalising": "formalizing",
617
+ "fossilisation": "fossilization",
618
+ "fossilise": "fossilize",
619
+ "fossilised": "fossilized",
620
+ "fossilises": "fossilizes",
621
+ "fossilising": "fossilizing",
622
+ "fraternisation": "fraternization",
623
+ "fraternise": "fraternize",
624
+ "fraternised": "fraternized",
625
+ "fraternises": "fraternizes",
626
+ "fraternising": "fraternizing",
627
+ "fulfil": "fulfill",
628
+ "fulfilment": "fulfillment",
629
+ "fulfils": "fulfills",
630
+ "funnelled": "funneled",
631
+ "funnelling": "funneling",
632
+ "gage": "gauge",
633
+ "gaged": "gauged",
634
+ "gages": "gauges",
635
+ "gaging": "gauging",
636
+ "galvanise": "galvanize",
637
+ "galvanised": "galvanized",
638
+ "galvanises": "galvanizes",
639
+ "galvanising": "galvanizing",
640
+ "gambolled": "gamboled",
641
+ "gambolling": "gamboling",
642
+ "gaol": "jail",
643
+ "gaolbird": "jailbird",
644
+ "gaolbirds": "jailbirds",
645
+ "gaolbreak": "jailbreak",
646
+ "gaolbreaks": "jailbreaks",
647
+ "gaoled": "jailed",
648
+ "gaoler": "jailer",
649
+ "gaolers": "jailers",
650
+ "gaoling": "jailing",
651
+ "gaols": "jails",
652
+ "gasses": "gases",
653
+ "generalisation": "generalization",
654
+ "generalisations": "generalizations",
655
+ "generalise": "generalize",
656
+ "generalised": "generalized",
657
+ "generalises": "generalizes",
658
+ "generalising": "generalizing",
659
+ "ghettoise": "ghettoize",
660
+ "ghettoised": "ghettoized",
661
+ "ghettoises": "ghettoizes",
662
+ "ghettoising": "ghettoizing",
663
+ "gipsies": "gypsies",
664
+ "glamor": "glamour",
665
+ "glamorise": "glamorize",
666
+ "glamorised": "glamorized",
667
+ "glamorises": "glamorizes",
668
+ "glamorising": "glamorizing",
669
+ "globalisation": "globalization",
670
+ "globalise": "globalize",
671
+ "globalised": "globalized",
672
+ "globalises": "globalizes",
673
+ "globalising": "globalizing",
674
+ "glueing": "gluing",
675
+ "goitre": "goiter",
676
+ "goitres": "goiters",
677
+ "gonorrhoea": "gonorrhea",
678
+ "gramme": "gram",
679
+ "grammes": "grams",
680
+ "gravelled": "graveled",
681
+ "grey": "gray",
682
+ "greyed": "grayed",
683
+ "greying": "graying",
684
+ "greyish": "grayish",
685
+ "greyness": "grayness",
686
+ "greys": "grays",
687
+ "grovelled": "groveled",
688
+ "grovelling": "groveling",
689
+ "groyne": "groin",
690
+ "groynes": "groins",
691
+ "gruelling": "grueling",
692
+ "gruellingly": "gruelingly",
693
+ "gryphon": "griffin",
694
+ "gryphons": "griffins",
695
+ "gynaecological": "gynecological",
696
+ "gynaecologist": "gynecologist",
697
+ "gynaecologists": "gynecologists",
698
+ "gynaecology": "gynecology",
699
+ "haematological": "hematological",
700
+ "haematologist": "hematologist",
701
+ "haematologists": "hematologists",
702
+ "haematology": "hematology",
703
+ "haemoglobin": "hemoglobin",
704
+ "haemophilia": "hemophilia",
705
+ "haemophiliac": "hemophiliac",
706
+ "haemophiliacs": "hemophiliacs",
707
+ "haemorrhage": "hemorrhage",
708
+ "haemorrhaged": "hemorrhaged",
709
+ "haemorrhages": "hemorrhages",
710
+ "haemorrhaging": "hemorrhaging",
711
+ "haemorrhoids": "hemorrhoids",
712
+ "harbour": "harbor",
713
+ "harboured": "harbored",
714
+ "harbouring": "harboring",
715
+ "harbours": "harbors",
716
+ "harmonisation": "harmonization",
717
+ "harmonise": "harmonize",
718
+ "harmonised": "harmonized",
719
+ "harmonises": "harmonizes",
720
+ "harmonising": "harmonizing",
721
+ "homoeopath": "homeopath",
722
+ "homoeopathic": "homeopathic",
723
+ "homoeopaths": "homeopaths",
724
+ "homoeopathy": "homeopathy",
725
+ "homogenise": "homogenize",
726
+ "homogenised": "homogenized",
727
+ "homogenises": "homogenizes",
728
+ "homogenising": "homogenizing",
729
+ "honour": "honor",
730
+ "honourable": "honorable",
731
+ "honourably": "honorably",
732
+ "honoured": "honored",
733
+ "honouring": "honoring",
734
+ "honours": "honors",
735
+ "hospitalisation": "hospitalization",
736
+ "hospitalise": "hospitalize",
737
+ "hospitalised": "hospitalized",
738
+ "hospitalises": "hospitalizes",
739
+ "hospitalising": "hospitalizing",
740
+ "humanise": "humanize",
741
+ "humanised": "humanized",
742
+ "humanises": "humanizes",
743
+ "humanising": "humanizing",
744
+ "humour": "humor",
745
+ "humoured": "humored",
746
+ "humouring": "humoring",
747
+ "humourless": "humorless",
748
+ "humours": "humors",
749
+ "hybridise": "hybridize",
750
+ "hybridised": "hybridized",
751
+ "hybridises": "hybridizes",
752
+ "hybridising": "hybridizing",
753
+ "hypnotise": "hypnotize",
754
+ "hypnotised": "hypnotized",
755
+ "hypnotises": "hypnotizes",
756
+ "hypnotising": "hypnotizing",
757
+ "hypothesise": "hypothesize",
758
+ "hypothesised": "hypothesized",
759
+ "hypothesises": "hypothesizes",
760
+ "hypothesising": "hypothesizing",
761
+ "idealisation": "idealization",
762
+ "idealise": "idealize",
763
+ "idealised": "idealized",
764
+ "idealises": "idealizes",
765
+ "idealising": "idealizing",
766
+ "idolise": "idolize",
767
+ "idolised": "idolized",
768
+ "idolises": "idolizes",
769
+ "idolising": "idolizing",
770
+ "immobilisation": "immobilization",
771
+ "immobilise": "immobilize",
772
+ "immobilised": "immobilized",
773
+ "immobiliser": "immobilizer",
774
+ "immobilisers": "immobilizers",
775
+ "immobilises": "immobilizes",
776
+ "immobilising": "immobilizing",
777
+ "immortalise": "immortalize",
778
+ "immortalised": "immortalized",
779
+ "immortalises": "immortalizes",
780
+ "immortalising": "immortalizing",
781
+ "immunisation": "immunization",
782
+ "immunise": "immunize",
783
+ "immunised": "immunized",
784
+ "immunises": "immunizes",
785
+ "immunising": "immunizing",
786
+ "impanelled": "impaneled",
787
+ "impanelling": "impaneling",
788
+ "imperilled": "imperiled",
789
+ "imperilling": "imperiling",
790
+ "individualise": "individualize",
791
+ "individualised": "individualized",
792
+ "individualises": "individualizes",
793
+ "individualising": "individualizing",
794
+ "industrialise": "industrialize",
795
+ "industrialised": "industrialized",
796
+ "industrialises": "industrializes",
797
+ "industrialising": "industrializing",
798
+ "inflexion": "inflection",
799
+ "inflexions": "inflections",
800
+ "initialise": "initialize",
801
+ "initialised": "initialized",
802
+ "initialises": "initializes",
803
+ "initialising": "initializing",
804
+ "initialled": "initialed",
805
+ "initialling": "initialing",
806
+ "instal": "install",
807
+ "instalment": "installment",
808
+ "instalments": "installments",
809
+ "instals": "installs",
810
+ "instil": "instill",
811
+ "instils": "instills",
812
+ "institutionalisation": "institutionalization",
813
+ "institutionalise": "institutionalize",
814
+ "institutionalised": "institutionalized",
815
+ "institutionalises": "institutionalizes",
816
+ "institutionalising": "institutionalizing",
817
+ "intellectualise": "intellectualize",
818
+ "intellectualised": "intellectualized",
819
+ "intellectualises": "intellectualizes",
820
+ "intellectualising": "intellectualizing",
821
+ "internalisation": "internalization",
822
+ "internalise": "internalize",
823
+ "internalised": "internalized",
824
+ "internalises": "internalizes",
825
+ "internalising": "internalizing",
826
+ "internationalisation": "internationalization",
827
+ "internationalise": "internationalize",
828
+ "internationalised": "internationalized",
829
+ "internationalises": "internationalizes",
830
+ "internationalising": "internationalizing",
831
+ "ionisation": "ionization",
832
+ "ionise": "ionize",
833
+ "ionised": "ionized",
834
+ "ioniser": "ionizer",
835
+ "ionisers": "ionizers",
836
+ "ionises": "ionizes",
837
+ "ionising": "ionizing",
838
+ "italicise": "italicize",
839
+ "italicised": "italicized",
840
+ "italicises": "italicizes",
841
+ "italicising": "italicizing",
842
+ "itemise": "itemize",
843
+ "itemised": "itemized",
844
+ "itemises": "itemizes",
845
+ "itemising": "itemizing",
846
+ "jeopardise": "jeopardize",
847
+ "jeopardised": "jeopardized",
848
+ "jeopardises": "jeopardizes",
849
+ "jeopardising": "jeopardizing",
850
+ "jewelled": "jeweled",
851
+ "jeweller": "jeweler",
852
+ "jewellers": "jewelers",
853
+ "jewellery": "jewelry",
854
+ "judgement": "judgment",
855
+ "kilogramme": "kilogram",
856
+ "kilogrammes": "kilograms",
857
+ "kilometre": "kilometer",
858
+ "kilometres": "kilometers",
859
+ "labelled": "labeled",
860
+ "labelling": "labeling",
861
+ "labour": "labor",
862
+ "laboured": "labored",
863
+ "labourer": "laborer",
864
+ "labourers": "laborers",
865
+ "labouring": "laboring",
866
+ "labours": "labors",
867
+ "lacklustre": "lackluster",
868
+ "legalisation": "legalization",
869
+ "legalise": "legalize",
870
+ "legalised": "legalized",
871
+ "legalises": "legalizes",
872
+ "legalising": "legalizing",
873
+ "legitimise": "legitimize",
874
+ "legitimised": "legitimized",
875
+ "legitimises": "legitimizes",
876
+ "legitimising": "legitimizing",
877
+ "leukaemia": "leukemia",
878
+ "levelled": "leveled",
879
+ "leveller": "leveler",
880
+ "levellers": "levelers",
881
+ "levelling": "leveling",
882
+ "libelled": "libeled",
883
+ "libelling": "libeling",
884
+ "libellous": "libelous",
885
+ "liberalisation": "liberalization",
886
+ "liberalise": "liberalize",
887
+ "liberalised": "liberalized",
888
+ "liberalises": "liberalizes",
889
+ "liberalising": "liberalizing",
890
+ "licence": "license",
891
+ "licenced": "licensed",
892
+ "licences": "licenses",
893
+ "licencing": "licensing",
894
+ "likeable": "likable",
895
+ "lionisation": "lionization",
896
+ "lionise": "lionize",
897
+ "lionised": "lionized",
898
+ "lionises": "lionizes",
899
+ "lionising": "lionizing",
900
+ "liquidise": "liquidize",
901
+ "liquidised": "liquidized",
902
+ "liquidiser": "liquidizer",
903
+ "liquidisers": "liquidizers",
904
+ "liquidises": "liquidizes",
905
+ "liquidising": "liquidizing",
906
+ "litre": "liter",
907
+ "litres": "liters",
908
+ "localise": "localize",
909
+ "localised": "localized",
910
+ "localises": "localizes",
911
+ "localising": "localizing",
912
+ "louvre": "louver",
913
+ "louvred": "louvered",
914
+ "louvres": "louvers",
915
+ "lustre": "luster",
916
+ "magnetise": "magnetize",
917
+ "magnetised": "magnetized",
918
+ "magnetises": "magnetizes",
919
+ "magnetising": "magnetizing",
920
+ "manoeuvrability": "maneuverability",
921
+ "manoeuvrable": "maneuverable",
922
+ "manoeuvre": "maneuver",
923
+ "manoeuvred": "maneuvered",
924
+ "manoeuvres": "maneuvers",
925
+ "manoeuvring": "maneuvering",
926
+ "manoeuvrings": "maneuverings",
927
+ "marginalisation": "marginalization",
928
+ "marginalise": "marginalize",
929
+ "marginalised": "marginalized",
930
+ "marginalises": "marginalizes",
931
+ "marginalising": "marginalizing",
932
+ "marshalled": "marshaled",
933
+ "marshalling": "marshaling",
934
+ "marvelled": "marveled",
935
+ "marvelling": "marveling",
936
+ "marvellous": "marvelous",
937
+ "marvellously": "marvelously",
938
+ "materialisation": "materialization",
939
+ "materialise": "materialize",
940
+ "materialised": "materialized",
941
+ "materialises": "materializes",
942
+ "materialising": "materializing",
943
+ "maximisation": "maximization",
944
+ "maximise": "maximize",
945
+ "maximised": "maximized",
946
+ "maximises": "maximizes",
947
+ "maximising": "maximizing",
948
+ "meagre": "meager",
949
+ "mechanisation": "mechanization",
950
+ "mechanise": "mechanize",
951
+ "mechanised": "mechanized",
952
+ "mechanises": "mechanizes",
953
+ "mechanising": "mechanizing",
954
+ "mediaeval": "medieval",
955
+ "memorialise": "memorialize",
956
+ "memorialised": "memorialized",
957
+ "memorialises": "memorializes",
958
+ "memorialising": "memorializing",
959
+ "memorise": "memorize",
960
+ "memorised": "memorized",
961
+ "memorises": "memorizes",
962
+ "memorising": "memorizing",
963
+ "mesmerise": "mesmerize",
964
+ "mesmerised": "mesmerized",
965
+ "mesmerises": "mesmerizes",
966
+ "mesmerising": "mesmerizing",
967
+ "metabolise": "metabolize",
968
+ "metabolised": "metabolized",
969
+ "metabolises": "metabolizes",
970
+ "metabolising": "metabolizing",
971
+ "metre": "meter",
972
+ "metres": "meters",
973
+ "mhm": "hmm",
974
+ "micrometre": "micrometer",
975
+ "micrometres": "micrometers",
976
+ "militarise": "militarize",
977
+ "militarised": "militarized",
978
+ "militarises": "militarizes",
979
+ "militarising": "militarizing",
980
+ "milligramme": "milligram",
981
+ "milligrammes": "milligrams",
982
+ "millilitre": "milliliter",
983
+ "millilitres": "milliliters",
984
+ "millimetre": "millimeter",
985
+ "millimetres": "millimeters",
986
+ "miniaturisation": "miniaturization",
987
+ "miniaturise": "miniaturize",
988
+ "miniaturised": "miniaturized",
989
+ "miniaturises": "miniaturizes",
990
+ "miniaturising": "miniaturizing",
991
+ "minibusses": "minibuses",
992
+ "minimise": "minimize",
993
+ "minimised": "minimized",
994
+ "minimises": "minimizes",
995
+ "minimising": "minimizing",
996
+ "misbehaviour": "misbehavior",
997
+ "misdemeanour": "misdemeanor",
998
+ "misdemeanours": "misdemeanors",
999
+ "misspelt": "misspelled",
1000
+ "mitre": "miter",
1001
+ "mitres": "miters",
1002
+ "mm": "hmm",
1003
+ "mmm": "hmm",
1004
+ "mobilisation": "mobilization",
1005
+ "mobilise": "mobilize",
1006
+ "mobilised": "mobilized",
1007
+ "mobilises": "mobilizes",
1008
+ "mobilising": "mobilizing",
1009
+ "modelled": "modeled",
1010
+ "modeller": "modeler",
1011
+ "modellers": "modelers",
1012
+ "modelling": "modeling",
1013
+ "modernise": "modernize",
1014
+ "modernised": "modernized",
1015
+ "modernises": "modernizes",
1016
+ "modernising": "modernizing",
1017
+ "moisturise": "moisturize",
1018
+ "moisturised": "moisturized",
1019
+ "moisturiser": "moisturizer",
1020
+ "moisturisers": "moisturizers",
1021
+ "moisturises": "moisturizes",
1022
+ "moisturising": "moisturizing",
1023
+ "monologue": "monolog",
1024
+ "monologues": "monologs",
1025
+ "monopolisation": "monopolization",
1026
+ "monopolise": "monopolize",
1027
+ "monopolised": "monopolized",
1028
+ "monopolises": "monopolizes",
1029
+ "monopolising": "monopolizing",
1030
+ "moralise": "moralize",
1031
+ "moralised": "moralized",
1032
+ "moralises": "moralizes",
1033
+ "moralising": "moralizing",
1034
+ "motorised": "motorized",
1035
+ "mould": "mold",
1036
+ "moulded": "molded",
1037
+ "moulder": "molder",
1038
+ "mouldered": "moldered",
1039
+ "mouldering": "moldering",
1040
+ "moulders": "molders",
1041
+ "mouldier": "moldier",
1042
+ "mouldiest": "moldiest",
1043
+ "moulding": "molding",
1044
+ "mouldings": "moldings",
1045
+ "moulds": "molds",
1046
+ "mouldy": "moldy",
1047
+ "moult": "molt",
1048
+ "moulted": "molted",
1049
+ "moulting": "molting",
1050
+ "moults": "molts",
1051
+ "moustache": "mustache",
1052
+ "moustached": "mustached",
1053
+ "moustaches": "mustaches",
1054
+ "moustachioed": "mustachioed",
1055
+ "multicoloured": "multicolored",
1056
+ "nationalisation": "nationalization",
1057
+ "nationalisations": "nationalizations",
1058
+ "nationalise": "nationalize",
1059
+ "nationalised": "nationalized",
1060
+ "nationalises": "nationalizes",
1061
+ "nationalising": "nationalizing",
1062
+ "naturalisation": "naturalization",
1063
+ "naturalise": "naturalize",
1064
+ "naturalised": "naturalized",
1065
+ "naturalises": "naturalizes",
1066
+ "naturalising": "naturalizing",
1067
+ "neighbour": "neighbor",
1068
+ "neighbourhood": "neighborhood",
1069
+ "neighbourhoods": "neighborhoods",
1070
+ "neighbouring": "neighboring",
1071
+ "neighbourliness": "neighborliness",
1072
+ "neighbourly": "neighborly",
1073
+ "neighbours": "neighbors",
1074
+ "neutralisation": "neutralization",
1075
+ "neutralise": "neutralize",
1076
+ "neutralised": "neutralized",
1077
+ "neutralises": "neutralizes",
1078
+ "neutralising": "neutralizing",
1079
+ "normalisation": "normalization",
1080
+ "normalise": "normalize",
1081
+ "normalised": "normalized",
1082
+ "normalises": "normalizes",
1083
+ "normalising": "normalizing",
1084
+ "odour": "odor",
1085
+ "odourless": "odorless",
1086
+ "odours": "odors",
1087
+ "oesophagus": "esophagus",
1088
+ "oesophaguses": "esophaguses",
1089
+ "oestrogen": "estrogen",
1090
+ "offence": "offense",
1091
+ "offences": "offenses",
1092
+ "omelette": "omelet",
1093
+ "omelettes": "omelets",
1094
+ "optimise": "optimize",
1095
+ "optimised": "optimized",
1096
+ "optimises": "optimizes",
1097
+ "optimising": "optimizing",
1098
+ "organisation": "organization",
1099
+ "organisational": "organizational",
1100
+ "organisations": "organizations",
1101
+ "organise": "organize",
1102
+ "organised": "organized",
1103
+ "organiser": "organizer",
1104
+ "organisers": "organizers",
1105
+ "organises": "organizes",
1106
+ "organising": "organizing",
1107
+ "orthopaedic": "orthopedic",
1108
+ "orthopaedics": "orthopedics",
1109
+ "ostracise": "ostracize",
1110
+ "ostracised": "ostracized",
1111
+ "ostracises": "ostracizes",
1112
+ "ostracising": "ostracizing",
1113
+ "outmanoeuvre": "outmaneuver",
1114
+ "outmanoeuvred": "outmaneuvered",
1115
+ "outmanoeuvres": "outmaneuvers",
1116
+ "outmanoeuvring": "outmaneuvering",
1117
+ "overemphasise": "overemphasize",
1118
+ "overemphasised": "overemphasized",
1119
+ "overemphasises": "overemphasizes",
1120
+ "overemphasising": "overemphasizing",
1121
+ "oxidisation": "oxidization",
1122
+ "oxidise": "oxidize",
1123
+ "oxidised": "oxidized",
1124
+ "oxidises": "oxidizes",
1125
+ "oxidising": "oxidizing",
1126
+ "paederast": "pederast",
1127
+ "paederasts": "pederasts",
1128
+ "paediatric": "pediatric",
1129
+ "paediatrician": "pediatrician",
1130
+ "paediatricians": "pediatricians",
1131
+ "paediatrics": "pediatrics",
1132
+ "paedophile": "pedophile",
1133
+ "paedophiles": "pedophiles",
1134
+ "paedophilia": "pedophilia",
1135
+ "palaeolithic": "paleolithic",
1136
+ "palaeontologist": "paleontologist",
1137
+ "palaeontologists": "paleontologists",
1138
+ "palaeontology": "paleontology",
1139
+ "panelled": "paneled",
1140
+ "panelling": "paneling",
1141
+ "panellist": "panelist",
1142
+ "panellists": "panelists",
1143
+ "paralyse": "paralyze",
1144
+ "paralysed": "paralyzed",
1145
+ "paralyses": "paralyzes",
1146
+ "paralysing": "paralyzing",
1147
+ "parcelled": "parceled",
1148
+ "parcelling": "parceling",
1149
+ "parlour": "parlor",
1150
+ "parlours": "parlors",
1151
+ "particularise": "particularize",
1152
+ "particularised": "particularized",
1153
+ "particularises": "particularizes",
1154
+ "particularising": "particularizing",
1155
+ "passivisation": "passivization",
1156
+ "passivise": "passivize",
1157
+ "passivised": "passivized",
1158
+ "passivises": "passivizes",
1159
+ "passivising": "passivizing",
1160
+ "pasteurisation": "pasteurization",
1161
+ "pasteurise": "pasteurize",
1162
+ "pasteurised": "pasteurized",
1163
+ "pasteurises": "pasteurizes",
1164
+ "pasteurising": "pasteurizing",
1165
+ "patronise": "patronize",
1166
+ "patronised": "patronized",
1167
+ "patronises": "patronizes",
1168
+ "patronising": "patronizing",
1169
+ "patronisingly": "patronizingly",
1170
+ "pedalled": "pedaled",
1171
+ "pedalling": "pedaling",
1172
+ "pedestrianisation": "pedestrianization",
1173
+ "pedestrianise": "pedestrianize",
1174
+ "pedestrianised": "pedestrianized",
1175
+ "pedestrianises": "pedestrianizes",
1176
+ "pedestrianising": "pedestrianizing",
1177
+ "penalise": "penalize",
1178
+ "penalised": "penalized",
1179
+ "penalises": "penalizes",
1180
+ "penalising": "penalizing",
1181
+ "pencilled": "penciled",
1182
+ "pencilling": "penciling",
1183
+ "personalise": "personalize",
1184
+ "personalised": "personalized",
1185
+ "personalises": "personalizes",
1186
+ "personalising": "personalizing",
1187
+ "pharmacopoeia": "pharmacopeia",
1188
+ "pharmacopoeias": "pharmacopeias",
1189
+ "philosophise": "philosophize",
1190
+ "philosophised": "philosophized",
1191
+ "philosophises": "philosophizes",
1192
+ "philosophising": "philosophizing",
1193
+ "philtre": "filter",
1194
+ "philtres": "filters",
1195
+ "phoney": "phony",
1196
+ "plagiarise": "plagiarize",
1197
+ "plagiarised": "plagiarized",
1198
+ "plagiarises": "plagiarizes",
1199
+ "plagiarising": "plagiarizing",
1200
+ "plough": "plow",
1201
+ "ploughed": "plowed",
1202
+ "ploughing": "plowing",
1203
+ "ploughman": "plowman",
1204
+ "ploughmen": "plowmen",
1205
+ "ploughs": "plows",
1206
+ "ploughshare": "plowshare",
1207
+ "ploughshares": "plowshares",
1208
+ "polarisation": "polarization",
1209
+ "polarise": "polarize",
1210
+ "polarised": "polarized",
1211
+ "polarises": "polarizes",
1212
+ "polarising": "polarizing",
1213
+ "politicisation": "politicization",
1214
+ "politicise": "politicize",
1215
+ "politicised": "politicized",
1216
+ "politicises": "politicizes",
1217
+ "politicising": "politicizing",
1218
+ "popularisation": "popularization",
1219
+ "popularise": "popularize",
1220
+ "popularised": "popularized",
1221
+ "popularises": "popularizes",
1222
+ "popularising": "popularizing",
1223
+ "pouffe": "pouf",
1224
+ "pouffes": "poufs",
1225
+ "practise": "practice",
1226
+ "practised": "practiced",
1227
+ "practises": "practices",
1228
+ "practising": "practicing",
1229
+ "praesidium": "presidium",
1230
+ "praesidiums": "presidiums",
1231
+ "pressurisation": "pressurization",
1232
+ "pressurise": "pressurize",
1233
+ "pressurised": "pressurized",
1234
+ "pressurises": "pressurizes",
1235
+ "pressurising": "pressurizing",
1236
+ "pretence": "pretense",
1237
+ "pretences": "pretenses",
1238
+ "primaeval": "primeval",
1239
+ "prioritisation": "prioritization",
1240
+ "prioritise": "prioritize",
1241
+ "prioritised": "prioritized",
1242
+ "prioritises": "prioritizes",
1243
+ "prioritising": "prioritizing",
1244
+ "privatisation": "privatization",
1245
+ "privatisations": "privatizations",
1246
+ "privatise": "privatize",
1247
+ "privatised": "privatized",
1248
+ "privatises": "privatizes",
1249
+ "privatising": "privatizing",
1250
+ "professionalisation": "professionalization",
1251
+ "professionalise": "professionalize",
1252
+ "professionalised": "professionalized",
1253
+ "professionalises": "professionalizes",
1254
+ "professionalising": "professionalizing",
1255
+ "programme": "program",
1256
+ "programmes": "programs",
1257
+ "prologue": "prolog",
1258
+ "prologues": "prologs",
1259
+ "propagandise": "propagandize",
1260
+ "propagandised": "propagandized",
1261
+ "propagandises": "propagandizes",
1262
+ "propagandising": "propagandizing",
1263
+ "proselytise": "proselytize",
1264
+ "proselytised": "proselytized",
1265
+ "proselytiser": "proselytizer",
1266
+ "proselytisers": "proselytizers",
1267
+ "proselytises": "proselytizes",
1268
+ "proselytising": "proselytizing",
1269
+ "psychoanalyse": "psychoanalyze",
1270
+ "psychoanalysed": "psychoanalyzed",
1271
+ "psychoanalyses": "psychoanalyzes",
1272
+ "psychoanalysing": "psychoanalyzing",
1273
+ "publicise": "publicize",
1274
+ "publicised": "publicized",
1275
+ "publicises": "publicizes",
1276
+ "publicising": "publicizing",
1277
+ "pulverisation": "pulverization",
1278
+ "pulverise": "pulverize",
1279
+ "pulverised": "pulverized",
1280
+ "pulverises": "pulverizes",
1281
+ "pulverising": "pulverizing",
1282
+ "pummelled": "pummel",
1283
+ "pummelling": "pummeled",
1284
+ "pyjama": "pajama",
1285
+ "pyjamas": "pajamas",
1286
+ "pzazz": "pizzazz",
1287
+ "quarrelled": "quarreled",
1288
+ "quarrelling": "quarreling",
1289
+ "radicalise": "radicalize",
1290
+ "radicalised": "radicalized",
1291
+ "radicalises": "radicalizes",
1292
+ "radicalising": "radicalizing",
1293
+ "rancour": "rancor",
1294
+ "randomise": "randomize",
1295
+ "randomised": "randomized",
1296
+ "randomises": "randomizes",
1297
+ "randomising": "randomizing",
1298
+ "rationalisation": "rationalization",
1299
+ "rationalisations": "rationalizations",
1300
+ "rationalise": "rationalize",
1301
+ "rationalised": "rationalized",
1302
+ "rationalises": "rationalizes",
1303
+ "rationalising": "rationalizing",
1304
+ "ravelled": "raveled",
1305
+ "ravelling": "raveling",
1306
+ "realisable": "realizable",
1307
+ "realisation": "realization",
1308
+ "realisations": "realizations",
1309
+ "realise": "realize",
1310
+ "realised": "realized",
1311
+ "realises": "realizes",
1312
+ "realising": "realizing",
1313
+ "recognisable": "recognizable",
1314
+ "recognisably": "recognizably",
1315
+ "recognisance": "recognizance",
1316
+ "recognise": "recognize",
1317
+ "recognised": "recognized",
1318
+ "recognises": "recognizes",
1319
+ "recognising": "recognizing",
1320
+ "reconnoitre": "reconnoiter",
1321
+ "reconnoitred": "reconnoitered",
1322
+ "reconnoitres": "reconnoiters",
1323
+ "reconnoitring": "reconnoitering",
1324
+ "refuelled": "refueled",
1325
+ "refuelling": "refueling",
1326
+ "regularisation": "regularization",
1327
+ "regularise": "regularize",
1328
+ "regularised": "regularized",
1329
+ "regularises": "regularizes",
1330
+ "regularising": "regularizing",
1331
+ "remodelled": "remodeled",
1332
+ "remodelling": "remodeling",
1333
+ "remould": "remold",
1334
+ "remoulded": "remolded",
1335
+ "remoulding": "remolding",
1336
+ "remoulds": "remolds",
1337
+ "reorganisation": "reorganization",
1338
+ "reorganisations": "reorganizations",
1339
+ "reorganise": "reorganize",
1340
+ "reorganised": "reorganized",
1341
+ "reorganises": "reorganizes",
1342
+ "reorganising": "reorganizing",
1343
+ "revelled": "reveled",
1344
+ "reveller": "reveler",
1345
+ "revellers": "revelers",
1346
+ "revelling": "reveling",
1347
+ "revitalise": "revitalize",
1348
+ "revitalised": "revitalized",
1349
+ "revitalises": "revitalizes",
1350
+ "revitalising": "revitalizing",
1351
+ "revolutionise": "revolutionize",
1352
+ "revolutionised": "revolutionized",
1353
+ "revolutionises": "revolutionizes",
1354
+ "revolutionising": "revolutionizing",
1355
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1456
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1495
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1496
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1497
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1498
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1499
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1500
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1504
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1507
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1525
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1528
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1632
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1633
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1681
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1682
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1690
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1692
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1693
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1694
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1695
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1702
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1704
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1707
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1708
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1709
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1710
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1711
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1712
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1713
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1714
+ "watercolour": "watercolor",
1715
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1716
+ "weaselled": "weaseled",
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1721
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1729
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1730
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1731
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1732
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1733
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1734
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1735
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1736
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1737
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1739
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+ "yoghurts": "yogurts"
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+ "sha256": "2e6fc356876de06fbcb7d8eeefd7989603f7e5a9fe8656e436d937510d92aca5"
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+ },
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+ "requirements.txt": {
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+ "bytes": 43,
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+ "sha256": "ca99bf3c5ba6f0372b2c651cb92de1f886051bf70e0a1a997b1d275e56090293"
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+ },
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+ "special_tokens_map.json": {
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+ "bytes": 2325,
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+ "sha256": "384dd595fb17ba285f1a3fc559f6014fcb7758de12b4884fc954caa69e1a4f2f"
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+ },
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+ "tokenizer.json": {
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+ "bytes": 3930645,
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+ "sha256": "5c1bf30c9e716e1477bedef846b01be0013daecb89e9e3ef7ab89b23c178df1b"
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+ },
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+ "transcribe.py": {
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+ "bytes": 2683,
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+ "sha256": "40f607a8e8f44811b6313287bd265e1bcf4f52c42a54f29c7571657debce0390"
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+ },
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+ "verification.json": {
83
+ "bytes": 1519,
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+ "sha256": "75297052a2bbbae2900c26f239ce859cb4db7468c0633127098d9b2eedb8c0e6"
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+ },
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+ "vocab.json": {
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+ "bytes": 835528,
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+ "sha256": "6788c80b082e9b0d1393147d3a3e62ba19285ac0c82ace8e5ef00f37ead58971"
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+ },
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+ "vocabulary.json": {
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+ "bytes": 1119981,
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+ "sha256": "697ee5a65726e46b4d294d1a243d98e1878f5d81caba17891b1c5ebab7a912a9"
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+ },
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+ "WHISPER_LICENSE": {
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+ "bytes": 1084,
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+ "sha256": "328df171594796be1ffa76628981c82bdbe2e391edb0204ac37acac6a63cc852"
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+ }
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+ },
99
+ "verification_runtime_versions": {
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+ "faster-whisper": "1.2.1",
101
+ "ctranslate2": "4.8.1",
102
+ "torch": "2.13.0",
103
+ "transformers": "4.57.6",
104
+ "peft": "0.21.0",
105
+ "huggingface-hub": "0.36.2",
106
+ "safetensors": "0.8.0"
107
+ }
108
+ }
requirements.txt ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ faster-whisper==1.2.1
2
+ ctranslate2==4.8.1
special_tokens_map.json ADDED
@@ -0,0 +1,139 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|startoftranscript|>",
4
+ "<|en|>",
5
+ "<|zh|>",
6
+ "<|de|>",
7
+ "<|es|>",
8
+ "<|ru|>",
9
+ "<|ko|>",
10
+ "<|fr|>",
11
+ "<|ja|>",
12
+ "<|pt|>",
13
+ "<|tr|>",
14
+ "<|pl|>",
15
+ "<|ca|>",
16
+ "<|nl|>",
17
+ "<|ar|>",
18
+ "<|sv|>",
19
+ "<|it|>",
20
+ "<|id|>",
21
+ "<|hi|>",
22
+ "<|fi|>",
23
+ "<|vi|>",
24
+ "<|he|>",
25
+ "<|uk|>",
26
+ "<|el|>",
27
+ "<|ms|>",
28
+ "<|cs|>",
29
+ "<|ro|>",
30
+ "<|da|>",
31
+ "<|hu|>",
32
+ "<|ta|>",
33
+ "<|no|>",
34
+ "<|th|>",
35
+ "<|ur|>",
36
+ "<|hr|>",
37
+ "<|bg|>",
38
+ "<|lt|>",
39
+ "<|la|>",
40
+ "<|mi|>",
41
+ "<|ml|>",
42
+ "<|cy|>",
43
+ "<|sk|>",
44
+ "<|te|>",
45
+ "<|fa|>",
46
+ "<|lv|>",
47
+ "<|bn|>",
48
+ "<|sr|>",
49
+ "<|az|>",
50
+ "<|sl|>",
51
+ "<|kn|>",
52
+ "<|et|>",
53
+ "<|mk|>",
54
+ "<|br|>",
55
+ "<|eu|>",
56
+ "<|is|>",
57
+ "<|hy|>",
58
+ "<|ne|>",
59
+ "<|mn|>",
60
+ "<|bs|>",
61
+ "<|kk|>",
62
+ "<|sq|>",
63
+ "<|sw|>",
64
+ "<|gl|>",
65
+ "<|mr|>",
66
+ "<|pa|>",
67
+ "<|si|>",
68
+ "<|km|>",
69
+ "<|sn|>",
70
+ "<|yo|>",
71
+ "<|so|>",
72
+ "<|af|>",
73
+ "<|oc|>",
74
+ "<|ka|>",
75
+ "<|be|>",
76
+ "<|tg|>",
77
+ "<|sd|>",
78
+ "<|gu|>",
79
+ "<|am|>",
80
+ "<|yi|>",
81
+ "<|lo|>",
82
+ "<|uz|>",
83
+ "<|fo|>",
84
+ "<|ht|>",
85
+ "<|ps|>",
86
+ "<|tk|>",
87
+ "<|nn|>",
88
+ "<|mt|>",
89
+ "<|sa|>",
90
+ "<|lb|>",
91
+ "<|my|>",
92
+ "<|bo|>",
93
+ "<|tl|>",
94
+ "<|mg|>",
95
+ "<|as|>",
96
+ "<|tt|>",
97
+ "<|haw|>",
98
+ "<|ln|>",
99
+ "<|ha|>",
100
+ "<|ba|>",
101
+ "<|jw|>",
102
+ "<|su|>",
103
+ "<|yue|>",
104
+ "<|translate|>",
105
+ "<|transcribe|>",
106
+ "<|startoflm|>",
107
+ "<|startofprev|>",
108
+ "<|nospeech|>",
109
+ "<|notimestamps|>"
110
+ ],
111
+ "bos_token": {
112
+ "content": "<|endoftext|>",
113
+ "lstrip": false,
114
+ "normalized": false,
115
+ "rstrip": false,
116
+ "single_word": false
117
+ },
118
+ "eos_token": {
119
+ "content": "<|endoftext|>",
120
+ "lstrip": false,
121
+ "normalized": false,
122
+ "rstrip": false,
123
+ "single_word": false
124
+ },
125
+ "pad_token": {
126
+ "content": "<|endoftext|>",
127
+ "lstrip": false,
128
+ "normalized": false,
129
+ "rstrip": false,
130
+ "single_word": false
131
+ },
132
+ "unk_token": {
133
+ "content": "<|endoftext|>",
134
+ "lstrip": false,
135
+ "normalized": false,
136
+ "rstrip": false,
137
+ "single_word": false
138
+ }
139
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
transcribe.py ADDED
@@ -0,0 +1,60 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # SPDX-License-Identifier: MIT
2
+ """Transcribe a local audio file with NepTrans v4 and faster-whisper."""
3
+ from __future__ import annotations
4
+
5
+ import argparse
6
+ import json
7
+ from pathlib import Path
8
+
9
+ from faster_whisper import WhisperModel
10
+
11
+
12
+ def main():
13
+ parser = argparse.ArgumentParser(description=__doc__)
14
+ parser.add_argument("audio", type=Path)
15
+ parser.add_argument("--model", default="DontHash/neptrans-v4-ct2")
16
+ parser.add_argument("--revision", default="v4.0.0")
17
+ parser.add_argument("--device", choices=["cpu", "cuda"], default="cpu")
18
+ parser.add_argument("--compute-type", default=None)
19
+ parser.add_argument("--language", choices=["ne", "en"], default=None)
20
+ parser.add_argument("--beam-size", type=int, default=1)
21
+ parser.add_argument("--local-files-only", action="store_true")
22
+ parser.add_argument("--output", type=Path)
23
+ parser.add_argument("--json-output", type=Path)
24
+ args = parser.parse_args()
25
+ if not args.audio.is_file():
26
+ parser.error(f"Audio file not found: {args.audio}")
27
+ if args.beam_size < 1:
28
+ parser.error("--beam-size must be positive")
29
+ if args.output and args.output.resolve() == args.audio.resolve():
30
+ parser.error("Output must not overwrite the input audio")
31
+ if args.json_output and args.json_output.resolve() == args.audio.resolve():
32
+ parser.error("JSON output must not overwrite the input audio")
33
+ if args.output and args.json_output and args.output.resolve() == args.json_output.resolve():
34
+ parser.error("Text and JSON outputs must be different files")
35
+ compute_type = args.compute_type or ("int8_float16" if args.device == "cuda" else "int8")
36
+ model = WhisperModel(
37
+ args.model, revision=args.revision, device=args.device,
38
+ compute_type=compute_type, local_files_only=args.local_files_only,
39
+ )
40
+ segments, info = model.transcribe(
41
+ str(args.audio), language=args.language, task="transcribe",
42
+ beam_size=args.beam_size, condition_on_previous_text=False,
43
+ )
44
+ rows = [{"start": s.start, "end": s.end, "text": s.text.strip()} for s in segments]
45
+ text = "\n".join(row["text"] for row in rows) + "\n"
46
+ if args.output:
47
+ args.output.write_text(text, encoding="utf-8")
48
+ else:
49
+ print(text, end="")
50
+ if args.json_output:
51
+ args.json_output.write_text(json.dumps({
52
+ "language": info.language,
53
+ "language_probability": info.language_probability,
54
+ "duration": info.duration,
55
+ "segments": rows,
56
+ }, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
57
+
58
+
59
+ if __name__ == "__main__":
60
+ main()
verification.json ADDED
@@ -0,0 +1,58 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "verification_date": "2026-10-20",
3
+ "operating_system": "Windows",
4
+ "python": "3.13.7",
5
+ "package_versions": {
6
+ "faster-whisper": "1.2.1",
7
+ "ctranslate2": "4.8.1",
8
+ "torch": "2.13.0",
9
+ "transformers": "4.57.6",
10
+ "peft": "0.21.0",
11
+ "huggingface-hub": "0.36.2",
12
+ "safetensors": "0.8.0"
13
+ },
14
+ "benchmark_repeated": false,
15
+ "scope": "Functional packaging checks; not an independent quality evaluation",
16
+ "checks": {
17
+ "original_export_hash_matches": true,
18
+ "gpu": {
19
+ "status": "passed",
20
+ "hardware": "NVIDIA RTX 2050 4 GiB",
21
+ "compute_type": "int8_float16",
22
+ "local_files_only": true,
23
+ "clips": [
24
+ {
25
+ "bucket": "nepali",
26
+ "duration_seconds": 3.04,
27
+ "detected_language": "ne",
28
+ "output_nonempty": true
29
+ },
30
+ {
31
+ "bucket": "english",
32
+ "duration_seconds": 3.12,
33
+ "detected_language": "en",
34
+ "output_nonempty": true
35
+ },
36
+ {
37
+ "bucket": "mixed",
38
+ "duration_seconds": 3.0,
39
+ "detected_language": "ne",
40
+ "output_nonempty": true
41
+ }
42
+ ]
43
+ },
44
+ "cpu": {
45
+ "status": "passed",
46
+ "compute_type": "int8",
47
+ "local_files_only": true,
48
+ "clip_duration_seconds": 3.0,
49
+ "output_nonempty": true
50
+ },
51
+ "transcribe_helper": {
52
+ "status": "passed",
53
+ "device": "cpu",
54
+ "text_output": "nonempty",
55
+ "json_segments": 1
56
+ }
57
+ }
58
+ }
vocab.json ADDED
The diff for this file is too large to render. See raw diff
 
vocabulary.json ADDED
The diff for this file is too large to render. See raw diff