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- .gitattributes +25 -0
- LICENSE.md +39 -0
- README.md +86 -1
- loras/YuE2-instrumental-cot-full-loras/.gitattributes +35 -0
- loras/YuE2-instrumental-cot-full-loras/README.md +92 -0
- loras/YuE2-instrumental-cot-full-loras/scripts/ar_generate.py +38 -0
- loras/YuE2-instrumental-cot-full-loras/scripts/ckpt_io.py +20 -0
- models/SheetSage2-F32.gguf +3 -0
- models/SheetSage2-Q8_0.gguf +3 -0
- models/YuE2-3B-BF16.gguf +3 -0
- models/YuE2-3B-Q5_K_M.gguf +3 -0
- models/YuE2-3B-Q6_K.gguf +3 -0
- models/YuE2-3B-Q8_0.gguf +3 -0
- models/YuE2-Vae-F32.gguf +3 -0
- models/YuE2-Vae-blend-F32.gguf +3 -0
- models/YuE2-Vae-legacy-F32.gguf +3 -0
- separation/demucs/htdemucs_ft.yaml +7 -0
- sliders/acoustic-folk.gguf +3 -0
- sliders/afrobeats.gguf +3 -0
- sliders/catalog.json +123 -0
- sliders/country.gguf +3 -0
- sliders/disco-funk.gguf +3 -0
- sliders/female.gguf +3 -0
- sliders/hiphop.gguf +3 -0
- sliders/house.gguf +3 -0
- sliders/indie-rock.gguf +3 -0
- sliders/kpop.gguf +3 -0
- sliders/lofi.gguf +3 -0
- sliders/male.gguf +3 -0
- sliders/metal.gguf +3 -0
- sliders/pop-punk.gguf +3 -0
- sliders/pop.gguf +3 -0
- sliders/reggaeton.gguf +3 -0
- sliders/rnb.gguf +3 -0
- universr/config.yaml +37 -0
- universr/pytorch_model.bin +3 -0
- whisper/whisper-large-v3-ct2-float16/.gitattributes +35 -0
- whisper/whisper-large-v3-ct2-float16/README.md +566 -0
- whisper/whisper-large-v3-ct2-float16/added_tokens.json +1611 -0
- whisper/whisper-large-v3-ct2-float16/config.json +240 -0
- whisper/whisper-large-v3-ct2-float16/generation_config.json +264 -0
- whisper/whisper-large-v3-ct2-float16/merges.txt +0 -0
- whisper/whisper-large-v3-ct2-float16/model.bin +3 -0
- whisper/whisper-large-v3-ct2-float16/model.safetensors.index.json +0 -0
- whisper/whisper-large-v3-ct2-float16/normalizer.json +1742 -0
- whisper/whisper-large-v3-ct2-float16/preprocessor_config.json +14 -0
- whisper/whisper-large-v3-ct2-float16/special_tokens_map.json +133 -0
- whisper/whisper-large-v3-ct2-float16/tokenizer.json +0 -0
- whisper/whisper-large-v3-ct2-float16/tokenizer_config.json +0 -0
- whisper/whisper-large-v3-ct2-float16/vocab.json +0 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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models/YuE2-3B-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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models/YuE2-3B-BF16.gguf filter=lfs diff=lfs merge=lfs -text
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models/SheetSage2-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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models/YuE2-3B-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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models/SheetSage2-F32.gguf filter=lfs diff=lfs merge=lfs -text
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models/YuE2-Vae-blend-F32.gguf filter=lfs diff=lfs merge=lfs -text
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models/YuE2-3B-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
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models/YuE2-Vae-F32.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/acoustic-folk.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/house.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/disco-funk.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/kpop.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/reggaeton.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/afrobeats.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/lofi.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/female.gguf filter=lfs diff=lfs merge=lfs -text
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models/YuE2-Vae-legacy-F32.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/male.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/pop.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/hiphop.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/rnb.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/indie-rock.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/pop-punk.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/metal.gguf filter=lfs diff=lfs merge=lfs -text
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sliders/country.gguf filter=lfs diff=lfs merge=lfs -text
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LICENSE.md
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# Licences of the files in this repository
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Each file keeps the licence of its source. This repository adds nothing of its own to them.
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| files | licence | source | licence text |
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|---|---|---|---|
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| `models/YuE2-3B-*.gguf` | CC BY-NC 4.0 | m-a-p/YuE2-3B, converted by Serveurperso | https://creativecommons.org/licenses/by-nc/4.0/legalcode |
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| `models/YuE2-Vae-F32.gguf` | CC BY-NC 4.0 | m-a-p/YuE2-Vae | as above |
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| `models/YuE2-Vae-legacy-F32.gguf` | CC BY-NC 4.0 | m-a-p/YuE2-Vae-legacy | as above |
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| `models/YuE2-Vae-blend-F32.gguf` | CC BY-NC 4.0 | Mothersuperior/YuE2-Vae-merge-0.666 | as above |
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| `models/SheetSage2-*.gguf` | CC BY-NC 4.0 | m-a-p/SheetSage2, m-a-p/MERT-v2-FullSong | as above |
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| `sliders/*` | CC BY-NC 4.0 | ntc-ai/yue2-particle-sliders | as above |
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| `loras/YuE2-instrumental-cot-full-loras/*` | CC BY-NC 4.0 | Mothersuperior/YuE2-instrumental-cot-full-loras | as above |
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| `whisper/whisper-large-v3-ct2-float16/*` | Apache-2.0 | openai/whisper-large-v3 (converted to CTranslate2) | https://www.apache.org/licenses/LICENSE-2.0 |
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| `separation/demucs/*` | MIT | facebookresearch/demucs | below |
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| `universr/*` | CC BY 4.0 | woongzip1/universr-audio | https://creativecommons.org/licenses/by/4.0/legalcode |
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Changes: the GGUF files are format conversions of the original weights (yue2.cpp's converters);
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the Whisper files are a CTranslate2 float16 conversion of openai/whisper-large-v3. Nothing else was
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changed.
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## MIT License (Demucs)
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Copyright (c) Meta Platforms, Inc. and affiliates.
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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
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associated documentation files (the "Software"), to deal in the Software without restriction,
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including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense,
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and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
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subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all copies or substantial
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portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT
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LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO
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EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
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IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR
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THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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README.md
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---
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license:
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---
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---
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license: other
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license_name: mixed-non-commercial
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license_link: LICENSE.md
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tags:
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- music-generation
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- yue2
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- gguf
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- text-to-music
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- speech
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library_name: ggml
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pipeline_tag: text-to-audio
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---
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# Ruach Studio · Models
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Every weight Ruach Studio (its code repository opens with the release) uses, in one repository, laid out
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exactly as the studio's own folders, so one command puts each file where the studio looks for it:
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```bash
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cd Ruach_Studio # the root of the studio
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hf download goldhub/Ruach_Studio_Models --local-dir .
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```
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or only what you need:
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```bash
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hf download goldhub/Ruach_Studio_Models --local-dir . --include "models/YuE2-3B-Q8_0.gguf" "models/YuE2-Vae-F32.gguf"
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```
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**Nothing here is ours.** These are the works of the people and teams credited below, re-hosted
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unchanged (the GGUF files as converted by yue2.cpp's tools, byte for byte the same as their sources'),
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so that a studio install does not have to visit a dozen repositories. Every file keeps its own
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licence; most are **non-commercial** (CC BY-NC 4.0). Using this repository you accept each licence
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of the files you take. If you are an author and want a file of yours taken down, open a discussion
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here and it goes.
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---
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## What is here
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| folder | files | size | for | source | licence |
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|---|---|---|---|---|---|
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| `models/` | `YuE2-3B-BF16.gguf` | 6.7 GB | the music model (24 GB GPUs) | [Serveurperso/YuE2-GGUF](https://huggingface.co/Serveurperso/YuE2-GGUF), from [m-a-p/YuE2-3B](https://huggingface.co/m-a-p/YuE2-3B) | CC BY-NC 4.0 |
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| `models/` | `YuE2-3B-Q8_0` · `Q6_K` · `Q5_K_M.gguf` | 3.6 · 2.8 · 2.5 GB | the same, quantized (12–16 GB GPUs) | as above | CC BY-NC 4.0 |
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| `models/` | `YuE2-Vae-F32.gguf` | 0.5 GB | the sound decoder (default) | [m-a-p/YuE2-Vae](https://huggingface.co/m-a-p/YuE2-Vae), converted | CC BY-NC 4.0 |
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| `models/` | `YuE2-Vae-legacy-F32.gguf` | 0.5 GB | the first decoder | [m-a-p/YuE2-Vae-legacy](https://huggingface.co/m-a-p/YuE2-Vae-legacy), converted | CC BY-NC 4.0 |
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| `models/` | `YuE2-Vae-blend-F32.gguf` | 0.5 GB | a 0.666 merge of the two | [Mothersuperior/YuE2-Vae-merge-0.666](https://huggingface.co/Mothersuperior/YuE2-Vae-merge-0.666), converted | CC BY-NC 4.0 |
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| `models/` | `SheetSage2-F32.gguf` · `SheetSage2-Q8_0.gguf` | 2.6 · 0.9 GB | audio → score (covers) | [m-a-p/SheetSage2](https://huggingface.co/m-a-p/SheetSage2) + [m-a-p/MERT-v2-FullSong](https://huggingface.co/m-a-p/MERT-v2-FullSong), converted | CC BY-NC 4.0 |
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| `sliders/` | 16 sliders + `catalog.json` | 0.2 GB | voice and genre sliders | [ntc-ai/yue2-particle-sliders](https://huggingface.co/ntc-ai/yue2-particle-sliders), converted | CC BY-NC 4.0 |
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| `loras/YuE2-instrumental-cot-full-loras/` | `ar_lora_inst_v3abc.safetensors` + its README and scripts | 0.3 GB | instrumental pieces | [Mothersuperior/YuE2-instrumental-cot-full-loras](https://huggingface.co/Mothersuperior/YuE2-instrumental-cot-full-loras) | CC BY-NC 4.0 |
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| `whisper/whisper-large-v3-ct2-float16/` | CTranslate2 float16 | 2.9 GB | the lyrics check, karaoke timing, trim to the text | [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3), converted with CTranslate2 | Apache-2.0 |
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| `separation/demucs/` | `htdemucs_ft` (4 models + yaml) | 0.3 GB | drums, bass, other, vocals | [facebookresearch/demucs](https://github.com/facebookresearch/demucs), as fetched by [python-audio-separator](https://github.com/nomadkaraoke/python-audio-separator) | MIT |
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| `universr/` | `pytorch_model.bin`, `config.yaml` | 0.2 GB | upscale: redraws the top of the spectrum | [woongzip1/universr-audio](https://huggingface.co/woongzip1/universr-audio), code [woongzip1/UniverSR](https://github.com/woongzip1/UniverSR) | CC BY 4.0 |
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### Not here, and why
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| what | why | where to get it |
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|---|---|---|
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| BS-Roformer ep317 (vocal stems) | its licence is not stated by its author | fetched by audio-separator at first use, or `heresy/fetch-heresy.sh` |
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| `yue2-industrial-rock-lora`, `yue2-steps-from-hell` (monsterovich), `YuE2_Deathmetalv1_lora` (pduncan) | no licence given: all rights stay with the authors | their repositories, through the studio's `tools/download-loras.sh` |
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| the raw checkpoints (`m-a-p/YuE2-3B` and the rest) | only needed to convert again | their repositories, `tools/download-checkpoints.sh` |
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---
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## Licences in short
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- **CC BY-NC 4.0** — share and adapt, with credit, **not for commercial purposes**. This covers YuE2,
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its decoders, SheetSage2, MERT, the sliders and the LoRA here. Music made with them falls under the
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terms their authors set; read their cards.
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- **Apache-2.0** — Whisper large-v3 (OpenAI).
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- **MIT** — Demucs (Meta AI Research).
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- **CC BY 4.0** — UniverSR weights.
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The full texts and every file's source are in [LICENSE.md](LICENSE.md).
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## Credits
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- **YuE2, its decoders, SheetSage2, MERT** — the Multimodal Art Projection team ([m-a-p](https://huggingface.co/m-a-p)).
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- **GGUF conversions** — Serveurperso ([yue2.cpp](https://github.com/ServeurpersoCom/yue2.cpp), [YuE2-GGUF](https://huggingface.co/Serveurperso/YuE2-GGUF)).
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- **The YuE2 Kit** these files were gathered by — IronWolve ([yue2-kit](https://github.com/IronWolve/yue2-kit)).
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- **VAE merge, instrumental LoRA** — Mothersuperior.
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- **Particle sliders** — ntc-ai.
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- **Whisper** — OpenAI; CTranslate2 and faster-whisper — SYSTRAN.
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- **Demucs** — Meta AI Research; **python-audio-separator** — nomadkaraoke.
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- **UniverSR** — woongzip1.
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Gathered for Ruach Studio by Viktor Zhuromskyy (ЙирмиЙа́Ѓу) with Claude (Anthropic) · HERETICAL TANDEM™.
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loras/YuE2-instrumental-cot-full-loras/.gitattributes
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loras/YuE2-instrumental-cot-full-loras/README.md
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| 1 |
+
---
|
| 2 |
+
license: cc-by-nc-4.0
|
| 3 |
+
base_model:
|
| 4 |
+
- m-a-p/YuE2-3B
|
| 5 |
+
tags:
|
| 6 |
+
- audio
|
| 7 |
+
- music
|
| 8 |
+
- yue2
|
| 9 |
+
- lora
|
| 10 |
+
- instrumental
|
| 11 |
+
---
|
| 12 |
+
# YuE2-instrumental-cot-full-loras
|
| 13 |
+
|
| 14 |
+
AR-branch LoRA for [YuE2-3B](https://huggingface.co/m-a-p/YuE2-3B) that makes the model write **instrumental** music with a **section plan**.
|
| 15 |
+
Rank 64 on `self_attn.{q,k,v,o}_proj` + `mlp.{gate,up,down}_proj` of all 28 layers, trained on ~2.7k instrumental tracks paired with
|
| 16 |
+
chord-annotated ABC scores from [SheetSage2](https://huggingface.co/m-a-p/SheetSage2), regularized 50/50 with YuE2's own generations.
|
| 17 |
+
|
| 18 |
+
## Use with `cot="full"`
|
| 19 |
+
This LoRA is meant to run with YuE2's chain-of-thought **on** (`cot="full"`): the model first writes its own ABC score, then the music tokens
|
| 20 |
+
conditioned on that score. It was trained with the score present half the time and absent half the time, so `cot="off"` still works, but in our
|
| 21 |
+
renders the score-first path ended songs on its own 8 times out of 9 and sounded more deliberate, while the score-free path either bailed early or ran
|
| 22 |
+
to the length cap. Set `cot="full"` in the pipeline call (ComfyUI: the `mode` input of the YuE2 nodes with a non-empty ABC step; our script: `COT=full`).
|
| 23 |
+
|
| 24 |
+
## Files
|
| 25 |
+
| file | what |
|
| 26 |
+
|---|---|
|
| 27 |
+
| `ar_lora_inst_v3abc.safetensors` | fp32, 392 named tensors: `layers.{i}.self_attn.{q,k,v,o}_proj.lora_{A,B}`, `layers.{i}.mlp.{gate,up,down}_proj.lora_{A,B}`. Apply `W += lora_B @ lora_A`, scale 1.0. |
|
| 28 |
+
| `ar_lora_inst_v3abc.bf16.safetensors` | same weights in bf16 (half size). |
|
| 29 |
+
| `ar_lora_inst_v3abc_comfyui.safetensors` | ComfyUI-native layout (fused qkv / gate_up keys), for the LoraLoader CLIP slot. |
|
| 30 |
+
| `scripts/ar_generate.py`, `scripts/ckpt_io.py` | reference merge-and-generate script (folds the LoRA into the base weights, then runs YuE2's stock pipeline). |
|
| 31 |
+
|
| 32 |
+
Works with the stock NAR decoder. For real-production sounding output pair it with the NAR LoRA and tokenizer from
|
| 33 |
+
[yue2-mothersuperior-realaudio-tokenizer-v4](https://huggingface.co/Mothersuperior/yue2-mothersuperior-realaudio-tokenizer-v4) (our renders used that NAR LoRA).
|
| 34 |
+
|
| 35 |
+
## ComfyUI
|
| 36 |
+
`ar_lora_inst_v3abc_comfyui.safetensors` is the same LoRA in ComfyUI's native YuE2 layout. In ComfyUI the AR planner lives in the **CLIP** slot, so
|
| 37 |
+
load this file with the standard **LoraLoader** and connect its **CLIP** output onward (strength 1.0); the MODEL output is untouched by this file.
|
| 38 |
+
Set the YuE2 Generate nodes' `mode` to **full** and connect the ABC node so the model writes its score first. ComfyUI fuses q/k/v into
|
| 39 |
+
`text_encoders.model.layers.{i}.self_attn.qkv_proj` and gate/up into `mlp.gate_up_proj`; this file packs the separate LoRAs block-diagonally into
|
| 40 |
+
those fused keys (`lora_up`/`lora_down`, no alpha = scale 1.0; `up @ down` equals the stacked deltas exactly). Pair with the NAR LoRA from
|
| 41 |
+
[yue2-mothersuperior-realaudio-tokenizer-v4](https://huggingface.co/Mothersuperior/yue2-mothersuperior-realaudio-tokenizer-v4) (`nar_lora_joint_v4_comfyui.safetensors`, MODEL slot) for real-production sound.
|
| 42 |
+
|
| 43 |
+
## Prompting: three caption styles
|
| 44 |
+
The **style** field is a normal YuE2 tag string (genre, instruments, mood, BPM). The **lyrics** field carries the structure, in one of three forms the
|
| 45 |
+
LoRA was trained on in equal thirds. Use only these bare tag names: `intro`, `verse`, `pre-chorus`, `chorus`, `bridge`, `outro`. One tag per line,
|
| 46 |
+
real line breaks, nothing else in the brackets.
|
| 47 |
+
|
| 48 |
+
**1. Bare** — let the model choose the structure and length.
|
| 49 |
+
```
|
| 50 |
+
[instrumental]
|
| 51 |
+
```
|
| 52 |
+
|
| 53 |
+
**2. Untimed tags** — you choose the section order, the model chooses the timing.
|
| 54 |
+
```
|
| 55 |
+
[intro]
|
| 56 |
+
[verse]
|
| 57 |
+
[chorus]
|
| 58 |
+
[bridge]
|
| 59 |
+
[chorus]
|
| 60 |
+
[outro]
|
| 61 |
+
```
|
| 62 |
+
|
| 63 |
+
**3. Timed tags** — you also give each section a start and end in `m:ss`. The model was trained with exact section times from real tracks, so this is the
|
| 64 |
+
strongest structural steer. Treat the times as a guide rather than a guarantee: the model follows the section order and proportions better than the
|
| 65 |
+
absolute end time, and it tends toward 3–5 minute songs regardless of the plan.
|
| 66 |
+
```
|
| 67 |
+
[intro 0:00-0:15]
|
| 68 |
+
[verse 0:15-0:45]
|
| 69 |
+
[chorus 0:45-1:10]
|
| 70 |
+
[bridge 1:10-1:40]
|
| 71 |
+
[chorus 1:40-2:05]
|
| 72 |
+
[outro 2:05-2:30]
|
| 73 |
+
```
|
| 74 |
+
|
| 75 |
+
Keep production notes (`[guitar solo, distorted]`), lyrics text, and literal `\n` characters out of the lyrics field: the LoRA never saw them and they
|
| 76 |
+
pull the output away from the trained behavior.
|
| 77 |
+
|
| 78 |
+
## Example
|
| 79 |
+
```bash
|
| 80 |
+
COT=full STYLE_TEXT="dark ambient, water phone, theremin, piano, slow" \
|
| 81 |
+
python scripts/ar_generate.py ar_lora_inst_v3abc.safetensors none out_tag any_style_track plan.txt 21
|
| 82 |
+
```
|
| 83 |
+
`plan.txt` holds one of the three caption forms above. `AR_SCALE` (default 1.0) scales the LoRA delta; lower values give the base model more say.
|
| 84 |
+
|
| 85 |
+
## Training notes
|
| 86 |
+
lr 6e-5, 5,000 steps, rank 64, one song per step with 2-step gradient accumulation; 50% of steps use the score-conditioned layout (`cot=full`) and 50% the
|
| 87 |
+
score-free layout with a separate score-writing sequence, so the model always learns to write the score; an auxiliary section-cursor loss (frame → active
|
| 88 |
+
tag line) and an up-weighted END token. Held-out loss on unseen instrumentals fell from 5.33 to 4.51 (score-free) and 3.64 to 2.92 (score-conditioned)
|
| 89 |
+
with no overfitting turn. About 5% of tracks were held out and never trained on.
|
| 90 |
+
|
| 91 |
+
## License
|
| 92 |
+
Weights derive from YuE2-3B (CC BY-NC 4.0): non-commercial use only. Provided as-is.
|
loras/YuE2-instrumental-cot-full-loras/scripts/ar_generate.py
ADDED
|
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|
| 1 |
+
"""Generate songs with AR LoRA + joint_v1 NAR LoRA through YuE2's own pipeline (cot=off).
|
| 2 |
+
usage: ar_generate.py <ar_lora.pt> <nar_lora.pt> <out_tag> <style_from_track_name> <lyrics_file> <seed>"""
|
| 3 |
+
import sys, os as _os; sys.path.insert(0,_os.path.dirname(_os.path.abspath(__file__))); from ckpt_io import load_ckpt
|
| 4 |
+
import os, sys, glob, math, json, numpy as np, torch, torch.nn as nn
|
| 5 |
+
os.environ.setdefault("HF_HOME","/workspace/hf")
|
| 6 |
+
from yue2 import YuE2Pipeline
|
| 7 |
+
AR_CK,NAR_CK,TAG,STYLE_TRACK,LYR_FILE,SEED=sys.argv[1:7]; OUT="/workspace/tok/full/gen"; os.makedirs(OUT,exist_ok=True); dev="cuda"
|
| 8 |
+
pipe=YuE2Pipeline.from_pretrained("m-a-p/YuE2-3B", vae="m-a-p/YuE2-Vae"); model=pipe._load_model(); bb=model.model
|
| 9 |
+
class LoRALinear(nn.Module):
|
| 10 |
+
def __init__(s, base, r):
|
| 11 |
+
super().__init__(); s.base=base; s.A=nn.Parameter(torch.zeros(r, base.in_features, device=base.weight.device)); s.B=nn.Parameter(torch.zeros(base.out_features, r, device=base.weight.device))
|
| 12 |
+
def forward(s,x): return s.base(x)+((x.float()@s.A.T)@s.B.T).to(x.dtype)
|
| 13 |
+
def merge(attn_name, mlp_name, tensors, scale=1.0):
|
| 14 |
+
"""Fold LoRA deltas (W += scale*B@A) into the base nn.Linear weights so the pipeline's CUDA-graph sampler keeps working."""
|
| 15 |
+
it=iter(tensors); n_merged=0
|
| 16 |
+
for layer in bb.layers:
|
| 17 |
+
for mod,names in ((getattr(layer,attn_name),("q_proj","k_proj","v_proj","o_proj")),(getattr(layer,mlp_name),("gate_proj","up_proj","down_proj"))):
|
| 18 |
+
for n in names:
|
| 19 |
+
A=next(it).to(dev).float(); B=next(it).to(dev).float(); lin=getattr(mod,n); lin.weight.add_((scale*(B@A)).to(lin.weight.dtype)); n_merged+=1
|
| 20 |
+
return n_merged
|
| 21 |
+
with torch.no_grad():
|
| 22 |
+
AR_SCALE=float(os.environ.get("AR_SCALE","1.0"))
|
| 23 |
+
if AR_CK!="none": ar=load_ckpt(AR_CK,dev); print(f"merged AR linears (scale {AR_SCALE}):", merge("self_attn","mlp",ar["lora"],AR_SCALE), flush=True)
|
| 24 |
+
else: print("AR: stock (no LoRA)", flush=True)
|
| 25 |
+
if NAR_CK!="none":
|
| 26 |
+
nar=load_ckpt(NAR_CK,dev); print("merged NAR linears:", merge("nar_self_attn","nar_mlp",nar["lora"]), flush=True)
|
| 27 |
+
model.vae2llm.load_state_dict({k:v.to(torch.bfloat16) for k,v in nar["io"]["vae2llm"].items()}); model.llm2vae.load_state_dict({k:v.to(torch.bfloat16) for k,v in nar["io"]["llm2vae"].items()})
|
| 28 |
+
else: print("NAR: stock (no LoRA)", flush=True)
|
| 29 |
+
model.eval(); print("LoRAs loaded", flush=True)
|
| 30 |
+
import re
|
| 31 |
+
cap=open(f"/workspace/real/artist/{STYLE_TRACK}.txt").read().split("===LYRICS===")[0].replace("Global Metadata:","").strip(); style=" ".join(cap.split())[:1500]
|
| 32 |
+
if os.environ.get("STRIP_TEMPO_KEY"): style=re.sub(r",?\s*\d+\s*BPM,?\s*(key of [A-G][#b]? ?(major|minor)?)?,?","",style).replace(" "," ")
|
| 33 |
+
lyrics=open(LYR_FILE).read().strip()
|
| 34 |
+
ABC_FILE=os.environ.get("ABC_FILE"); COT=os.environ.get("COT","off"); kw={}
|
| 35 |
+
if ABC_FILE: kw["abc"]=open(ABC_FILE).read(); COT=os.environ.get("COT","melody"); print(f"cover mode: cot={COT} abc chars {len(kw['abc'])}", flush=True)
|
| 36 |
+
res=pipe(style=style, lyrics=lyrics, cot=COT, seed=int(SEED), id=TAG, **kw); res.save(f"{OUT}/{TAG}.flac"); np.save(f"{OUT}/{TAG}_tokens.npy", np.asarray(res.semantic.tokens,dtype=np.int32))
|
| 37 |
+
json.dump({"style":style,"lyrics":lyrics,"seed":int(SEED),"cot":COT,"abc_file":ABC_FILE,"ar":AR_CK,"ar_scale":AR_SCALE,"nar":NAR_CK,"audio_seconds":len(res.audio)/48000}, open(f"{OUT}/{TAG}.json","w"), indent=1)
|
| 38 |
+
print(f"GEN DONE {TAG} {len(res.audio)/48000:.1f}s tokens {len(res.semantic.tokens)}", flush=True)
|
loras/YuE2-instrumental-cot-full-loras/scripts/ckpt_io.py
ADDED
|
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|
| 1 |
+
"""Load our checkpoints from either the original .pt files or the .safetensors releases, returning the same dict layout the scripts expect.
|
| 2 |
+
head : {"model": state_dict, "cfg": {...}}
|
| 3 |
+
NAR LoRA: {"lora": [A0,B0,...] (layer-major: nar_self_attn q,k,v,o then nar_mlp gate,up,down), "io": {"vae2llm": sd, "llm2vae": sd}, "rank": int}
|
| 4 |
+
AR LoRA : {"lora": [A0,B0,...] (layer-major: self_attn q,k,v,o then mlp gate,up,down), "rank": int}"""
|
| 5 |
+
import torch
|
| 6 |
+
def _mods(prefix): return [(f"{prefix}self_attn",n) for n in ("q_proj","k_proj","v_proj","o_proj")]+[(f"{prefix}mlp",n) for n in ("gate_proj","up_proj","down_proj")]
|
| 7 |
+
def load_ckpt(path, map_location="cpu"):
|
| 8 |
+
if not str(path).endswith(".safetensors"): return torch.load(path, map_location=map_location, weights_only=False)
|
| 9 |
+
from safetensors.torch import load_file; from safetensors import safe_open
|
| 10 |
+
t=load_file(path, device=str(map_location))
|
| 11 |
+
with safe_open(path, "pt") as f: meta=f.metadata() or {}
|
| 12 |
+
if any(k.endswith(".lora_A") for k in t):
|
| 13 |
+
prefix="nar_" if any(".nar_self_attn." in k for k in t) else ""
|
| 14 |
+
layers=sorted({int(k.split(".")[1]) for k in t if k.startswith("layers.")}); lora=[]
|
| 15 |
+
for L in layers:
|
| 16 |
+
for blk,proj in _mods(prefix): lora+=[t[f"layers.{L}.{blk}.{proj}.lora_A"], t[f"layers.{L}.{blk}.{proj}.lora_B"]]
|
| 17 |
+
out={"lora":lora, "rank":int(meta.get("rank", lora[0].shape[0]))}
|
| 18 |
+
if prefix: out["io"]={m:{k.split(".",1)[1]:v for k,v in t.items() if k.startswith(m+".")} for m in ("vae2llm","llm2vae")}
|
| 19 |
+
return out
|
| 20 |
+
return {"model":t, "cfg":{"instnorm": meta.get("input","").find("instnorm=true")>=0}}
|
models/SheetSage2-F32.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8a801a08cd27270aa0caffd0d1446907856f356ad1b044a3b3b3d8be231881e5
|
| 3 |
+
size 2708176640
|
models/SheetSage2-Q8_0.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4507d8c1d9245f312c0894ea610ab18fbcbf31443764b5bd6a60e4b6df59e973
|
| 3 |
+
size 957571488
|
models/YuE2-3B-BF16.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:668ca9ffa4622449916aae5068b5737363e7ec186a824cd3d7a1a8a95269f166
|
| 3 |
+
size 7166072352
|
models/YuE2-3B-Q5_K_M.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cd3efd250b734a229172800f08f33a893ad1f66b521666e176cdae4a0729b281
|
| 3 |
+
size 2622936832
|
models/YuE2-3B-Q6_K.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:42068d5e57713df11b9f6a5a3751072d9bbefe231d1d088f9c100e026ffa4ef9
|
| 3 |
+
size 2943331072
|
models/YuE2-3B-Q8_0.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:41121ce97786d7795a325bcf123ca196956c03bb252c9e75384cfc1f2fc19e6b
|
| 3 |
+
size 3810232064
|
models/YuE2-Vae-F32.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:93e49dfb1970e89ad64cacb17cf13b5d05f6bb30ef7ed3adae3050bcb728638a
|
| 3 |
+
size 530497344
|
models/YuE2-Vae-blend-F32.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:157b07f1487e6c58d4f9cf7589a9c75be88af7ecd1b63ef5f96cb6987043bc4d
|
| 3 |
+
size 530497344
|
models/YuE2-Vae-legacy-F32.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b632c2e607e7f1d4c5369c8a943f7f6db55ab7dbf8c7ec35221d4d786d80afc8
|
| 3 |
+
size 530497344
|
separation/demucs/htdemucs_ft.yaml
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
models: ['f7e0c4bc', 'd12395a8', '92cfc3b6', '04573f0d']
|
| 2 |
+
weights: [
|
| 3 |
+
[1., 0., 0., 0.],
|
| 4 |
+
[0., 1., 0., 0.],
|
| 5 |
+
[0., 0., 1., 0.],
|
| 6 |
+
[0., 0., 0., 1.],
|
| 7 |
+
]
|
sliders/acoustic-folk.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2d631c558eecba9826ed862f402d89ecd5f75d4ebb46d3076ac907f5cec78840
|
| 3 |
+
size 15851520
|
sliders/afrobeats.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d23acd922ee081793fb17b2abaa006fea7063963b0513e2d84c37d57ae6c2c59
|
| 3 |
+
size 15851520
|
sliders/catalog.json
ADDED
|
@@ -0,0 +1,123 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"source": "ntc-ai/yue2-particle-sliders",
|
| 3 |
+
"release": "particle-gmix-1600-v2",
|
| 4 |
+
"experimental": true,
|
| 5 |
+
"recommended_range": [
|
| 6 |
+
0,
|
| 7 |
+
1
|
| 8 |
+
],
|
| 9 |
+
"sliders": [
|
| 10 |
+
{
|
| 11 |
+
"id": "female",
|
| 12 |
+
"label": "Female",
|
| 13 |
+
"description": "One adult female lead with clear melodic phrasing",
|
| 14 |
+
"file": "female.gguf",
|
| 15 |
+
"source_sha256": "1daba0682dcaf15d05d08637d5ccca7a1f157d298f8f491dbc6ad712ff0d0ead"
|
| 16 |
+
},
|
| 17 |
+
{
|
| 18 |
+
"id": "male",
|
| 19 |
+
"label": "Male",
|
| 20 |
+
"description": "One adult male lead with clear melodic phrasing",
|
| 21 |
+
"file": "male.gguf",
|
| 22 |
+
"source_sha256": "c916db3511d8c0f436900785b722dd65b9f5a4195649d0f06ef3c807dd4527ad"
|
| 23 |
+
},
|
| 24 |
+
{
|
| 25 |
+
"id": "pop",
|
| 26 |
+
"label": "Pop",
|
| 27 |
+
"description": "Clear hooks, crisp drums and a polished chorus",
|
| 28 |
+
"file": "pop.gguf",
|
| 29 |
+
"source_sha256": "36ece86ff462cc879d12b55dc0ee7ffdc8776e2fed1c9136e0772648aee7bf02"
|
| 30 |
+
},
|
| 31 |
+
{
|
| 32 |
+
"id": "hiphop",
|
| 33 |
+
"label": "Hip-Hop",
|
| 34 |
+
"description": "Rapped verses, deep sub bass and nimble hats",
|
| 35 |
+
"file": "hiphop.gguf",
|
| 36 |
+
"source_sha256": "fc1104e34eb86fe86b918246358b8cb5e05e3a6e1a6d589e6e3bffff845392f0"
|
| 37 |
+
},
|
| 38 |
+
{
|
| 39 |
+
"id": "rnb",
|
| 40 |
+
"label": "R&B",
|
| 41 |
+
"description": "Warm keys, deep pocket and fluid vocal phrasing",
|
| 42 |
+
"file": "rnb.gguf",
|
| 43 |
+
"source_sha256": "b68802ad79352e3c66f3ce13ae5b7eaaca2e24f4bb307276589250db25acbdda"
|
| 44 |
+
},
|
| 45 |
+
{
|
| 46 |
+
"id": "indie-rock",
|
| 47 |
+
"label": "Indie Rock",
|
| 48 |
+
"description": "Chiming guitars, moving bass and a human drum kit",
|
| 49 |
+
"file": "indie-rock.gguf",
|
| 50 |
+
"source_sha256": "d3dabcf0b20f32c680394b72af565058099d5fb9c0aab969b96bb224a5cf5067"
|
| 51 |
+
},
|
| 52 |
+
{
|
| 53 |
+
"id": "pop-punk",
|
| 54 |
+
"label": "Pop Punk",
|
| 55 |
+
"description": "Palm-muted power chords and driving chorus drums",
|
| 56 |
+
"file": "pop-punk.gguf",
|
| 57 |
+
"source_sha256": "c7a1978d5a1d896a74645ba1296f664877090e9b796faf359e1e95a675c1bab5"
|
| 58 |
+
},
|
| 59 |
+
{
|
| 60 |
+
"id": "metal",
|
| 61 |
+
"label": "Metal",
|
| 62 |
+
"description": "Heavy guitar riffs, tight kicks and big melodic choruses",
|
| 63 |
+
"file": "metal.gguf",
|
| 64 |
+
"source_sha256": "d7b9907b46d573f20bc5919f49164648356458b219df4b81f248d0399f492aac"
|
| 65 |
+
},
|
| 66 |
+
{
|
| 67 |
+
"id": "country",
|
| 68 |
+
"label": "Country",
|
| 69 |
+
"description": "Acoustic strum, twangy fills and an easy backbeat",
|
| 70 |
+
"file": "country.gguf",
|
| 71 |
+
"source_sha256": "aa9cd5a4d7c5978d24d6260e0e2dc84027eb55884b6ce7126704d278cdb574d4"
|
| 72 |
+
},
|
| 73 |
+
{
|
| 74 |
+
"id": "acoustic-folk",
|
| 75 |
+
"label": "Acoustic Folk",
|
| 76 |
+
"description": "Fingerpicked strings and a warm small-room performance",
|
| 77 |
+
"file": "acoustic-folk.gguf",
|
| 78 |
+
"source_sha256": "12967b7681f44d2aa7a3f4e2539d4a1e8615b38c2025292165dbd6c8c5407dbc"
|
| 79 |
+
},
|
| 80 |
+
{
|
| 81 |
+
"id": "house",
|
| 82 |
+
"label": "House",
|
| 83 |
+
"description": "Steady club kick, offbeat hats and a rolling bass line",
|
| 84 |
+
"file": "house.gguf",
|
| 85 |
+
"source_sha256": "aa010f666d8f097fc9a1567f285b5a9ca96504e588e59c351f35c13f78b6d9d8"
|
| 86 |
+
},
|
| 87 |
+
{
|
| 88 |
+
"id": "disco-funk",
|
| 89 |
+
"label": "Disco Funk",
|
| 90 |
+
"description": "Elastic bass, clipped guitar and bright dance-floor strings",
|
| 91 |
+
"file": "disco-funk.gguf",
|
| 92 |
+
"source_sha256": "5141cdf3d4697f829eacc7ffab535228aa94f3d2ca9804def17abc931fc8f932"
|
| 93 |
+
},
|
| 94 |
+
{
|
| 95 |
+
"id": "kpop",
|
| 96 |
+
"label": "K-pop",
|
| 97 |
+
"description": "Sharp synth hooks, tight edits and a big chorus lift",
|
| 98 |
+
"file": "kpop.gguf",
|
| 99 |
+
"source_sha256": "aa798a14da2d49670c18e6cc45a0edcf0a6c56895b7e7126adb03218778c8aa9"
|
| 100 |
+
},
|
| 101 |
+
{
|
| 102 |
+
"id": "reggaeton",
|
| 103 |
+
"label": "Reggaeton",
|
| 104 |
+
"description": "Dembow drums, rounded sub bass and clipped melodic hooks",
|
| 105 |
+
"file": "reggaeton.gguf",
|
| 106 |
+
"source_sha256": "4fe2e1d879d824ea980fe4340dd6bcf8d3e64bca5474b80833f818ffd6152381"
|
| 107 |
+
},
|
| 108 |
+
{
|
| 109 |
+
"id": "afrobeats",
|
| 110 |
+
"label": "Afrobeats",
|
| 111 |
+
"description": "Interlocking percussion, melodic bass and buoyant guitar",
|
| 112 |
+
"file": "afrobeats.gguf",
|
| 113 |
+
"source_sha256": "aff7892d6cb7f6302f8d043f6611c1db97c4dea54c30ad32815a8f46fbc45a27"
|
| 114 |
+
},
|
| 115 |
+
{
|
| 116 |
+
"id": "lofi",
|
| 117 |
+
"label": "Lo-fi",
|
| 118 |
+
"description": "Soft swung drums, mellow keys and gentle tape warmth",
|
| 119 |
+
"file": "lofi.gguf",
|
| 120 |
+
"source_sha256": "664fa47cede00f63e63ef8fe1ef56bfdbda9d34a61a8d1cdffb863d551af5bdf"
|
| 121 |
+
}
|
| 122 |
+
]
|
| 123 |
+
}
|
sliders/country.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d8f05ca539755bf94e250c0e24065d10014b873273d8433b0c4d1442177281a5
|
| 3 |
+
size 15851488
|
sliders/disco-funk.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a82a124d46eed958919e6e70ed99d28e2a59d04253ac165ee73923b270e275fb
|
| 3 |
+
size 15851520
|
sliders/female.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7e85e3bbe6de48df9ced1c60ee8c080b45035d750ed0329d47ddf65e92bea3e7
|
| 3 |
+
size 15851488
|
sliders/hiphop.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:15ad090a15e32aea1064cb5ea992e30c79eef5192b7bfa90e07d2d1a88aa5ae2
|
| 3 |
+
size 15851488
|
sliders/house.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d7e5d93bd700db440155639eb5024f55e54316bde55c08eeabb48db76820d319
|
| 3 |
+
size 15851488
|
sliders/indie-rock.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:836d82e1883e0fa60f29e6a1af1522f1c65977a4a80ae8c168fd44cb7a5b7f6d
|
| 3 |
+
size 15851520
|
sliders/kpop.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cec833453a6fcd099dc2a4dc1f9d6cafae538eb0ca759657172075cfeeb26c2c
|
| 3 |
+
size 15851488
|
sliders/lofi.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:58586891d6e0dc696fd6a71391bd84dba489a9326c6fdcaf355ff7b4ccb9e04d
|
| 3 |
+
size 15851488
|
sliders/male.gguf
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
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universr/config.yaml
ADDED
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| 1 |
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model:
|
| 2 |
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in_channels: 2
|
| 3 |
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out_channels: 2
|
| 4 |
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dims:
|
| 5 |
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| 6 |
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| 8 |
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|
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depths:
|
| 10 |
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|
| 11 |
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| 12 |
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|
| 13 |
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| 14 |
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drop_path: 0
|
| 15 |
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time_dim: 256
|
| 16 |
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cond_dim: 384
|
| 17 |
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total_freq_bins: 512
|
| 18 |
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hr_freq_bins: 432
|
| 19 |
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feature_enc_layers: 4
|
| 20 |
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cond_dropout_prob: 0.1
|
| 21 |
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sr_to_lr_bins:
|
| 22 |
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8: 80
|
| 23 |
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12: 128
|
| 24 |
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16: 170
|
| 25 |
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24: 256
|
| 26 |
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transform:
|
| 27 |
+
window_fn: hann
|
| 28 |
+
n_fft: 1024
|
| 29 |
+
sampling_rate: 48000
|
| 30 |
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hop_length: 512
|
| 31 |
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alpha: 0.2
|
| 32 |
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beta: 1
|
| 33 |
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comp_eps: 0.0001
|
| 34 |
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path:
|
| 35 |
+
class_path: universr.flow.path.OriginalCFMPath
|
| 36 |
+
init_args:
|
| 37 |
+
sigma_min: 0.0001
|
universr/pytorch_model.bin
ADDED
|
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whisper/whisper-large-v3-ct2-float16/.gitattributes
ADDED
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*.7z filter=lfs diff=lfs merge=lfs -text
|
| 2 |
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*.arrow filter=lfs diff=lfs merge=lfs -text
|
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|
| 10 |
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|
| 11 |
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| 12 |
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| 13 |
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|
| 14 |
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*.npy filter=lfs diff=lfs merge=lfs -text
|
| 15 |
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|
| 16 |
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|
| 17 |
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|
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|
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|
| 20 |
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|
| 21 |
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|
| 22 |
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*.pt filter=lfs diff=lfs merge=lfs -text
|
| 23 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 28 |
+
*.tar filter=lfs diff=lfs merge=lfs -text
|
| 29 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 30 |
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|
| 31 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 32 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 33 |
+
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|
| 34 |
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*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
whisper/whisper-large-v3-ct2-float16/README.md
ADDED
|
@@ -0,0 +1,566 @@
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|
| 1 |
+
---
|
| 2 |
+
language:
|
| 3 |
+
- en
|
| 4 |
+
- zh
|
| 5 |
+
- de
|
| 6 |
+
- es
|
| 7 |
+
- ru
|
| 8 |
+
- ko
|
| 9 |
+
- fr
|
| 10 |
+
- ja
|
| 11 |
+
- pt
|
| 12 |
+
- tr
|
| 13 |
+
- pl
|
| 14 |
+
- ca
|
| 15 |
+
- nl
|
| 16 |
+
- ar
|
| 17 |
+
- sv
|
| 18 |
+
- it
|
| 19 |
+
- id
|
| 20 |
+
- hi
|
| 21 |
+
- fi
|
| 22 |
+
- vi
|
| 23 |
+
- he
|
| 24 |
+
- uk
|
| 25 |
+
- el
|
| 26 |
+
- ms
|
| 27 |
+
- cs
|
| 28 |
+
- ro
|
| 29 |
+
- da
|
| 30 |
+
- hu
|
| 31 |
+
- ta
|
| 32 |
+
- no
|
| 33 |
+
- th
|
| 34 |
+
- ur
|
| 35 |
+
- hr
|
| 36 |
+
- bg
|
| 37 |
+
- lt
|
| 38 |
+
- la
|
| 39 |
+
- mi
|
| 40 |
+
- ml
|
| 41 |
+
- cy
|
| 42 |
+
- sk
|
| 43 |
+
- te
|
| 44 |
+
- fa
|
| 45 |
+
- lv
|
| 46 |
+
- bn
|
| 47 |
+
- sr
|
| 48 |
+
- az
|
| 49 |
+
- sl
|
| 50 |
+
- kn
|
| 51 |
+
- et
|
| 52 |
+
- mk
|
| 53 |
+
- br
|
| 54 |
+
- eu
|
| 55 |
+
- is
|
| 56 |
+
- hy
|
| 57 |
+
- ne
|
| 58 |
+
- mn
|
| 59 |
+
- bs
|
| 60 |
+
- kk
|
| 61 |
+
- sq
|
| 62 |
+
- sw
|
| 63 |
+
- gl
|
| 64 |
+
- mr
|
| 65 |
+
- pa
|
| 66 |
+
- si
|
| 67 |
+
- km
|
| 68 |
+
- sn
|
| 69 |
+
- yo
|
| 70 |
+
- so
|
| 71 |
+
- af
|
| 72 |
+
- oc
|
| 73 |
+
- ka
|
| 74 |
+
- be
|
| 75 |
+
- tg
|
| 76 |
+
- sd
|
| 77 |
+
- gu
|
| 78 |
+
- am
|
| 79 |
+
- yi
|
| 80 |
+
- lo
|
| 81 |
+
- uz
|
| 82 |
+
- fo
|
| 83 |
+
- ht
|
| 84 |
+
- ps
|
| 85 |
+
- tk
|
| 86 |
+
- nn
|
| 87 |
+
- mt
|
| 88 |
+
- sa
|
| 89 |
+
- lb
|
| 90 |
+
- my
|
| 91 |
+
- bo
|
| 92 |
+
- tl
|
| 93 |
+
- mg
|
| 94 |
+
- as
|
| 95 |
+
- tt
|
| 96 |
+
- haw
|
| 97 |
+
- ln
|
| 98 |
+
- ha
|
| 99 |
+
- ba
|
| 100 |
+
- jw
|
| 101 |
+
- su
|
| 102 |
+
tags:
|
| 103 |
+
- audio
|
| 104 |
+
- automatic-speech-recognition
|
| 105 |
+
- hf-asr-leaderboard
|
| 106 |
+
widget:
|
| 107 |
+
- example_title: Librispeech sample 1
|
| 108 |
+
src: https://cdn-media.huggingface.co/speech_samples/sample1.flac
|
| 109 |
+
- example_title: Librispeech sample 2
|
| 110 |
+
src: https://cdn-media.huggingface.co/speech_samples/sample2.flac
|
| 111 |
+
pipeline_tag: automatic-speech-recognition
|
| 112 |
+
license: apache-2.0
|
| 113 |
+
---
|
| 114 |
+
|
| 115 |
+
# Whisper
|
| 116 |
+
|
| 117 |
+
Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours
|
| 118 |
+
of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains **without** the need
|
| 119 |
+
for fine-tuning.
|
| 120 |
+
|
| 121 |
+
Whisper was proposed in the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://arxiv.org/abs/2212.04356)
|
| 122 |
+
by Alec Radford et al. from OpenAI. The original code repository can be found [here](https://github.com/openai/whisper).
|
| 123 |
+
|
| 124 |
+
Whisper `large-v3` has the same architecture as the previous large models except the following minor differences:
|
| 125 |
+
|
| 126 |
+
1. The input uses 128 Mel frequency bins instead of 80
|
| 127 |
+
2. A new language token for Cantonese
|
| 128 |
+
|
| 129 |
+
The Whisper `large-v3` model is trained on 1 million hours of weakly labeled audio and 4 million hours of pseudolabeled audio collected using Whisper `large-v2`.
|
| 130 |
+
The model was trained for 2.0 epochs over this mixture dataset.
|
| 131 |
+
|
| 132 |
+
The `large-v3` model shows improved performance over a wide variety of languages, showing 10% to 20% reduction of errors compared to Whisper `large-v2`.
|
| 133 |
+
|
| 134 |
+
|
| 135 |
+
**Disclaimer**: Content for this model card has partly been written by the Hugging Face team, and parts of it were
|
| 136 |
+
copied and pasted from the original model card.
|
| 137 |
+
|
| 138 |
+
## Model details
|
| 139 |
+
|
| 140 |
+
Whisper is a Transformer based encoder-decoder model, also referred to as a _sequence-to-sequence_ model.
|
| 141 |
+
It was trained on 1 million hours of weakly labeled audio and 4 million hours of pseudolabeled audio collected using Whisper `large-v2`.
|
| 142 |
+
|
| 143 |
+
The models were trained on either English-only data or multilingual data. The English-only models were trained
|
| 144 |
+
on the task of speech recognition. The multilingual models were trained on both speech recognition and speech
|
| 145 |
+
translation. For speech recognition, the model predicts transcriptions in the *same* language as the audio.
|
| 146 |
+
For speech translation, the model predicts transcriptions to a *different* language to the audio.
|
| 147 |
+
|
| 148 |
+
Whisper checkpoints come in five configurations of varying model sizes.
|
| 149 |
+
The smallest four are trained on either English-only or multilingual data.
|
| 150 |
+
The largest checkpoints are multilingual only. All ten of the pre-trained checkpoints
|
| 151 |
+
are available on the [Hugging Face Hub](https://huggingface.co/models?search=openai/whisper). The
|
| 152 |
+
checkpoints are summarised in the following table with links to the models on the Hub:
|
| 153 |
+
|
| 154 |
+
| Size | Parameters | English-only | Multilingual |
|
| 155 |
+
|----------|------------|------------------------------------------------------|-----------------------------------------------------|
|
| 156 |
+
| tiny | 39 M | [✓](https://huggingface.co/openai/whisper-tiny.en) | [✓](https://huggingface.co/openai/whisper-tiny) |
|
| 157 |
+
| base | 74 M | [✓](https://huggingface.co/openai/whisper-base.en) | [✓](https://huggingface.co/openai/whisper-base) |
|
| 158 |
+
| small | 244 M | [✓](https://huggingface.co/openai/whisper-small.en) | [✓](https://huggingface.co/openai/whisper-small) |
|
| 159 |
+
| medium | 769 M | [✓](https://huggingface.co/openai/whisper-medium.en) | [✓](https://huggingface.co/openai/whisper-medium) |
|
| 160 |
+
| large | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large) |
|
| 161 |
+
| large-v2 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v2) |
|
| 162 |
+
| large-v3 | 1550 M | x | [✓](https://huggingface.co/openai/whisper-large-v3) |
|
| 163 |
+
|
| 164 |
+
## Usage
|
| 165 |
+
|
| 166 |
+
Whisper `large-v3` is supported in Hugging Face 🤗 Transformers. To run the model, first
|
| 167 |
+
install the Transformers library through the GitHub repo. For this example, we'll also install 🤗 Datasets to load toy
|
| 168 |
+
audio dataset from the Hugging Face Hub:
|
| 169 |
+
|
| 170 |
+
```bash
|
| 171 |
+
pip install --upgrade pip
|
| 172 |
+
pip install --upgrade git+https://github.com/huggingface/transformers.git accelerate datasets[audio]
|
| 173 |
+
```
|
| 174 |
+
|
| 175 |
+
### Short-Form Transcription
|
| 176 |
+
|
| 177 |
+
The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
|
| 178 |
+
class to transcribe short-form audio files (< 30-seconds) as follows:
|
| 179 |
+
|
| 180 |
+
```python
|
| 181 |
+
import torch
|
| 182 |
+
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
|
| 183 |
+
from datasets import load_dataset
|
| 184 |
+
|
| 185 |
+
|
| 186 |
+
device = "cuda:0" if torch.cuda.is_available() else "cpu"
|
| 187 |
+
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
|
| 188 |
+
|
| 189 |
+
model_id = "openai/whisper-large-v3"
|
| 190 |
+
|
| 191 |
+
model = AutoModelForSpeechSeq2Seq.from_pretrained(
|
| 192 |
+
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
|
| 193 |
+
)
|
| 194 |
+
model.to(device)
|
| 195 |
+
|
| 196 |
+
processor = AutoProcessor.from_pretrained(model_id)
|
| 197 |
+
|
| 198 |
+
pipe = pipeline(
|
| 199 |
+
"automatic-speech-recognition",
|
| 200 |
+
model=model,
|
| 201 |
+
tokenizer=processor.tokenizer,
|
| 202 |
+
feature_extractor=processor.feature_extractor,
|
| 203 |
+
max_new_tokens=128,
|
| 204 |
+
chunk_length_s=30,
|
| 205 |
+
batch_size=16,
|
| 206 |
+
return_timestamps=True,
|
| 207 |
+
torch_dtype=torch_dtype,
|
| 208 |
+
device=device,
|
| 209 |
+
)
|
| 210 |
+
|
| 211 |
+
dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
|
| 212 |
+
sample = dataset[0]["audio"]
|
| 213 |
+
|
| 214 |
+
result = pipe(sample)
|
| 215 |
+
print(result["text"])
|
| 216 |
+
```
|
| 217 |
+
|
| 218 |
+
To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline:
|
| 219 |
+
```diff
|
| 220 |
+
- result = pipe(sample)
|
| 221 |
+
+ result = pipe("audio.mp3")
|
| 222 |
+
```
|
| 223 |
+
|
| 224 |
+
Whisper predicts the language of the source audio automatically. If the source audio language is known *a-priori*, it
|
| 225 |
+
can be passed as an argument to the pipeline:
|
| 226 |
+
|
| 227 |
+
```python
|
| 228 |
+
result = pipe(sample, generate_kwargs={"language": "english"})
|
| 229 |
+
```
|
| 230 |
+
|
| 231 |
+
By default, Whisper performs the task of *speech transcription*, where the source audio language is the same as the target
|
| 232 |
+
text language. To perform *speech translation*, where the target text is in English, set the task to `"translate"`:
|
| 233 |
+
|
| 234 |
+
```python
|
| 235 |
+
result = pipe(sample, generate_kwargs={"task": "translate"})
|
| 236 |
+
```
|
| 237 |
+
|
| 238 |
+
Finally, the model can be made to predict timestamps. For sentence-level timestamps, pass the `return_timestamps` argument:
|
| 239 |
+
|
| 240 |
+
```python
|
| 241 |
+
result = pipe(sample, return_timestamps=True)
|
| 242 |
+
print(result["chunks"])
|
| 243 |
+
```
|
| 244 |
+
|
| 245 |
+
And for word-level timestamps:
|
| 246 |
+
|
| 247 |
+
```python
|
| 248 |
+
result = pipe(sample, return_timestamps="word")
|
| 249 |
+
print(result["chunks"])
|
| 250 |
+
```
|
| 251 |
+
|
| 252 |
+
The above arguments can be used in isolation or in combination. For example, to perform the task of speech transcription
|
| 253 |
+
where the source audio is in French, and we want to return sentence-level timestamps, the following can be used:
|
| 254 |
+
|
| 255 |
+
```python
|
| 256 |
+
result = pipe(sample, return_timestamps=True, generate_kwargs={"language": "french", "task": "translate"})
|
| 257 |
+
print(result["chunks"])
|
| 258 |
+
```
|
| 259 |
+
|
| 260 |
+
<details>
|
| 261 |
+
|
| 262 |
+
<summary> For more control over the generation parameters, use the model + processor API directly: </summary>
|
| 263 |
+
|
| 264 |
+
Ad-hoc generation arguments can be passed to `model.generate`, including `num_beams` for beam-search, `return_timestamps`
|
| 265 |
+
for segment-level timestamps, and `prompt_ids` for prompting. See the [docstrings](https://huggingface.co/docs/transformers/en/model_doc/whisper#transformers.WhisperForConditionalGeneration.generate)
|
| 266 |
+
for more details.
|
| 267 |
+
|
| 268 |
+
```python
|
| 269 |
+
import torch
|
| 270 |
+
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
|
| 271 |
+
from datasets import Audio, load_dataset
|
| 272 |
+
|
| 273 |
+
|
| 274 |
+
device = "cuda:0" if torch.cuda.is_available() else "cpu"
|
| 275 |
+
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
|
| 276 |
+
|
| 277 |
+
model_id = "openai/whisper-large-v3"
|
| 278 |
+
|
| 279 |
+
model = AutoModelForSpeechSeq2Seq.from_pretrained(
|
| 280 |
+
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
|
| 281 |
+
)
|
| 282 |
+
model.to(device)
|
| 283 |
+
|
| 284 |
+
processor = AutoProcessor.from_pretrained(model_id)
|
| 285 |
+
|
| 286 |
+
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
| 287 |
+
dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate))
|
| 288 |
+
sample = dataset[0]["audio"]
|
| 289 |
+
|
| 290 |
+
input_features = processor(
|
| 291 |
+
sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt"
|
| 292 |
+
).input_features
|
| 293 |
+
|
| 294 |
+
input_features = input_features.to(device, dtype=torch_dtype)
|
| 295 |
+
|
| 296 |
+
gen_kwargs = {
|
| 297 |
+
"max_new_tokens": 128,
|
| 298 |
+
"num_beams": 1,
|
| 299 |
+
"return_timestamps": False,
|
| 300 |
+
}
|
| 301 |
+
|
| 302 |
+
pred_ids = model.generate(input_features, **gen_kwargs)
|
| 303 |
+
pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=gen_kwargs["return_timestamps"])
|
| 304 |
+
|
| 305 |
+
print(pred_text)
|
| 306 |
+
```
|
| 307 |
+
|
| 308 |
+
</details>
|
| 309 |
+
|
| 310 |
+
### Sequential Long-Form
|
| 311 |
+
|
| 312 |
+
This algorithm uses a sliding window for buffered inference of long audio files (> 30-seconds),
|
| 313 |
+
and returns more accurate transcriptions compared to the [chunked long-form algorithm](#chunked-long-form).
|
| 314 |
+
|
| 315 |
+
The sequential long-form algorithm should be used in either of the following scenarios:
|
| 316 |
+
1. Transcription accuracy is the most important factor, and latency is less of a consideration
|
| 317 |
+
2. You are transcribing **batches** of long audio files, in which case the latency of sequential is comparable to chunked, while being up to 0.5% WER more accurate
|
| 318 |
+
|
| 319 |
+
The [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
|
| 320 |
+
class can be used to transcribe long audio files with the sequential algorithm as follows:
|
| 321 |
+
|
| 322 |
+
```python
|
| 323 |
+
import torch
|
| 324 |
+
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
|
| 325 |
+
from datasets import load_dataset
|
| 326 |
+
|
| 327 |
+
|
| 328 |
+
device = "cuda:0" if torch.cuda.is_available() else "cpu"
|
| 329 |
+
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
|
| 330 |
+
|
| 331 |
+
model_id = "openai/whisper-large-v3"
|
| 332 |
+
|
| 333 |
+
model = AutoModelForSpeechSeq2Seq.from_pretrained(
|
| 334 |
+
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
|
| 335 |
+
)
|
| 336 |
+
model.to(device)
|
| 337 |
+
|
| 338 |
+
processor = AutoProcessor.from_pretrained(model_id)
|
| 339 |
+
|
| 340 |
+
pipe = pipeline(
|
| 341 |
+
"automatic-speech-recognition",
|
| 342 |
+
model=model,
|
| 343 |
+
tokenizer=processor.tokenizer,
|
| 344 |
+
feature_extractor=processor.feature_extractor,
|
| 345 |
+
max_new_tokens=128,
|
| 346 |
+
torch_dtype=torch_dtype,
|
| 347 |
+
device=device,
|
| 348 |
+
)
|
| 349 |
+
|
| 350 |
+
dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
|
| 351 |
+
sample = dataset[0]["audio"]
|
| 352 |
+
|
| 353 |
+
result = pipe(sample)
|
| 354 |
+
print(result["text"])
|
| 355 |
+
```
|
| 356 |
+
|
| 357 |
+
<details>
|
| 358 |
+
|
| 359 |
+
<summary> For more control over the generation parameters, use the model + processor API directly: </summary>
|
| 360 |
+
|
| 361 |
+
```python
|
| 362 |
+
import torch
|
| 363 |
+
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
|
| 364 |
+
from datasets import Audio, load_dataset
|
| 365 |
+
|
| 366 |
+
|
| 367 |
+
device = "cuda:0" if torch.cuda.is_available() else "cpu"
|
| 368 |
+
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
|
| 369 |
+
|
| 370 |
+
model_id = "openai/whisper-large-v3"
|
| 371 |
+
|
| 372 |
+
model = AutoModelForSpeechSeq2Seq.from_pretrained(
|
| 373 |
+
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
|
| 374 |
+
)
|
| 375 |
+
model.to(device)
|
| 376 |
+
|
| 377 |
+
processor = AutoProcessor.from_pretrained(model_id)
|
| 378 |
+
|
| 379 |
+
dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
| 380 |
+
dataset = dataset.cast_column("audio", Audio(processor.feature_extractor.sampling_rate))
|
| 381 |
+
sample = dataset[0]["audio"]
|
| 382 |
+
|
| 383 |
+
inputs = processor(
|
| 384 |
+
sample["array"],
|
| 385 |
+
sampling_rate=sample["sampling_rate"],
|
| 386 |
+
return_tensors="pt",
|
| 387 |
+
truncation=False,
|
| 388 |
+
padding="longest",
|
| 389 |
+
return_attention_mask=True,
|
| 390 |
+
)
|
| 391 |
+
inputs = inputs.to(device, dtype=torch_dtype)
|
| 392 |
+
|
| 393 |
+
gen_kwargs = {
|
| 394 |
+
"max_new_tokens": 448,
|
| 395 |
+
"num_beams": 1,
|
| 396 |
+
"condition_on_prev_tokens": False,
|
| 397 |
+
"compression_ratio_threshold": 1.35, # zlib compression ratio threshold (in token space)
|
| 398 |
+
"temperature": (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
|
| 399 |
+
"logprob_threshold": -1.0,
|
| 400 |
+
"no_speech_threshold": 0.6,
|
| 401 |
+
"return_timestamps": True,
|
| 402 |
+
}
|
| 403 |
+
|
| 404 |
+
pred_ids = model.generate(**i nputs, **gen_kwargs)
|
| 405 |
+
pred_text = processor.batch_decode(pred_ids, skip_special_tokens=True, decode_with_timestamps=False)
|
| 406 |
+
|
| 407 |
+
print(pred_text)
|
| 408 |
+
```
|
| 409 |
+
|
| 410 |
+
</details>
|
| 411 |
+
|
| 412 |
+
### Chunked Long-Form
|
| 413 |
+
|
| 414 |
+
large-v3 remains compatible with the Transformers chunked long-form algorithm. This algorithm should be used when
|
| 415 |
+
a single large audio file is being transcribed and the fastest possible inference is required. In such circumstances,
|
| 416 |
+
the chunked algorithm is up to 9x faster than OpenAI's sequential long-form implementation (see Table 7 of the
|
| 417 |
+
[Distil-Whisper paper](https://arxiv.org/pdf/2311.00430.pdf)).
|
| 418 |
+
|
| 419 |
+
To enable chunking, pass the `chunk_length_s` parameter to the `pipeline`. For distil-large-v3, a chunk length of 25-seconds
|
| 420 |
+
is optimal. To activate batching over long audio files, pass the argument `batch_size`:
|
| 421 |
+
|
| 422 |
+
```python
|
| 423 |
+
import torch
|
| 424 |
+
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
|
| 425 |
+
from datasets import load_dataset
|
| 426 |
+
|
| 427 |
+
|
| 428 |
+
device = "cuda:0" if torch.cuda.is_available() else "cpu"
|
| 429 |
+
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
|
| 430 |
+
|
| 431 |
+
model_id = "openai/whisper-large-v3"
|
| 432 |
+
|
| 433 |
+
model = AutoModelForSpeechSeq2Seq.from_pretrained(
|
| 434 |
+
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
|
| 435 |
+
)
|
| 436 |
+
model.to(device)
|
| 437 |
+
|
| 438 |
+
processor = AutoProcessor.from_pretrained(model_id)
|
| 439 |
+
|
| 440 |
+
pipe = pipeline(
|
| 441 |
+
"automatic-speech-recognition",
|
| 442 |
+
model=model,
|
| 443 |
+
tokenizer=processor.tokenizer,
|
| 444 |
+
feature_extractor=processor.feature_extractor,
|
| 445 |
+
max_new_tokens=128,
|
| 446 |
+
chunk_length_s=25,
|
| 447 |
+
batch_size=16,
|
| 448 |
+
torch_dtype=torch_dtype,
|
| 449 |
+
device=device,
|
| 450 |
+
)
|
| 451 |
+
|
| 452 |
+
dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
|
| 453 |
+
sample = dataset[0]["audio"]
|
| 454 |
+
|
| 455 |
+
result = pipe(sample)
|
| 456 |
+
print(result["text"])
|
| 457 |
+
```
|
| 458 |
+
|
| 459 |
+
### Additional Speed & Memory Improvements
|
| 460 |
+
|
| 461 |
+
You can apply additional speed and memory improvements to Distil-Whisper to further reduce the inference speed and VRAM
|
| 462 |
+
requirements. These optimisations primarily target the attention kernel, swapping it from an eager implementation to a
|
| 463 |
+
more efficient flash attention version.
|
| 464 |
+
|
| 465 |
+
#### Flash Attention 2
|
| 466 |
+
|
| 467 |
+
We recommend using [Flash-Attention 2](https://huggingface.co/docs/transformers/main/en/perf_infer_gpu_one#flashattention-2)
|
| 468 |
+
if your GPU allows for it. To do so, you first need to install [Flash Attention](https://github.com/Dao-AILab/flash-attention):
|
| 469 |
+
|
| 470 |
+
```
|
| 471 |
+
pip install flash-attn --no-build-isolation
|
| 472 |
+
```
|
| 473 |
+
|
| 474 |
+
Then pass `attn_implementation="flash_attention_2"` to `from_pretrained`:
|
| 475 |
+
|
| 476 |
+
```diff
|
| 477 |
+
- model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)
|
| 478 |
+
+ model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, attn_implementation="flash_attention_2")
|
| 479 |
+
```
|
| 480 |
+
|
| 481 |
+
#### Torch Scale-Product-Attention (SDPA)
|
| 482 |
+
|
| 483 |
+
If your GPU does not support Flash Attention, we recommend making use of PyTorch [scaled dot-product attention (SDPA)](https://pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html).
|
| 484 |
+
This attention implementation is activated **by default** for PyTorch versions 2.1.1 or greater. To check
|
| 485 |
+
whether you have a compatible PyTorch version, run the following Python code snippet:
|
| 486 |
+
|
| 487 |
+
```python
|
| 488 |
+
from transformers.utils import is_torch_sdpa_available
|
| 489 |
+
|
| 490 |
+
print(is_torch_sdpa_available())
|
| 491 |
+
```
|
| 492 |
+
|
| 493 |
+
If the above returns `True`, you have a valid version of PyTorch installed and SDPA is activated by default. If it
|
| 494 |
+
returns `False`, you need to upgrade your PyTorch version according to the [official instructions](https://pytorch.org/get-started/locally/)
|
| 495 |
+
|
| 496 |
+
Once a valid PyTorch version is installed, SDPA is activated by default. It can also be set explicitly by specifying
|
| 497 |
+
`attn_implementation="sdpa"` as follows:
|
| 498 |
+
|
| 499 |
+
```diff
|
| 500 |
+
- model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True)
|
| 501 |
+
+ model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True, attn_implementation="sdpa")
|
| 502 |
+
```
|
| 503 |
+
|
| 504 |
+
For more information about how to use the SDPA refer to the [Transformers SDPA documentation](https://huggingface.co/docs/transformers/en/perf_infer_gpu_one#pytorch-scaled-dot-product-attention).
|
| 505 |
+
|
| 506 |
+
#### Torch compile
|
| 507 |
+
|
| 508 |
+
Coming soon...
|
| 509 |
+
|
| 510 |
+
#### 4-bit and 8-bit Inference
|
| 511 |
+
|
| 512 |
+
Coming soon...
|
| 513 |
+
|
| 514 |
+
## Fine-Tuning
|
| 515 |
+
|
| 516 |
+
The pre-trained Whisper model demonstrates a strong ability to generalise to different datasets and domains. However,
|
| 517 |
+
its predictive capabilities can be improved further for certain languages and tasks through *fine-tuning*. The blog
|
| 518 |
+
post [Fine-Tune Whisper with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper) provides a step-by-step
|
| 519 |
+
guide to fine-tuning the Whisper model with as little as 5 hours of labelled data.
|
| 520 |
+
|
| 521 |
+
### Evaluated Use
|
| 522 |
+
|
| 523 |
+
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.
|
| 524 |
+
|
| 525 |
+
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.
|
| 526 |
+
|
| 527 |
+
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.
|
| 528 |
+
|
| 529 |
+
|
| 530 |
+
## Training Data
|
| 531 |
+
|
| 532 |
+
The models are trained on 1 million hours of weakly labeled audio and 4 million hours of pseudolabeled audio collected using Whisper `large-v2`.
|
| 533 |
+
|
| 534 |
+
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.
|
| 535 |
+
|
| 536 |
+
|
| 537 |
+
## Performance and Limitations
|
| 538 |
+
|
| 539 |
+
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.
|
| 540 |
+
|
| 541 |
+
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.
|
| 542 |
+
|
| 543 |
+
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).
|
| 544 |
+
|
| 545 |
+
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.
|
| 546 |
+
|
| 547 |
+
|
| 548 |
+
## Broader Implications
|
| 549 |
+
|
| 550 |
+
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.
|
| 551 |
+
|
| 552 |
+
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.
|
| 553 |
+
|
| 554 |
+
|
| 555 |
+
### BibTeX entry and citation info
|
| 556 |
+
```bibtex
|
| 557 |
+
@misc{radford2022whisper,
|
| 558 |
+
doi = {10.48550/ARXIV.2212.04356},
|
| 559 |
+
url = {https://arxiv.org/abs/2212.04356},
|
| 560 |
+
author = {Radford, Alec and Kim, Jong Wook and Xu, Tao and Brockman, Greg and McLeavey, Christine and Sutskever, Ilya},
|
| 561 |
+
title = {Robust Speech Recognition via Large-Scale Weak Supervision},
|
| 562 |
+
publisher = {arXiv},
|
| 563 |
+
year = {2022},
|
| 564 |
+
copyright = {arXiv.org perpetual, non-exclusive license}
|
| 565 |
+
}
|
| 566 |
+
```
|
whisper/whisper-large-v3-ct2-float16/added_tokens.json
ADDED
|
@@ -0,0 +1,1611 @@
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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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|
| 11 |
+
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|
| 12 |
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|
| 13 |
+
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|
| 14 |
+
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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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 |
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|
| 27 |
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|
| 28 |
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|
| 29 |
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|
| 30 |
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|
| 31 |
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
| 38 |
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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|
| 53 |
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|
| 54 |
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|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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|
| 61 |
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|
| 62 |
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|
| 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 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 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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|
| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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|
| 102 |
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|
| 103 |
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|
| 104 |
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|
| 105 |
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|
| 106 |
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|
| 107 |
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|
| 108 |
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|
| 109 |
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|
| 110 |
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|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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|
| 119 |
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|
| 120 |
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|
| 121 |
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|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
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|
| 141 |
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|
| 142 |
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|
| 143 |
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|
| 144 |
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|
| 145 |
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|
| 146 |
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|
| 147 |
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|
| 148 |
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|
| 149 |
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|
| 150 |
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|
| 151 |
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|
| 152 |
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|
| 153 |
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|
| 154 |
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|
| 155 |
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|
| 156 |
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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 |
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|
| 162 |
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|
| 163 |
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|
| 164 |
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|
| 165 |
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|
| 166 |
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|
| 167 |
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|
| 168 |
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|
| 169 |
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|
| 170 |
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|
| 171 |
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|
| 172 |
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|
| 173 |
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|
| 174 |
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|
| 175 |
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|
| 176 |
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|
| 177 |
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|
| 178 |
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|
| 179 |
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|
| 180 |
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|
| 181 |
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|
| 182 |
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|
| 183 |
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|
| 184 |
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|
| 185 |
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|
| 186 |
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|
| 187 |
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|
| 188 |
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|
| 189 |
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|
| 190 |
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|
| 191 |
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|
| 192 |
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|
| 193 |
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|
| 194 |
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|
| 195 |
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|
| 196 |
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|
| 197 |
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|
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| 1526 |
+
"<|fa|>": 50300,
|
| 1527 |
+
"<|fi|>": 50277,
|
| 1528 |
+
"<|fo|>": 50338,
|
| 1529 |
+
"<|fr|>": 50265,
|
| 1530 |
+
"<|gl|>": 50319,
|
| 1531 |
+
"<|gu|>": 50333,
|
| 1532 |
+
"<|haw|>": 50352,
|
| 1533 |
+
"<|ha|>": 50354,
|
| 1534 |
+
"<|he|>": 50279,
|
| 1535 |
+
"<|hi|>": 50276,
|
| 1536 |
+
"<|hr|>": 50291,
|
| 1537 |
+
"<|ht|>": 50339,
|
| 1538 |
+
"<|hu|>": 50286,
|
| 1539 |
+
"<|hy|>": 50312,
|
| 1540 |
+
"<|id|>": 50275,
|
| 1541 |
+
"<|is|>": 50311,
|
| 1542 |
+
"<|it|>": 50274,
|
| 1543 |
+
"<|ja|>": 50266,
|
| 1544 |
+
"<|jw|>": 50356,
|
| 1545 |
+
"<|ka|>": 50329,
|
| 1546 |
+
"<|kk|>": 50316,
|
| 1547 |
+
"<|km|>": 50323,
|
| 1548 |
+
"<|kn|>": 50306,
|
| 1549 |
+
"<|ko|>": 50264,
|
| 1550 |
+
"<|la|>": 50294,
|
| 1551 |
+
"<|lb|>": 50345,
|
| 1552 |
+
"<|ln|>": 50353,
|
| 1553 |
+
"<|lo|>": 50336,
|
| 1554 |
+
"<|lt|>": 50293,
|
| 1555 |
+
"<|lv|>": 50301,
|
| 1556 |
+
"<|mg|>": 50349,
|
| 1557 |
+
"<|mi|>": 50295,
|
| 1558 |
+
"<|mk|>": 50308,
|
| 1559 |
+
"<|ml|>": 50296,
|
| 1560 |
+
"<|mn|>": 50314,
|
| 1561 |
+
"<|mr|>": 50320,
|
| 1562 |
+
"<|ms|>": 50282,
|
| 1563 |
+
"<|mt|>": 50343,
|
| 1564 |
+
"<|my|>": 50346,
|
| 1565 |
+
"<|ne|>": 50313,
|
| 1566 |
+
"<|nl|>": 50271,
|
| 1567 |
+
"<|nn|>": 50342,
|
| 1568 |
+
"<|nospeech|>": 50363,
|
| 1569 |
+
"<|notimestamps|>": 50364,
|
| 1570 |
+
"<|no|>": 50288,
|
| 1571 |
+
"<|oc|>": 50328,
|
| 1572 |
+
"<|pa|>": 50321,
|
| 1573 |
+
"<|pl|>": 50269,
|
| 1574 |
+
"<|ps|>": 50340,
|
| 1575 |
+
"<|pt|>": 50267,
|
| 1576 |
+
"<|ro|>": 50284,
|
| 1577 |
+
"<|ru|>": 50263,
|
| 1578 |
+
"<|sa|>": 50344,
|
| 1579 |
+
"<|sd|>": 50332,
|
| 1580 |
+
"<|si|>": 50322,
|
| 1581 |
+
"<|sk|>": 50298,
|
| 1582 |
+
"<|sl|>": 50305,
|
| 1583 |
+
"<|sn|>": 50324,
|
| 1584 |
+
"<|so|>": 50326,
|
| 1585 |
+
"<|sq|>": 50317,
|
| 1586 |
+
"<|sr|>": 50303,
|
| 1587 |
+
"<|startoflm|>": 50361,
|
| 1588 |
+
"<|startofprev|>": 50362,
|
| 1589 |
+
"<|startoftranscript|>": 50258,
|
| 1590 |
+
"<|su|>": 50357,
|
| 1591 |
+
"<|sv|>": 50273,
|
| 1592 |
+
"<|sw|>": 50318,
|
| 1593 |
+
"<|ta|>": 50287,
|
| 1594 |
+
"<|te|>": 50299,
|
| 1595 |
+
"<|tg|>": 50331,
|
| 1596 |
+
"<|th|>": 50289,
|
| 1597 |
+
"<|tk|>": 50341,
|
| 1598 |
+
"<|tl|>": 50348,
|
| 1599 |
+
"<|transcribe|>": 50360,
|
| 1600 |
+
"<|translate|>": 50359,
|
| 1601 |
+
"<|tr|>": 50268,
|
| 1602 |
+
"<|tt|>": 50351,
|
| 1603 |
+
"<|uk|>": 50280,
|
| 1604 |
+
"<|ur|>": 50290,
|
| 1605 |
+
"<|uz|>": 50337,
|
| 1606 |
+
"<|vi|>": 50278,
|
| 1607 |
+
"<|yi|>": 50335,
|
| 1608 |
+
"<|yo|>": 50325,
|
| 1609 |
+
"<|yue|>": 50358,
|
| 1610 |
+
"<|zh|>": 50260
|
| 1611 |
+
}
|
whisper/whisper-large-v3-ct2-float16/config.json
ADDED
|
@@ -0,0 +1,240 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"alignment_heads": [
|
| 3 |
+
[
|
| 4 |
+
7,
|
| 5 |
+
0
|
| 6 |
+
],
|
| 7 |
+
[
|
| 8 |
+
10,
|
| 9 |
+
17
|
| 10 |
+
],
|
| 11 |
+
[
|
| 12 |
+
12,
|
| 13 |
+
18
|
| 14 |
+
],
|
| 15 |
+
[
|
| 16 |
+
13,
|
| 17 |
+
12
|
| 18 |
+
],
|
| 19 |
+
[
|
| 20 |
+
16,
|
| 21 |
+
1
|
| 22 |
+
],
|
| 23 |
+
[
|
| 24 |
+
17,
|
| 25 |
+
14
|
| 26 |
+
],
|
| 27 |
+
[
|
| 28 |
+
19,
|
| 29 |
+
11
|
| 30 |
+
],
|
| 31 |
+
[
|
| 32 |
+
21,
|
| 33 |
+
4
|
| 34 |
+
],
|
| 35 |
+
[
|
| 36 |
+
24,
|
| 37 |
+
1
|
| 38 |
+
],
|
| 39 |
+
[
|
| 40 |
+
25,
|
| 41 |
+
6
|
| 42 |
+
]
|
| 43 |
+
],
|
| 44 |
+
"lang_ids": [
|
| 45 |
+
50259,
|
| 46 |
+
50260,
|
| 47 |
+
50261,
|
| 48 |
+
50262,
|
| 49 |
+
50263,
|
| 50 |
+
50264,
|
| 51 |
+
50265,
|
| 52 |
+
50266,
|
| 53 |
+
50267,
|
| 54 |
+
50268,
|
| 55 |
+
50269,
|
| 56 |
+
50270,
|
| 57 |
+
50271,
|
| 58 |
+
50272,
|
| 59 |
+
50273,
|
| 60 |
+
50274,
|
| 61 |
+
50275,
|
| 62 |
+
50276,
|
| 63 |
+
50277,
|
| 64 |
+
50278,
|
| 65 |
+
50279,
|
| 66 |
+
50280,
|
| 67 |
+
50281,
|
| 68 |
+
50282,
|
| 69 |
+
50283,
|
| 70 |
+
50284,
|
| 71 |
+
50285,
|
| 72 |
+
50286,
|
| 73 |
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50287,
|
| 74 |
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50288,
|
| 75 |
+
50289,
|
| 76 |
+
50290,
|
| 77 |
+
50291,
|
| 78 |
+
50292,
|
| 79 |
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50293,
|
| 80 |
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50294,
|
| 81 |
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50295,
|
| 82 |
+
50296,
|
| 83 |
+
50297,
|
| 84 |
+
50298,
|
| 85 |
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50299,
|
| 86 |
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50300,
|
| 87 |
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50301,
|
| 88 |
+
50302,
|
| 89 |
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50303,
|
| 90 |
+
50304,
|
| 91 |
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50305,
|
| 92 |
+
50306,
|
| 93 |
+
50307,
|
| 94 |
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50308,
|
| 95 |
+
50309,
|
| 96 |
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50310,
|
| 97 |
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50311,
|
| 98 |
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50312,
|
| 99 |
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50313,
|
| 100 |
+
50314,
|
| 101 |
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50315,
|
| 102 |
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50316,
|
| 103 |
+
50317,
|
| 104 |
+
50318,
|
| 105 |
+
50319,
|
| 106 |
+
50320,
|
| 107 |
+
50321,
|
| 108 |
+
50322,
|
| 109 |
+
50323,
|
| 110 |
+
50324,
|
| 111 |
+
50325,
|
| 112 |
+
50326,
|
| 113 |
+
50327,
|
| 114 |
+
50328,
|
| 115 |
+
50329,
|
| 116 |
+
50330,
|
| 117 |
+
50331,
|
| 118 |
+
50332,
|
| 119 |
+
50333,
|
| 120 |
+
50334,
|
| 121 |
+
50335,
|
| 122 |
+
50336,
|
| 123 |
+
50337,
|
| 124 |
+
50338,
|
| 125 |
+
50339,
|
| 126 |
+
50340,
|
| 127 |
+
50341,
|
| 128 |
+
50342,
|
| 129 |
+
50343,
|
| 130 |
+
50344,
|
| 131 |
+
50345,
|
| 132 |
+
50346,
|
| 133 |
+
50347,
|
| 134 |
+
50348,
|
| 135 |
+
50349,
|
| 136 |
+
50350,
|
| 137 |
+
50351,
|
| 138 |
+
50352,
|
| 139 |
+
50353,
|
| 140 |
+
50354,
|
| 141 |
+
50355,
|
| 142 |
+
50356,
|
| 143 |
+
50357,
|
| 144 |
+
50358
|
| 145 |
+
],
|
| 146 |
+
"suppress_ids": [
|
| 147 |
+
1,
|
| 148 |
+
2,
|
| 149 |
+
7,
|
| 150 |
+
8,
|
| 151 |
+
9,
|
| 152 |
+
10,
|
| 153 |
+
14,
|
| 154 |
+
25,
|
| 155 |
+
26,
|
| 156 |
+
27,
|
| 157 |
+
28,
|
| 158 |
+
29,
|
| 159 |
+
31,
|
| 160 |
+
58,
|
| 161 |
+
59,
|
| 162 |
+
60,
|
| 163 |
+
61,
|
| 164 |
+
62,
|
| 165 |
+
63,
|
| 166 |
+
90,
|
| 167 |
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91,
|
| 168 |
+
92,
|
| 169 |
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93,
|
| 170 |
+
359,
|
| 171 |
+
503,
|
| 172 |
+
522,
|
| 173 |
+
542,
|
| 174 |
+
873,
|
| 175 |
+
893,
|
| 176 |
+
902,
|
| 177 |
+
918,
|
| 178 |
+
922,
|
| 179 |
+
931,
|
| 180 |
+
1350,
|
| 181 |
+
1853,
|
| 182 |
+
1982,
|
| 183 |
+
2460,
|
| 184 |
+
2627,
|
| 185 |
+
3246,
|
| 186 |
+
3253,
|
| 187 |
+
3268,
|
| 188 |
+
3536,
|
| 189 |
+
3846,
|
| 190 |
+
3961,
|
| 191 |
+
4183,
|
| 192 |
+
4667,
|
| 193 |
+
6585,
|
| 194 |
+
6647,
|
| 195 |
+
7273,
|
| 196 |
+
9061,
|
| 197 |
+
9383,
|
| 198 |
+
10428,
|
| 199 |
+
10929,
|
| 200 |
+
11938,
|
| 201 |
+
12033,
|
| 202 |
+
12331,
|
| 203 |
+
12562,
|
| 204 |
+
13793,
|
| 205 |
+
14157,
|
| 206 |
+
14635,
|
| 207 |
+
15265,
|
| 208 |
+
15618,
|
| 209 |
+
16553,
|
| 210 |
+
16604,
|
| 211 |
+
18362,
|
| 212 |
+
18956,
|
| 213 |
+
20075,
|
| 214 |
+
21675,
|
| 215 |
+
22520,
|
| 216 |
+
26130,
|
| 217 |
+
26161,
|
| 218 |
+
26435,
|
| 219 |
+
28279,
|
| 220 |
+
29464,
|
| 221 |
+
31650,
|
| 222 |
+
32302,
|
| 223 |
+
32470,
|
| 224 |
+
36865,
|
| 225 |
+
42863,
|
| 226 |
+
47425,
|
| 227 |
+
49870,
|
| 228 |
+
50254,
|
| 229 |
+
50258,
|
| 230 |
+
50359,
|
| 231 |
+
50360,
|
| 232 |
+
50361,
|
| 233 |
+
50362,
|
| 234 |
+
50363
|
| 235 |
+
],
|
| 236 |
+
"suppress_ids_begin": [
|
| 237 |
+
220,
|
| 238 |
+
50257
|
| 239 |
+
]
|
| 240 |
+
}
|
whisper/whisper-large-v3-ct2-float16/generation_config.json
ADDED
|
@@ -0,0 +1,264 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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whisper/whisper-large-v3-ct2-float16/normalizer.json
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|
| 1 |
+
{
|
| 2 |
+
"accessorise": "accessorize",
|
| 3 |
+
"accessorised": "accessorized",
|
| 4 |
+
"accessorises": "accessorizes",
|
| 5 |
+
"accessorising": "accessorizing",
|
| 6 |
+
"acclimatisation": "acclimatization",
|
| 7 |
+
"acclimatise": "acclimatize",
|
| 8 |
+
"acclimatised": "acclimatized",
|
| 9 |
+
"acclimatises": "acclimatizes",
|
| 10 |
+
"acclimatising": "acclimatizing",
|
| 11 |
+
"accoutrements": "accouterments",
|
| 12 |
+
"aeon": "eon",
|
| 13 |
+
"aeons": "eons",
|
| 14 |
+
"aerogramme": "aerogram",
|
| 15 |
+
"aerogrammes": "aerograms",
|
| 16 |
+
"aeroplane": "airplane",
|
| 17 |
+
"aeroplanes": "airplanes",
|
| 18 |
+
"aesthete": "esthete",
|
| 19 |
+
"aesthetes": "esthetes",
|
| 20 |
+
"aesthetic": "esthetic",
|
| 21 |
+
"aesthetically": "esthetically",
|
| 22 |
+
"aesthetics": "esthetics",
|
| 23 |
+
"aetiology": "etiology",
|
| 24 |
+
"ageing": "aging",
|
| 25 |
+
"aggrandisement": "aggrandizement",
|
| 26 |
+
"agonise": "agonize",
|
| 27 |
+
"agonised": "agonized",
|
| 28 |
+
"agonises": "agonizes",
|
| 29 |
+
"agonising": "agonizing",
|
| 30 |
+
"agonisingly": "agonizingly",
|
| 31 |
+
"almanack": "almanac",
|
| 32 |
+
"almanacks": "almanacs",
|
| 33 |
+
"aluminium": "aluminum",
|
| 34 |
+
"amortisable": "amortizable",
|
| 35 |
+
"amortisation": "amortization",
|
| 36 |
+
"amortisations": "amortizations",
|
| 37 |
+
"amortise": "amortize",
|
| 38 |
+
"amortised": "amortized",
|
| 39 |
+
"amortises": "amortizes",
|
| 40 |
+
"amortising": "amortizing",
|
| 41 |
+
"amphitheatre": "amphitheater",
|
| 42 |
+
"amphitheatres": "amphitheaters",
|
| 43 |
+
"anaemia": "anemia",
|
| 44 |
+
"anaemic": "anemic",
|
| 45 |
+
"anaesthesia": "anesthesia",
|
| 46 |
+
"anaesthetic": "anesthetic",
|
| 47 |
+
"anaesthetics": "anesthetics",
|
| 48 |
+
"anaesthetise": "anesthetize",
|
| 49 |
+
"anaesthetised": "anesthetized",
|
| 50 |
+
"anaesthetises": "anesthetizes",
|
| 51 |
+
"anaesthetising": "anesthetizing",
|
| 52 |
+
"anaesthetist": "anesthetist",
|
| 53 |
+
"anaesthetists": "anesthetists",
|
| 54 |
+
"anaesthetize": "anesthetize",
|
| 55 |
+
"anaesthetized": "anesthetized",
|
| 56 |
+
"anaesthetizes": "anesthetizes",
|
| 57 |
+
"anaesthetizing": "anesthetizing",
|
| 58 |
+
"analogue": "analog",
|
| 59 |
+
"analogues": "analogs",
|
| 60 |
+
"analyse": "analyze",
|
| 61 |
+
"analysed": "analyzed",
|
| 62 |
+
"analyses": "analyzes",
|
| 63 |
+
"analysing": "analyzing",
|
| 64 |
+
"anglicise": "anglicize",
|
| 65 |
+
"anglicised": "anglicized",
|
| 66 |
+
"anglicises": "anglicizes",
|
| 67 |
+
"anglicising": "anglicizing",
|
| 68 |
+
"annualised": "annualized",
|
| 69 |
+
"antagonise": "antagonize",
|
| 70 |
+
"antagonised": "antagonized",
|
| 71 |
+
"antagonises": "antagonizes",
|
| 72 |
+
"antagonising": "antagonizing",
|
| 73 |
+
"apologise": "apologize",
|
| 74 |
+
"apologised": "apologized",
|
| 75 |
+
"apologises": "apologizes",
|
| 76 |
+
"apologising": "apologizing",
|
| 77 |
+
"appal": "appall",
|
| 78 |
+
"appals": "appalls",
|
| 79 |
+
"appetiser": "appetizer",
|
| 80 |
+
"appetisers": "appetizers",
|
| 81 |
+
"appetising": "appetizing",
|
| 82 |
+
"appetisingly": "appetizingly",
|
| 83 |
+
"arbour": "arbor",
|
| 84 |
+
"arbours": "arbors",
|
| 85 |
+
"archaeologically": "archeologically",
|
| 86 |
+
"archaeologist": "archeologist",
|
| 87 |
+
"archaeologists": "archeologists",
|
| 88 |
+
"archaeology": "archeology</span>",
|
| 89 |
+
"archeological": "archaeological",
|
| 90 |
+
"ardour": "ardor",
|
| 91 |
+
"armour": "armor",
|
| 92 |
+
"armoured": "armored",
|
| 93 |
+
"armourer": "armorer",
|
| 94 |
+
"armourers": "armorers",
|
| 95 |
+
"armouries": "armories",
|
| 96 |
+
"armoury": "armory",
|
| 97 |
+
"artefact": "artifact",
|
| 98 |
+
"artefacts": "artifacts",
|
| 99 |
+
"authorise": "authorize",
|
| 100 |
+
"authorised": "authorized",
|
| 101 |
+
"authorises": "authorizes",
|
| 102 |
+
"authorising": "authorizing",
|
| 103 |
+
"axe": "ax",
|
| 104 |
+
"backpedalled": "backpedaled",
|
| 105 |
+
"backpedalling": "backpedaling",
|
| 106 |
+
"bannister": "banister",
|
| 107 |
+
"bannisters": "banisters",
|
| 108 |
+
"baptise": "baptize",
|
| 109 |
+
"baptised": "baptized",
|
| 110 |
+
"baptises": "baptizes",
|
| 111 |
+
"baptising": "baptizing",
|
| 112 |
+
"bastardise": "bastardize",
|
| 113 |
+
"bastardised": "bastardized",
|
| 114 |
+
"bastardises": "bastardizes",
|
| 115 |
+
"bastardising": "bastardizing",
|
| 116 |
+
"battleax": "battleaxe",
|
| 117 |
+
"baulk": "balk",
|
| 118 |
+
"baulked": "balked",
|
| 119 |
+
"baulking": "balking",
|
| 120 |
+
"baulks": "balks",
|
| 121 |
+
"bedevilled": "bedeviled",
|
| 122 |
+
"bedevilling": "bedeviling",
|
| 123 |
+
"behaviour": "behavior",
|
| 124 |
+
"behavioural": "behavioral",
|
| 125 |
+
"behaviourism": "behaviorism",
|
| 126 |
+
"behaviourist": "behaviorist",
|
| 127 |
+
"behaviourists": "behaviorists",
|
| 128 |
+
"behaviours": "behaviors",
|
| 129 |
+
"behove": "behoove",
|
| 130 |
+
"behoved": "behooved",
|
| 131 |
+
"behoves": "behooves",
|
| 132 |
+
"bejewelled": "bejeweled",
|
| 133 |
+
"belabour": "belabor",
|
| 134 |
+
"belaboured": "belabored",
|
| 135 |
+
"belabouring": "belaboring",
|
| 136 |
+
"belabours": "belabors",
|
| 137 |
+
"bevelled": "beveled",
|
| 138 |
+
"bevvies": "bevies",
|
| 139 |
+
"bevvy": "bevy",
|
| 140 |
+
"biassed": "biased",
|
| 141 |
+
"biassing": "biasing",
|
| 142 |
+
"bingeing": "binging",
|
| 143 |
+
"bougainvillaea": "bougainvillea",
|
| 144 |
+
"bougainvillaeas": "bougainvilleas",
|
| 145 |
+
"bowdlerise": "bowdlerize",
|
| 146 |
+
"bowdlerised": "bowdlerized",
|
| 147 |
+
"bowdlerises": "bowdlerizes",
|
| 148 |
+
"bowdlerising": "bowdlerizing",
|
| 149 |
+
"breathalyse": "breathalyze",
|
| 150 |
+
"breathalysed": "breathalyzed",
|
| 151 |
+
"breathalyser": "breathalyzer",
|
| 152 |
+
"breathalysers": "breathalyzers",
|
| 153 |
+
"breathalyses": "breathalyzes",
|
| 154 |
+
"breathalysing": "breathalyzing",
|
| 155 |
+
"brutalise": "brutalize",
|
| 156 |
+
"brutalised": "brutalized",
|
| 157 |
+
"brutalises": "brutalizes",
|
| 158 |
+
"brutalising": "brutalizing",
|
| 159 |
+
"busses": "buses",
|
| 160 |
+
"bussing": "busing",
|
| 161 |
+
"caesarean": "cesarean",
|
| 162 |
+
"caesareans": "cesareans",
|
| 163 |
+
"calibre": "caliber",
|
| 164 |
+
"calibres": "calibers",
|
| 165 |
+
"calliper": "caliper",
|
| 166 |
+
"callipers": "calipers",
|
| 167 |
+
"callisthenics": "calisthenics",
|
| 168 |
+
"canalise": "canalize",
|
| 169 |
+
"canalised": "canalized",
|
| 170 |
+
"canalises": "canalizes",
|
| 171 |
+
"canalising": "canalizing",
|
| 172 |
+
"cancelation": "cancellation",
|
| 173 |
+
"cancelations": "cancellations",
|
| 174 |
+
"cancelled": "canceled",
|
| 175 |
+
"cancelling": "canceling",
|
| 176 |
+
"candour": "candor",
|
| 177 |
+
"cannibalise": "cannibalize",
|
| 178 |
+
"cannibalised": "cannibalized",
|
| 179 |
+
"cannibalises": "cannibalizes",
|
| 180 |
+
"cannibalising": "cannibalizing",
|
| 181 |
+
"canonise": "canonize",
|
| 182 |
+
"canonised": "canonized",
|
| 183 |
+
"canonises": "canonizes",
|
| 184 |
+
"canonising": "canonizing",
|
| 185 |
+
"capitalise": "capitalize",
|
| 186 |
+
"capitalised": "capitalized",
|
| 187 |
+
"capitalises": "capitalizes",
|
| 188 |
+
"capitalising": "capitalizing",
|
| 189 |
+
"caramelise": "caramelize",
|
| 190 |
+
"caramelised": "caramelized",
|
| 191 |
+
"caramelises": "caramelizes",
|
| 192 |
+
"caramelising": "caramelizing",
|
| 193 |
+
"carbonise": "carbonize",
|
| 194 |
+
"carbonised": "carbonized",
|
| 195 |
+
"carbonises": "carbonizes",
|
| 196 |
+
"carbonising": "carbonizing",
|
| 197 |
+
"carolled": "caroled",
|
| 198 |
+
"carolling": "caroling",
|
| 199 |
+
"catalogue": "catalog",
|
| 200 |
+
"catalogued": "cataloged",
|
| 201 |
+
"catalogues": "catalogs",
|
| 202 |
+
"cataloguing": "cataloging",
|
| 203 |
+
"catalyse": "catalyze",
|
| 204 |
+
"catalysed": "catalyzed",
|
| 205 |
+
"catalyses": "catalyzes",
|
| 206 |
+
"catalysing": "catalyzing",
|
| 207 |
+
"categorise": "categorize",
|
| 208 |
+
"categorised": "categorized",
|
| 209 |
+
"categorises": "categorizes",
|
| 210 |
+
"categorising": "categorizing",
|
| 211 |
+
"cauterise": "cauterize",
|
| 212 |
+
"cauterised": "cauterized",
|
| 213 |
+
"cauterises": "cauterizes",
|
| 214 |
+
"cauterising": "cauterizing",
|
| 215 |
+
"cavilled": "caviled",
|
| 216 |
+
"cavilling": "caviling",
|
| 217 |
+
"centigramme": "centigram",
|
| 218 |
+
"centigrammes": "centigrams",
|
| 219 |
+
"centilitre": "centiliter",
|
| 220 |
+
"centilitres": "centiliters",
|
| 221 |
+
"centimetre": "centimeter",
|
| 222 |
+
"centimetres": "centimeters",
|
| 223 |
+
"centralise": "centralize",
|
| 224 |
+
"centralised": "centralized",
|
| 225 |
+
"centralises": "centralizes",
|
| 226 |
+
"centralising": "centralizing",
|
| 227 |
+
"centre": "center",
|
| 228 |
+
"centred": "centered",
|
| 229 |
+
"centrefold": "centerfold",
|
| 230 |
+
"centrefolds": "centerfolds",
|
| 231 |
+
"centrepiece": "centerpiece",
|
| 232 |
+
"centrepieces": "centerpieces",
|
| 233 |
+
"centres": "centers",
|
| 234 |
+
"channelled": "channeled",
|
| 235 |
+
"channelling": "channeling",
|
| 236 |
+
"characterise": "characterize",
|
| 237 |
+
"characterised": "characterized",
|
| 238 |
+
"characterises": "characterizes",
|
| 239 |
+
"characterising": "characterizing",
|
| 240 |
+
"cheque": "check",
|
| 241 |
+
"chequebook": "checkbook",
|
| 242 |
+
"chequebooks": "checkbooks",
|
| 243 |
+
"chequered": "checkered",
|
| 244 |
+
"cheques": "checks",
|
| 245 |
+
"chilli": "chili",
|
| 246 |
+
"chimaera": "chimera",
|
| 247 |
+
"chimaeras": "chimeras",
|
| 248 |
+
"chiselled": "chiseled",
|
| 249 |
+
"chiselling": "chiseling",
|
| 250 |
+
"circularise": "circularize",
|
| 251 |
+
"circularised": "circularized",
|
| 252 |
+
"circularises": "circularizes",
|
| 253 |
+
"circularising": "circularizing",
|
| 254 |
+
"civilise": "civilize",
|
| 255 |
+
"civilised": "civilized",
|
| 256 |
+
"civilises": "civilizes",
|
| 257 |
+
"civilising": "civilizing",
|
| 258 |
+
"clamour": "clamor",
|
| 259 |
+
"clamoured": "clamored",
|
| 260 |
+
"clamouring": "clamoring",
|
| 261 |
+
"clamours": "clamors",
|
| 262 |
+
"clangour": "clangor",
|
| 263 |
+
"clarinettist": "clarinetist",
|
| 264 |
+
"clarinettists": "clarinetists",
|
| 265 |
+
"collectivise": "collectivize",
|
| 266 |
+
"collectivised": "collectivized",
|
| 267 |
+
"collectivises": "collectivizes",
|
| 268 |
+
"collectivising": "collectivizing",
|
| 269 |
+
"colonisation": "colonization",
|
| 270 |
+
"colonise": "colonize",
|
| 271 |
+
"colonised": "colonized",
|
| 272 |
+
"coloniser": "colonizer",
|
| 273 |
+
"colonisers": "colonizers",
|
| 274 |
+
"colonises": "colonizes",
|
| 275 |
+
"colonising": "colonizing",
|
| 276 |
+
"colour": "color",
|
| 277 |
+
"colourant": "colorant",
|
| 278 |
+
"colourants": "colorants",
|
| 279 |
+
"coloured": "colored",
|
| 280 |
+
"coloureds": "coloreds",
|
| 281 |
+
"colourful": "colorful",
|
| 282 |
+
"colourfully": "colorfully",
|
| 283 |
+
"colouring": "coloring",
|
| 284 |
+
"colourize": "colorize",
|
| 285 |
+
"colourized": "colorized",
|
| 286 |
+
"colourizes": "colorizes",
|
| 287 |
+
"colourizing": "colorizing",
|
| 288 |
+
"colourless": "colorless",
|
| 289 |
+
"colours": "colors",
|
| 290 |
+
"commercialise": "commercialize",
|
| 291 |
+
"commercialised": "commercialized",
|
| 292 |
+
"commercialises": "commercializes",
|
| 293 |
+
"commercialising": "commercializing",
|
| 294 |
+
"compartmentalise": "compartmentalize",
|
| 295 |
+
"compartmentalised": "compartmentalized",
|
| 296 |
+
"compartmentalises": "compartmentalizes",
|
| 297 |
+
"compartmentalising": "compartmentalizing",
|
| 298 |
+
"computerise": "computerize",
|
| 299 |
+
"computerised": "computerized",
|
| 300 |
+
"computerises": "computerizes",
|
| 301 |
+
"computerising": "computerizing",
|
| 302 |
+
"conceptualise": "conceptualize",
|
| 303 |
+
"conceptualised": "conceptualized",
|
| 304 |
+
"conceptualises": "conceptualizes",
|
| 305 |
+
"conceptualising": "conceptualizing",
|
| 306 |
+
"connexion": "connection",
|
| 307 |
+
"connexions": "connections",
|
| 308 |
+
"contextualise": "contextualize",
|
| 309 |
+
"contextualised": "contextualized",
|
| 310 |
+
"contextualises": "contextualizes",
|
| 311 |
+
"contextualising": "contextualizing",
|
| 312 |
+
"cosier": "cozier",
|
| 313 |
+
"cosies": "cozies",
|
| 314 |
+
"cosiest": "coziest",
|
| 315 |
+
"cosily": "cozily",
|
| 316 |
+
"cosiness": "coziness",
|
| 317 |
+
"cosy": "cozy",
|
| 318 |
+
"councillor": "councilor",
|
| 319 |
+
"councillors": "councilors",
|
| 320 |
+
"counselled": "counseled",
|
| 321 |
+
"counselling": "counseling",
|
| 322 |
+
"counsellor": "counselor",
|
| 323 |
+
"counsellors": "counselors",
|
| 324 |
+
"crenelated": "crenellated",
|
| 325 |
+
"criminalise": "criminalize",
|
| 326 |
+
"criminalised": "criminalized",
|
| 327 |
+
"criminalises": "criminalizes",
|
| 328 |
+
"criminalising": "criminalizing",
|
| 329 |
+
"criticise": "criticize",
|
| 330 |
+
"criticised": "criticized",
|
| 331 |
+
"criticises": "criticizes",
|
| 332 |
+
"criticising": "criticizing",
|
| 333 |
+
"crueller": "crueler",
|
| 334 |
+
"cruellest": "cruelest",
|
| 335 |
+
"crystallisation": "crystallization",
|
| 336 |
+
"crystallise": "crystallize",
|
| 337 |
+
"crystallised": "crystallized",
|
| 338 |
+
"crystallises": "crystallizes",
|
| 339 |
+
"crystallising": "crystallizing",
|
| 340 |
+
"cudgelled": "cudgeled",
|
| 341 |
+
"cudgelling": "cudgeling",
|
| 342 |
+
"customise": "customize",
|
| 343 |
+
"customised": "customized",
|
| 344 |
+
"customises": "customizes",
|
| 345 |
+
"customising": "customizing",
|
| 346 |
+
"cypher": "cipher",
|
| 347 |
+
"cyphers": "ciphers",
|
| 348 |
+
"decentralisation": "decentralization",
|
| 349 |
+
"decentralise": "decentralize",
|
| 350 |
+
"decentralised": "decentralized",
|
| 351 |
+
"decentralises": "decentralizes",
|
| 352 |
+
"decentralising": "decentralizing",
|
| 353 |
+
"decriminalisation": "decriminalization",
|
| 354 |
+
"decriminalise": "decriminalize",
|
| 355 |
+
"decriminalised": "decriminalized",
|
| 356 |
+
"decriminalises": "decriminalizes",
|
| 357 |
+
"decriminalising": "decriminalizing",
|
| 358 |
+
"defence": "defense",
|
| 359 |
+
"defenceless": "defenseless",
|
| 360 |
+
"defences": "defenses",
|
| 361 |
+
"dehumanisation": "dehumanization",
|
| 362 |
+
"dehumanise": "dehumanize",
|
| 363 |
+
"dehumanised": "dehumanized",
|
| 364 |
+
"dehumanises": "dehumanizes",
|
| 365 |
+
"dehumanising": "dehumanizing",
|
| 366 |
+
"demeanour": "demeanor",
|
| 367 |
+
"demilitarisation": "demilitarization",
|
| 368 |
+
"demilitarise": "demilitarize",
|
| 369 |
+
"demilitarised": "demilitarized",
|
| 370 |
+
"demilitarises": "demilitarizes",
|
| 371 |
+
"demilitarising": "demilitarizing",
|
| 372 |
+
"demobilisation": "demobilization",
|
| 373 |
+
"demobilise": "demobilize",
|
| 374 |
+
"demobilised": "demobilized",
|
| 375 |
+
"demobilises": "demobilizes",
|
| 376 |
+
"demobilising": "demobilizing",
|
| 377 |
+
"democratisation": "democratization",
|
| 378 |
+
"democratise": "democratize",
|
| 379 |
+
"democratised": "democratized",
|
| 380 |
+
"democratises": "democratizes",
|
| 381 |
+
"democratising": "democratizing",
|
| 382 |
+
"demonise": "demonize",
|
| 383 |
+
"demonised": "demonized",
|
| 384 |
+
"demonises": "demonizes",
|
| 385 |
+
"demonising": "demonizing",
|
| 386 |
+
"demoralisation": "demoralization",
|
| 387 |
+
"demoralise": "demoralize",
|
| 388 |
+
"demoralised": "demoralized",
|
| 389 |
+
"demoralises": "demoralizes",
|
| 390 |
+
"demoralising": "demoralizing",
|
| 391 |
+
"denationalisation": "denationalization",
|
| 392 |
+
"denationalise": "denationalize",
|
| 393 |
+
"denationalised": "denationalized",
|
| 394 |
+
"denationalises": "denationalizes",
|
| 395 |
+
"denationalising": "denationalizing",
|
| 396 |
+
"deodorise": "deodorize",
|
| 397 |
+
"deodorised": "deodorized",
|
| 398 |
+
"deodorises": "deodorizes",
|
| 399 |
+
"deodorising": "deodorizing",
|
| 400 |
+
"depersonalise": "depersonalize",
|
| 401 |
+
"depersonalised": "depersonalized",
|
| 402 |
+
"depersonalises": "depersonalizes",
|
| 403 |
+
"depersonalising": "depersonalizing",
|
| 404 |
+
"deputise": "deputize",
|
| 405 |
+
"deputised": "deputized",
|
| 406 |
+
"deputises": "deputizes",
|
| 407 |
+
"deputising": "deputizing",
|
| 408 |
+
"desensitisation": "desensitization",
|
| 409 |
+
"desensitise": "desensitize",
|
| 410 |
+
"desensitised": "desensitized",
|
| 411 |
+
"desensitises": "desensitizes",
|
| 412 |
+
"desensitising": "desensitizing",
|
| 413 |
+
"destabilisation": "destabilization",
|
| 414 |
+
"destabilise": "destabilize",
|
| 415 |
+
"destabilised": "destabilized",
|
| 416 |
+
"destabilises": "destabilizes",
|
| 417 |
+
"destabilising": "destabilizing",
|
| 418 |
+
"dialled": "dialed",
|
| 419 |
+
"dialling": "dialing",
|
| 420 |
+
"dialogue": "dialog",
|
| 421 |
+
"dialogues": "dialogs",
|
| 422 |
+
"diarrhoea": "diarrhea",
|
| 423 |
+
"digitise": "digitize",
|
| 424 |
+
"digitised": "digitized",
|
| 425 |
+
"digitises": "digitizes",
|
| 426 |
+
"digitising": "digitizing",
|
| 427 |
+
"disc": "disk",
|
| 428 |
+
"discolour": "discolor",
|
| 429 |
+
"discoloured": "discolored",
|
| 430 |
+
"discolouring": "discoloring",
|
| 431 |
+
"discolours": "discolors",
|
| 432 |
+
"discs": "disks",
|
| 433 |
+
"disembowelled": "disemboweled",
|
| 434 |
+
"disembowelling": "disemboweling",
|
| 435 |
+
"disfavour": "disfavor",
|
| 436 |
+
"dishevelled": "disheveled",
|
| 437 |
+
"dishonour": "dishonor",
|
| 438 |
+
"dishonourable": "dishonorable",
|
| 439 |
+
"dishonourably": "dishonorably",
|
| 440 |
+
"dishonoured": "dishonored",
|
| 441 |
+
"dishonouring": "dishonoring",
|
| 442 |
+
"dishonours": "dishonors",
|
| 443 |
+
"disorganisation": "disorganization",
|
| 444 |
+
"disorganised": "disorganized",
|
| 445 |
+
"distil": "distill",
|
| 446 |
+
"distils": "distills",
|
| 447 |
+
"dramatisation": "dramatization",
|
| 448 |
+
"dramatisations": "dramatizations",
|
| 449 |
+
"dramatise": "dramatize",
|
| 450 |
+
"dramatised": "dramatized",
|
| 451 |
+
"dramatises": "dramatizes",
|
| 452 |
+
"dramatising": "dramatizing",
|
| 453 |
+
"draught": "draft",
|
| 454 |
+
"draughtboard": "draftboard",
|
| 455 |
+
"draughtboards": "draftboards",
|
| 456 |
+
"draughtier": "draftier",
|
| 457 |
+
"draughtiest": "draftiest",
|
| 458 |
+
"draughts": "drafts",
|
| 459 |
+
"draughtsman": "draftsman",
|
| 460 |
+
"draughtsmanship": "draftsmanship",
|
| 461 |
+
"draughtsmen": "draftsmen",
|
| 462 |
+
"draughtswoman": "draftswoman",
|
| 463 |
+
"draughtswomen": "draftswomen",
|
| 464 |
+
"draughty": "drafty",
|
| 465 |
+
"drivelled": "driveled",
|
| 466 |
+
"drivelling": "driveling",
|
| 467 |
+
"duelled": "dueled",
|
| 468 |
+
"duelling": "dueling",
|
| 469 |
+
"economise": "economize",
|
| 470 |
+
"economised": "economized",
|
| 471 |
+
"economises": "economizes",
|
| 472 |
+
"economising": "economizing",
|
| 473 |
+
"editorialise": "editorialize",
|
| 474 |
+
"editorialised": "editorialized",
|
| 475 |
+
"editorialises": "editorializes",
|
| 476 |
+
"editorialising": "editorializing",
|
| 477 |
+
"edoema": "edema",
|
| 478 |
+
"empathise": "empathize",
|
| 479 |
+
"empathised": "empathized",
|
| 480 |
+
"empathises": "empathizes",
|
| 481 |
+
"empathising": "empathizing",
|
| 482 |
+
"emphasise": "emphasize",
|
| 483 |
+
"emphasised": "emphasized",
|
| 484 |
+
"emphasises": "emphasizes",
|
| 485 |
+
"emphasising": "emphasizing",
|
| 486 |
+
"enamelled": "enameled",
|
| 487 |
+
"enamelling": "enameling",
|
| 488 |
+
"enamoured": "enamored",
|
| 489 |
+
"encyclopaedia": "encyclopedia",
|
| 490 |
+
"encyclopaedias": "encyclopedias",
|
| 491 |
+
"encyclopaedic": "encyclopedic",
|
| 492 |
+
"endeavour": "endeavor",
|
| 493 |
+
"endeavoured": "endeavored",
|
| 494 |
+
"endeavouring": "endeavoring",
|
| 495 |
+
"endeavours": "endeavors",
|
| 496 |
+
"energise": "energize",
|
| 497 |
+
"energised": "energized",
|
| 498 |
+
"energises": "energizes",
|
| 499 |
+
"energising": "energizing",
|
| 500 |
+
"enrol": "enroll",
|
| 501 |
+
"enrols": "enrolls",
|
| 502 |
+
"enthral": "enthrall",
|
| 503 |
+
"enthrals": "enthralls",
|
| 504 |
+
"epaulette": "epaulet",
|
| 505 |
+
"epaulettes": "epaulets",
|
| 506 |
+
"epicentre": "epicenter",
|
| 507 |
+
"epicentres": "epicenters",
|
| 508 |
+
"epilogue": "epilog",
|
| 509 |
+
"epilogues": "epilogs",
|
| 510 |
+
"epitomise": "epitomize",
|
| 511 |
+
"epitomised": "epitomized",
|
| 512 |
+
"epitomises": "epitomizes",
|
| 513 |
+
"epitomising": "epitomizing",
|
| 514 |
+
"equalisation": "equalization",
|
| 515 |
+
"equalise": "equalize",
|
| 516 |
+
"equalised": "equalized",
|
| 517 |
+
"equaliser": "equalizer",
|
| 518 |
+
"equalisers": "equalizers",
|
| 519 |
+
"equalises": "equalizes",
|
| 520 |
+
"equalising": "equalizing",
|
| 521 |
+
"eulogise": "eulogize",
|
| 522 |
+
"eulogised": "eulogized",
|
| 523 |
+
"eulogises": "eulogizes",
|
| 524 |
+
"eulogising": "eulogizing",
|
| 525 |
+
"evangelise": "evangelize",
|
| 526 |
+
"evangelised": "evangelized",
|
| 527 |
+
"evangelises": "evangelizes",
|
| 528 |
+
"evangelising": "evangelizing",
|
| 529 |
+
"exorcise": "exorcize",
|
| 530 |
+
"exorcised": "exorcized",
|
| 531 |
+
"exorcises": "exorcizes",
|
| 532 |
+
"exorcising": "exorcizing",
|
| 533 |
+
"extemporisation": "extemporization",
|
| 534 |
+
"extemporise": "extemporize",
|
| 535 |
+
"extemporised": "extemporized",
|
| 536 |
+
"extemporises": "extemporizes",
|
| 537 |
+
"extemporising": "extemporizing",
|
| 538 |
+
"externalisation": "externalization",
|
| 539 |
+
"externalisations": "externalizations",
|
| 540 |
+
"externalise": "externalize",
|
| 541 |
+
"externalised": "externalized",
|
| 542 |
+
"externalises": "externalizes",
|
| 543 |
+
"externalising": "externalizing",
|
| 544 |
+
"factorise": "factorize",
|
| 545 |
+
"factorised": "factorized",
|
| 546 |
+
"factorises": "factorizes",
|
| 547 |
+
"factorising": "factorizing",
|
| 548 |
+
"faecal": "fecal",
|
| 549 |
+
"faeces": "feces",
|
| 550 |
+
"familiarisation": "familiarization",
|
| 551 |
+
"familiarise": "familiarize",
|
| 552 |
+
"familiarised": "familiarized",
|
| 553 |
+
"familiarises": "familiarizes",
|
| 554 |
+
"familiarising": "familiarizing",
|
| 555 |
+
"fantasise": "fantasize",
|
| 556 |
+
"fantasised": "fantasized",
|
| 557 |
+
"fantasises": "fantasizes",
|
| 558 |
+
"fantasising": "fantasizing",
|
| 559 |
+
"favour": "favor",
|
| 560 |
+
"favourable": "favorable",
|
| 561 |
+
"favourably": "favorably",
|
| 562 |
+
"favoured": "favored",
|
| 563 |
+
"favouring": "favoring",
|
| 564 |
+
"favourite": "favorite",
|
| 565 |
+
"favourites": "favorites",
|
| 566 |
+
"favouritism": "favoritism",
|
| 567 |
+
"favours": "favors",
|
| 568 |
+
"feminise": "feminize",
|
| 569 |
+
"feminised": "feminized",
|
| 570 |
+
"feminises": "feminizes",
|
| 571 |
+
"feminising": "feminizing",
|
| 572 |
+
"fertilisation": "fertilization",
|
| 573 |
+
"fertilise": "fertilize",
|
| 574 |
+
"fertilised": "fertilized",
|
| 575 |
+
"fertiliser": "fertilizer",
|
| 576 |
+
"fertilisers": "fertilizers",
|
| 577 |
+
"fertilises": "fertilizes",
|
| 578 |
+
"fertilising": "fertilizing",
|
| 579 |
+
"fervour": "fervor",
|
| 580 |
+
"fibre": "fiber",
|
| 581 |
+
"fibreglass": "fiberglass",
|
| 582 |
+
"fibres": "fibers",
|
| 583 |
+
"fictionalisation": "fictionalization",
|
| 584 |
+
"fictionalisations": "fictionalizations",
|
| 585 |
+
"fictionalise": "fictionalize",
|
| 586 |
+
"fictionalised": "fictionalized",
|
| 587 |
+
"fictionalises": "fictionalizes",
|
| 588 |
+
"fictionalising": "fictionalizing",
|
| 589 |
+
"fillet": "filet",
|
| 590 |
+
"filleted": "fileted",
|
| 591 |
+
"filleting": "fileting",
|
| 592 |
+
"fillets": "filets",
|
| 593 |
+
"finalisation": "finalization",
|
| 594 |
+
"finalise": "finalize",
|
| 595 |
+
"finalised": "finalized",
|
| 596 |
+
"finalises": "finalizes",
|
| 597 |
+
"finalising": "finalizing",
|
| 598 |
+
"flautist": "flutist",
|
| 599 |
+
"flautists": "flutists",
|
| 600 |
+
"flavour": "flavor",
|
| 601 |
+
"flavoured": "flavored",
|
| 602 |
+
"flavouring": "flavoring",
|
| 603 |
+
"flavourings": "flavorings",
|
| 604 |
+
"flavourless": "flavorless",
|
| 605 |
+
"flavours": "flavors",
|
| 606 |
+
"flavoursome": "flavorsome",
|
| 607 |
+
"flyer / flier": "flier / flyer",
|
| 608 |
+
"foetal": "fetal",
|
| 609 |
+
"foetid": "fetid",
|
| 610 |
+
"foetus": "fetus",
|
| 611 |
+
"foetuses": "fetuses",
|
| 612 |
+
"formalisation": "formalization",
|
| 613 |
+
"formalise": "formalize",
|
| 614 |
+
"formalised": "formalized",
|
| 615 |
+
"formalises": "formalizes",
|
| 616 |
+
"formalising": "formalizing",
|
| 617 |
+
"fossilisation": "fossilization",
|
| 618 |
+
"fossilise": "fossilize",
|
| 619 |
+
"fossilised": "fossilized",
|
| 620 |
+
"fossilises": "fossilizes",
|
| 621 |
+
"fossilising": "fossilizing",
|
| 622 |
+
"fraternisation": "fraternization",
|
| 623 |
+
"fraternise": "fraternize",
|
| 624 |
+
"fraternised": "fraternized",
|
| 625 |
+
"fraternises": "fraternizes",
|
| 626 |
+
"fraternising": "fraternizing",
|
| 627 |
+
"fulfil": "fulfill",
|
| 628 |
+
"fulfilment": "fulfillment",
|
| 629 |
+
"fulfils": "fulfills",
|
| 630 |
+
"funnelled": "funneled",
|
| 631 |
+
"funnelling": "funneling",
|
| 632 |
+
"gage": "gauge",
|
| 633 |
+
"gaged": "gauged",
|
| 634 |
+
"gages": "gauges",
|
| 635 |
+
"gaging": "gauging",
|
| 636 |
+
"galvanise": "galvanize",
|
| 637 |
+
"galvanised": "galvanized",
|
| 638 |
+
"galvanises": "galvanizes",
|
| 639 |
+
"galvanising": "galvanizing",
|
| 640 |
+
"gambolled": "gamboled",
|
| 641 |
+
"gambolling": "gamboling",
|
| 642 |
+
"gaol": "jail",
|
| 643 |
+
"gaolbird": "jailbird",
|
| 644 |
+
"gaolbirds": "jailbirds",
|
| 645 |
+
"gaolbreak": "jailbreak",
|
| 646 |
+
"gaolbreaks": "jailbreaks",
|
| 647 |
+
"gaoled": "jailed",
|
| 648 |
+
"gaoler": "jailer",
|
| 649 |
+
"gaolers": "jailers",
|
| 650 |
+
"gaoling": "jailing",
|
| 651 |
+
"gaols": "jails",
|
| 652 |
+
"gasses": "gases",
|
| 653 |
+
"generalisation": "generalization",
|
| 654 |
+
"generalisations": "generalizations",
|
| 655 |
+
"generalise": "generalize",
|
| 656 |
+
"generalised": "generalized",
|
| 657 |
+
"generalises": "generalizes",
|
| 658 |
+
"generalising": "generalizing",
|
| 659 |
+
"ghettoise": "ghettoize",
|
| 660 |
+
"ghettoised": "ghettoized",
|
| 661 |
+
"ghettoises": "ghettoizes",
|
| 662 |
+
"ghettoising": "ghettoizing",
|
| 663 |
+
"gipsies": "gypsies",
|
| 664 |
+
"glamor": "glamour",
|
| 665 |
+
"glamorise": "glamorize",
|
| 666 |
+
"glamorised": "glamorized",
|
| 667 |
+
"glamorises": "glamorizes",
|
| 668 |
+
"glamorising": "glamorizing",
|
| 669 |
+
"globalisation": "globalization",
|
| 670 |
+
"globalise": "globalize",
|
| 671 |
+
"globalised": "globalized",
|
| 672 |
+
"globalises": "globalizes",
|
| 673 |
+
"globalising": "globalizing",
|
| 674 |
+
"glueing": "gluing",
|
| 675 |
+
"goitre": "goiter",
|
| 676 |
+
"goitres": "goiters",
|
| 677 |
+
"gonorrhoea": "gonorrhea",
|
| 678 |
+
"gramme": "gram",
|
| 679 |
+
"grammes": "grams",
|
| 680 |
+
"gravelled": "graveled",
|
| 681 |
+
"grey": "gray",
|
| 682 |
+
"greyed": "grayed",
|
| 683 |
+
"greying": "graying",
|
| 684 |
+
"greyish": "grayish",
|
| 685 |
+
"greyness": "grayness",
|
| 686 |
+
"greys": "grays",
|
| 687 |
+
"grovelled": "groveled",
|
| 688 |
+
"grovelling": "groveling",
|
| 689 |
+
"groyne": "groin",
|
| 690 |
+
"groynes": "groins",
|
| 691 |
+
"gruelling": "grueling",
|
| 692 |
+
"gruellingly": "gruelingly",
|
| 693 |
+
"gryphon": "griffin",
|
| 694 |
+
"gryphons": "griffins",
|
| 695 |
+
"gynaecological": "gynecological",
|
| 696 |
+
"gynaecologist": "gynecologist",
|
| 697 |
+
"gynaecologists": "gynecologists",
|
| 698 |
+
"gynaecology": "gynecology",
|
| 699 |
+
"haematological": "hematological",
|
| 700 |
+
"haematologist": "hematologist",
|
| 701 |
+
"haematologists": "hematologists",
|
| 702 |
+
"haematology": "hematology",
|
| 703 |
+
"haemoglobin": "hemoglobin",
|
| 704 |
+
"haemophilia": "hemophilia",
|
| 705 |
+
"haemophiliac": "hemophiliac",
|
| 706 |
+
"haemophiliacs": "hemophiliacs",
|
| 707 |
+
"haemorrhage": "hemorrhage",
|
| 708 |
+
"haemorrhaged": "hemorrhaged",
|
| 709 |
+
"haemorrhages": "hemorrhages",
|
| 710 |
+
"haemorrhaging": "hemorrhaging",
|
| 711 |
+
"haemorrhoids": "hemorrhoids",
|
| 712 |
+
"harbour": "harbor",
|
| 713 |
+
"harboured": "harbored",
|
| 714 |
+
"harbouring": "harboring",
|
| 715 |
+
"harbours": "harbors",
|
| 716 |
+
"harmonisation": "harmonization",
|
| 717 |
+
"harmonise": "harmonize",
|
| 718 |
+
"harmonised": "harmonized",
|
| 719 |
+
"harmonises": "harmonizes",
|
| 720 |
+
"harmonising": "harmonizing",
|
| 721 |
+
"homoeopath": "homeopath",
|
| 722 |
+
"homoeopathic": "homeopathic",
|
| 723 |
+
"homoeopaths": "homeopaths",
|
| 724 |
+
"homoeopathy": "homeopathy",
|
| 725 |
+
"homogenise": "homogenize",
|
| 726 |
+
"homogenised": "homogenized",
|
| 727 |
+
"homogenises": "homogenizes",
|
| 728 |
+
"homogenising": "homogenizing",
|
| 729 |
+
"honour": "honor",
|
| 730 |
+
"honourable": "honorable",
|
| 731 |
+
"honourably": "honorably",
|
| 732 |
+
"honoured": "honored",
|
| 733 |
+
"honouring": "honoring",
|
| 734 |
+
"honours": "honors",
|
| 735 |
+
"hospitalisation": "hospitalization",
|
| 736 |
+
"hospitalise": "hospitalize",
|
| 737 |
+
"hospitalised": "hospitalized",
|
| 738 |
+
"hospitalises": "hospitalizes",
|
| 739 |
+
"hospitalising": "hospitalizing",
|
| 740 |
+
"humanise": "humanize",
|
| 741 |
+
"humanised": "humanized",
|
| 742 |
+
"humanises": "humanizes",
|
| 743 |
+
"humanising": "humanizing",
|
| 744 |
+
"humour": "humor",
|
| 745 |
+
"humoured": "humored",
|
| 746 |
+
"humouring": "humoring",
|
| 747 |
+
"humourless": "humorless",
|
| 748 |
+
"humours": "humors",
|
| 749 |
+
"hybridise": "hybridize",
|
| 750 |
+
"hybridised": "hybridized",
|
| 751 |
+
"hybridises": "hybridizes",
|
| 752 |
+
"hybridising": "hybridizing",
|
| 753 |
+
"hypnotise": "hypnotize",
|
| 754 |
+
"hypnotised": "hypnotized",
|
| 755 |
+
"hypnotises": "hypnotizes",
|
| 756 |
+
"hypnotising": "hypnotizing",
|
| 757 |
+
"hypothesise": "hypothesize",
|
| 758 |
+
"hypothesised": "hypothesized",
|
| 759 |
+
"hypothesises": "hypothesizes",
|
| 760 |
+
"hypothesising": "hypothesizing",
|
| 761 |
+
"idealisation": "idealization",
|
| 762 |
+
"idealise": "idealize",
|
| 763 |
+
"idealised": "idealized",
|
| 764 |
+
"idealises": "idealizes",
|
| 765 |
+
"idealising": "idealizing",
|
| 766 |
+
"idolise": "idolize",
|
| 767 |
+
"idolised": "idolized",
|
| 768 |
+
"idolises": "idolizes",
|
| 769 |
+
"idolising": "idolizing",
|
| 770 |
+
"immobilisation": "immobilization",
|
| 771 |
+
"immobilise": "immobilize",
|
| 772 |
+
"immobilised": "immobilized",
|
| 773 |
+
"immobiliser": "immobilizer",
|
| 774 |
+
"immobilisers": "immobilizers",
|
| 775 |
+
"immobilises": "immobilizes",
|
| 776 |
+
"immobilising": "immobilizing",
|
| 777 |
+
"immortalise": "immortalize",
|
| 778 |
+
"immortalised": "immortalized",
|
| 779 |
+
"immortalises": "immortalizes",
|
| 780 |
+
"immortalising": "immortalizing",
|
| 781 |
+
"immunisation": "immunization",
|
| 782 |
+
"immunise": "immunize",
|
| 783 |
+
"immunised": "immunized",
|
| 784 |
+
"immunises": "immunizes",
|
| 785 |
+
"immunising": "immunizing",
|
| 786 |
+
"impanelled": "impaneled",
|
| 787 |
+
"impanelling": "impaneling",
|
| 788 |
+
"imperilled": "imperiled",
|
| 789 |
+
"imperilling": "imperiling",
|
| 790 |
+
"individualise": "individualize",
|
| 791 |
+
"individualised": "individualized",
|
| 792 |
+
"individualises": "individualizes",
|
| 793 |
+
"individualising": "individualizing",
|
| 794 |
+
"industrialise": "industrialize",
|
| 795 |
+
"industrialised": "industrialized",
|
| 796 |
+
"industrialises": "industrializes",
|
| 797 |
+
"industrialising": "industrializing",
|
| 798 |
+
"inflexion": "inflection",
|
| 799 |
+
"inflexions": "inflections",
|
| 800 |
+
"initialise": "initialize",
|
| 801 |
+
"initialised": "initialized",
|
| 802 |
+
"initialises": "initializes",
|
| 803 |
+
"initialising": "initializing",
|
| 804 |
+
"initialled": "initialed",
|
| 805 |
+
"initialling": "initialing",
|
| 806 |
+
"instal": "install",
|
| 807 |
+
"instalment": "installment",
|
| 808 |
+
"instalments": "installments",
|
| 809 |
+
"instals": "installs",
|
| 810 |
+
"instil": "instill",
|
| 811 |
+
"instils": "instills",
|
| 812 |
+
"institutionalisation": "institutionalization",
|
| 813 |
+
"institutionalise": "institutionalize",
|
| 814 |
+
"institutionalised": "institutionalized",
|
| 815 |
+
"institutionalises": "institutionalizes",
|
| 816 |
+
"institutionalising": "institutionalizing",
|
| 817 |
+
"intellectualise": "intellectualize",
|
| 818 |
+
"intellectualised": "intellectualized",
|
| 819 |
+
"intellectualises": "intellectualizes",
|
| 820 |
+
"intellectualising": "intellectualizing",
|
| 821 |
+
"internalisation": "internalization",
|
| 822 |
+
"internalise": "internalize",
|
| 823 |
+
"internalised": "internalized",
|
| 824 |
+
"internalises": "internalizes",
|
| 825 |
+
"internalising": "internalizing",
|
| 826 |
+
"internationalisation": "internationalization",
|
| 827 |
+
"internationalise": "internationalize",
|
| 828 |
+
"internationalised": "internationalized",
|
| 829 |
+
"internationalises": "internationalizes",
|
| 830 |
+
"internationalising": "internationalizing",
|
| 831 |
+
"ionisation": "ionization",
|
| 832 |
+
"ionise": "ionize",
|
| 833 |
+
"ionised": "ionized",
|
| 834 |
+
"ioniser": "ionizer",
|
| 835 |
+
"ionisers": "ionizers",
|
| 836 |
+
"ionises": "ionizes",
|
| 837 |
+
"ionising": "ionizing",
|
| 838 |
+
"italicise": "italicize",
|
| 839 |
+
"italicised": "italicized",
|
| 840 |
+
"italicises": "italicizes",
|
| 841 |
+
"italicising": "italicizing",
|
| 842 |
+
"itemise": "itemize",
|
| 843 |
+
"itemised": "itemized",
|
| 844 |
+
"itemises": "itemizes",
|
| 845 |
+
"itemising": "itemizing",
|
| 846 |
+
"jeopardise": "jeopardize",
|
| 847 |
+
"jeopardised": "jeopardized",
|
| 848 |
+
"jeopardises": "jeopardizes",
|
| 849 |
+
"jeopardising": "jeopardizing",
|
| 850 |
+
"jewelled": "jeweled",
|
| 851 |
+
"jeweller": "jeweler",
|
| 852 |
+
"jewellers": "jewelers",
|
| 853 |
+
"jewellery": "jewelry",
|
| 854 |
+
"judgement": "judgment",
|
| 855 |
+
"kilogramme": "kilogram",
|
| 856 |
+
"kilogrammes": "kilograms",
|
| 857 |
+
"kilometre": "kilometer",
|
| 858 |
+
"kilometres": "kilometers",
|
| 859 |
+
"labelled": "labeled",
|
| 860 |
+
"labelling": "labeling",
|
| 861 |
+
"labour": "labor",
|
| 862 |
+
"laboured": "labored",
|
| 863 |
+
"labourer": "laborer",
|
| 864 |
+
"labourers": "laborers",
|
| 865 |
+
"labouring": "laboring",
|
| 866 |
+
"labours": "labors",
|
| 867 |
+
"lacklustre": "lackluster",
|
| 868 |
+
"legalisation": "legalization",
|
| 869 |
+
"legalise": "legalize",
|
| 870 |
+
"legalised": "legalized",
|
| 871 |
+
"legalises": "legalizes",
|
| 872 |
+
"legalising": "legalizing",
|
| 873 |
+
"legitimise": "legitimize",
|
| 874 |
+
"legitimised": "legitimized",
|
| 875 |
+
"legitimises": "legitimizes",
|
| 876 |
+
"legitimising": "legitimizing",
|
| 877 |
+
"leukaemia": "leukemia",
|
| 878 |
+
"levelled": "leveled",
|
| 879 |
+
"leveller": "leveler",
|
| 880 |
+
"levellers": "levelers",
|
| 881 |
+
"levelling": "leveling",
|
| 882 |
+
"libelled": "libeled",
|
| 883 |
+
"libelling": "libeling",
|
| 884 |
+
"libellous": "libelous",
|
| 885 |
+
"liberalisation": "liberalization",
|
| 886 |
+
"liberalise": "liberalize",
|
| 887 |
+
"liberalised": "liberalized",
|
| 888 |
+
"liberalises": "liberalizes",
|
| 889 |
+
"liberalising": "liberalizing",
|
| 890 |
+
"licence": "license",
|
| 891 |
+
"licenced": "licensed",
|
| 892 |
+
"licences": "licenses",
|
| 893 |
+
"licencing": "licensing",
|
| 894 |
+
"likeable": "likable",
|
| 895 |
+
"lionisation": "lionization",
|
| 896 |
+
"lionise": "lionize",
|
| 897 |
+
"lionised": "lionized",
|
| 898 |
+
"lionises": "lionizes",
|
| 899 |
+
"lionising": "lionizing",
|
| 900 |
+
"liquidise": "liquidize",
|
| 901 |
+
"liquidised": "liquidized",
|
| 902 |
+
"liquidiser": "liquidizer",
|
| 903 |
+
"liquidisers": "liquidizers",
|
| 904 |
+
"liquidises": "liquidizes",
|
| 905 |
+
"liquidising": "liquidizing",
|
| 906 |
+
"litre": "liter",
|
| 907 |
+
"litres": "liters",
|
| 908 |
+
"localise": "localize",
|
| 909 |
+
"localised": "localized",
|
| 910 |
+
"localises": "localizes",
|
| 911 |
+
"localising": "localizing",
|
| 912 |
+
"louvre": "louver",
|
| 913 |
+
"louvred": "louvered",
|
| 914 |
+
"louvres": "louvers",
|
| 915 |
+
"lustre": "luster",
|
| 916 |
+
"magnetise": "magnetize",
|
| 917 |
+
"magnetised": "magnetized",
|
| 918 |
+
"magnetises": "magnetizes",
|
| 919 |
+
"magnetising": "magnetizing",
|
| 920 |
+
"manoeuvrability": "maneuverability",
|
| 921 |
+
"manoeuvrable": "maneuverable",
|
| 922 |
+
"manoeuvre": "maneuver",
|
| 923 |
+
"manoeuvred": "maneuvered",
|
| 924 |
+
"manoeuvres": "maneuvers",
|
| 925 |
+
"manoeuvring": "maneuvering",
|
| 926 |
+
"manoeuvrings": "maneuverings",
|
| 927 |
+
"marginalisation": "marginalization",
|
| 928 |
+
"marginalise": "marginalize",
|
| 929 |
+
"marginalised": "marginalized",
|
| 930 |
+
"marginalises": "marginalizes",
|
| 931 |
+
"marginalising": "marginalizing",
|
| 932 |
+
"marshalled": "marshaled",
|
| 933 |
+
"marshalling": "marshaling",
|
| 934 |
+
"marvelled": "marveled",
|
| 935 |
+
"marvelling": "marveling",
|
| 936 |
+
"marvellous": "marvelous",
|
| 937 |
+
"marvellously": "marvelously",
|
| 938 |
+
"materialisation": "materialization",
|
| 939 |
+
"materialise": "materialize",
|
| 940 |
+
"materialised": "materialized",
|
| 941 |
+
"materialises": "materializes",
|
| 942 |
+
"materialising": "materializing",
|
| 943 |
+
"maximisation": "maximization",
|
| 944 |
+
"maximise": "maximize",
|
| 945 |
+
"maximised": "maximized",
|
| 946 |
+
"maximises": "maximizes",
|
| 947 |
+
"maximising": "maximizing",
|
| 948 |
+
"meagre": "meager",
|
| 949 |
+
"mechanisation": "mechanization",
|
| 950 |
+
"mechanise": "mechanize",
|
| 951 |
+
"mechanised": "mechanized",
|
| 952 |
+
"mechanises": "mechanizes",
|
| 953 |
+
"mechanising": "mechanizing",
|
| 954 |
+
"mediaeval": "medieval",
|
| 955 |
+
"memorialise": "memorialize",
|
| 956 |
+
"memorialised": "memorialized",
|
| 957 |
+
"memorialises": "memorializes",
|
| 958 |
+
"memorialising": "memorializing",
|
| 959 |
+
"memorise": "memorize",
|
| 960 |
+
"memorised": "memorized",
|
| 961 |
+
"memorises": "memorizes",
|
| 962 |
+
"memorising": "memorizing",
|
| 963 |
+
"mesmerise": "mesmerize",
|
| 964 |
+
"mesmerised": "mesmerized",
|
| 965 |
+
"mesmerises": "mesmerizes",
|
| 966 |
+
"mesmerising": "mesmerizing",
|
| 967 |
+
"metabolise": "metabolize",
|
| 968 |
+
"metabolised": "metabolized",
|
| 969 |
+
"metabolises": "metabolizes",
|
| 970 |
+
"metabolising": "metabolizing",
|
| 971 |
+
"metre": "meter",
|
| 972 |
+
"metres": "meters",
|
| 973 |
+
"mhm": "hmm",
|
| 974 |
+
"micrometre": "micrometer",
|
| 975 |
+
"micrometres": "micrometers",
|
| 976 |
+
"militarise": "militarize",
|
| 977 |
+
"militarised": "militarized",
|
| 978 |
+
"militarises": "militarizes",
|
| 979 |
+
"militarising": "militarizing",
|
| 980 |
+
"milligramme": "milligram",
|
| 981 |
+
"milligrammes": "milligrams",
|
| 982 |
+
"millilitre": "milliliter",
|
| 983 |
+
"millilitres": "milliliters",
|
| 984 |
+
"millimetre": "millimeter",
|
| 985 |
+
"millimetres": "millimeters",
|
| 986 |
+
"miniaturisation": "miniaturization",
|
| 987 |
+
"miniaturise": "miniaturize",
|
| 988 |
+
"miniaturised": "miniaturized",
|
| 989 |
+
"miniaturises": "miniaturizes",
|
| 990 |
+
"miniaturising": "miniaturizing",
|
| 991 |
+
"minibusses": "minibuses",
|
| 992 |
+
"minimise": "minimize",
|
| 993 |
+
"minimised": "minimized",
|
| 994 |
+
"minimises": "minimizes",
|
| 995 |
+
"minimising": "minimizing",
|
| 996 |
+
"misbehaviour": "misbehavior",
|
| 997 |
+
"misdemeanour": "misdemeanor",
|
| 998 |
+
"misdemeanours": "misdemeanors",
|
| 999 |
+
"misspelt": "misspelled",
|
| 1000 |
+
"mitre": "miter",
|
| 1001 |
+
"mitres": "miters",
|
| 1002 |
+
"mm": "hmm",
|
| 1003 |
+
"mmm": "hmm",
|
| 1004 |
+
"mobilisation": "mobilization",
|
| 1005 |
+
"mobilise": "mobilize",
|
| 1006 |
+
"mobilised": "mobilized",
|
| 1007 |
+
"mobilises": "mobilizes",
|
| 1008 |
+
"mobilising": "mobilizing",
|
| 1009 |
+
"modelled": "modeled",
|
| 1010 |
+
"modeller": "modeler",
|
| 1011 |
+
"modellers": "modelers",
|
| 1012 |
+
"modelling": "modeling",
|
| 1013 |
+
"modernise": "modernize",
|
| 1014 |
+
"modernised": "modernized",
|
| 1015 |
+
"modernises": "modernizes",
|
| 1016 |
+
"modernising": "modernizing",
|
| 1017 |
+
"moisturise": "moisturize",
|
| 1018 |
+
"moisturised": "moisturized",
|
| 1019 |
+
"moisturiser": "moisturizer",
|
| 1020 |
+
"moisturisers": "moisturizers",
|
| 1021 |
+
"moisturises": "moisturizes",
|
| 1022 |
+
"moisturising": "moisturizing",
|
| 1023 |
+
"monologue": "monolog",
|
| 1024 |
+
"monologues": "monologs",
|
| 1025 |
+
"monopolisation": "monopolization",
|
| 1026 |
+
"monopolise": "monopolize",
|
| 1027 |
+
"monopolised": "monopolized",
|
| 1028 |
+
"monopolises": "monopolizes",
|
| 1029 |
+
"monopolising": "monopolizing",
|
| 1030 |
+
"moralise": "moralize",
|
| 1031 |
+
"moralised": "moralized",
|
| 1032 |
+
"moralises": "moralizes",
|
| 1033 |
+
"moralising": "moralizing",
|
| 1034 |
+
"motorised": "motorized",
|
| 1035 |
+
"mould": "mold",
|
| 1036 |
+
"moulded": "molded",
|
| 1037 |
+
"moulder": "molder",
|
| 1038 |
+
"mouldered": "moldered",
|
| 1039 |
+
"mouldering": "moldering",
|
| 1040 |
+
"moulders": "molders",
|
| 1041 |
+
"mouldier": "moldier",
|
| 1042 |
+
"mouldiest": "moldiest",
|
| 1043 |
+
"moulding": "molding",
|
| 1044 |
+
"mouldings": "moldings",
|
| 1045 |
+
"moulds": "molds",
|
| 1046 |
+
"mouldy": "moldy",
|
| 1047 |
+
"moult": "molt",
|
| 1048 |
+
"moulted": "molted",
|
| 1049 |
+
"moulting": "molting",
|
| 1050 |
+
"moults": "molts",
|
| 1051 |
+
"moustache": "mustache",
|
| 1052 |
+
"moustached": "mustached",
|
| 1053 |
+
"moustaches": "mustaches",
|
| 1054 |
+
"moustachioed": "mustachioed",
|
| 1055 |
+
"multicoloured": "multicolored",
|
| 1056 |
+
"nationalisation": "nationalization",
|
| 1057 |
+
"nationalisations": "nationalizations",
|
| 1058 |
+
"nationalise": "nationalize",
|
| 1059 |
+
"nationalised": "nationalized",
|
| 1060 |
+
"nationalises": "nationalizes",
|
| 1061 |
+
"nationalising": "nationalizing",
|
| 1062 |
+
"naturalisation": "naturalization",
|
| 1063 |
+
"naturalise": "naturalize",
|
| 1064 |
+
"naturalised": "naturalized",
|
| 1065 |
+
"naturalises": "naturalizes",
|
| 1066 |
+
"naturalising": "naturalizing",
|
| 1067 |
+
"neighbour": "neighbor",
|
| 1068 |
+
"neighbourhood": "neighborhood",
|
| 1069 |
+
"neighbourhoods": "neighborhoods",
|
| 1070 |
+
"neighbouring": "neighboring",
|
| 1071 |
+
"neighbourliness": "neighborliness",
|
| 1072 |
+
"neighbourly": "neighborly",
|
| 1073 |
+
"neighbours": "neighbors",
|
| 1074 |
+
"neutralisation": "neutralization",
|
| 1075 |
+
"neutralise": "neutralize",
|
| 1076 |
+
"neutralised": "neutralized",
|
| 1077 |
+
"neutralises": "neutralizes",
|
| 1078 |
+
"neutralising": "neutralizing",
|
| 1079 |
+
"normalisation": "normalization",
|
| 1080 |
+
"normalise": "normalize",
|
| 1081 |
+
"normalised": "normalized",
|
| 1082 |
+
"normalises": "normalizes",
|
| 1083 |
+
"normalising": "normalizing",
|
| 1084 |
+
"odour": "odor",
|
| 1085 |
+
"odourless": "odorless",
|
| 1086 |
+
"odours": "odors",
|
| 1087 |
+
"oesophagus": "esophagus",
|
| 1088 |
+
"oesophaguses": "esophaguses",
|
| 1089 |
+
"oestrogen": "estrogen",
|
| 1090 |
+
"offence": "offense",
|
| 1091 |
+
"offences": "offenses",
|
| 1092 |
+
"omelette": "omelet",
|
| 1093 |
+
"omelettes": "omelets",
|
| 1094 |
+
"optimise": "optimize",
|
| 1095 |
+
"optimised": "optimized",
|
| 1096 |
+
"optimises": "optimizes",
|
| 1097 |
+
"optimising": "optimizing",
|
| 1098 |
+
"organisation": "organization",
|
| 1099 |
+
"organisational": "organizational",
|
| 1100 |
+
"organisations": "organizations",
|
| 1101 |
+
"organise": "organize",
|
| 1102 |
+
"organised": "organized",
|
| 1103 |
+
"organiser": "organizer",
|
| 1104 |
+
"organisers": "organizers",
|
| 1105 |
+
"organises": "organizes",
|
| 1106 |
+
"organising": "organizing",
|
| 1107 |
+
"orthopaedic": "orthopedic",
|
| 1108 |
+
"orthopaedics": "orthopedics",
|
| 1109 |
+
"ostracise": "ostracize",
|
| 1110 |
+
"ostracised": "ostracized",
|
| 1111 |
+
"ostracises": "ostracizes",
|
| 1112 |
+
"ostracising": "ostracizing",
|
| 1113 |
+
"outmanoeuvre": "outmaneuver",
|
| 1114 |
+
"outmanoeuvred": "outmaneuvered",
|
| 1115 |
+
"outmanoeuvres": "outmaneuvers",
|
| 1116 |
+
"outmanoeuvring": "outmaneuvering",
|
| 1117 |
+
"overemphasise": "overemphasize",
|
| 1118 |
+
"overemphasised": "overemphasized",
|
| 1119 |
+
"overemphasises": "overemphasizes",
|
| 1120 |
+
"overemphasising": "overemphasizing",
|
| 1121 |
+
"oxidisation": "oxidization",
|
| 1122 |
+
"oxidise": "oxidize",
|
| 1123 |
+
"oxidised": "oxidized",
|
| 1124 |
+
"oxidises": "oxidizes",
|
| 1125 |
+
"oxidising": "oxidizing",
|
| 1126 |
+
"paederast": "pederast",
|
| 1127 |
+
"paederasts": "pederasts",
|
| 1128 |
+
"paediatric": "pediatric",
|
| 1129 |
+
"paediatrician": "pediatrician",
|
| 1130 |
+
"paediatricians": "pediatricians",
|
| 1131 |
+
"paediatrics": "pediatrics",
|
| 1132 |
+
"paedophile": "pedophile",
|
| 1133 |
+
"paedophiles": "pedophiles",
|
| 1134 |
+
"paedophilia": "pedophilia",
|
| 1135 |
+
"palaeolithic": "paleolithic",
|
| 1136 |
+
"palaeontologist": "paleontologist",
|
| 1137 |
+
"palaeontologists": "paleontologists",
|
| 1138 |
+
"palaeontology": "paleontology",
|
| 1139 |
+
"panelled": "paneled",
|
| 1140 |
+
"panelling": "paneling",
|
| 1141 |
+
"panellist": "panelist",
|
| 1142 |
+
"panellists": "panelists",
|
| 1143 |
+
"paralyse": "paralyze",
|
| 1144 |
+
"paralysed": "paralyzed",
|
| 1145 |
+
"paralyses": "paralyzes",
|
| 1146 |
+
"paralysing": "paralyzing",
|
| 1147 |
+
"parcelled": "parceled",
|
| 1148 |
+
"parcelling": "parceling",
|
| 1149 |
+
"parlour": "parlor",
|
| 1150 |
+
"parlours": "parlors",
|
| 1151 |
+
"particularise": "particularize",
|
| 1152 |
+
"particularised": "particularized",
|
| 1153 |
+
"particularises": "particularizes",
|
| 1154 |
+
"particularising": "particularizing",
|
| 1155 |
+
"passivisation": "passivization",
|
| 1156 |
+
"passivise": "passivize",
|
| 1157 |
+
"passivised": "passivized",
|
| 1158 |
+
"passivises": "passivizes",
|
| 1159 |
+
"passivising": "passivizing",
|
| 1160 |
+
"pasteurisation": "pasteurization",
|
| 1161 |
+
"pasteurise": "pasteurize",
|
| 1162 |
+
"pasteurised": "pasteurized",
|
| 1163 |
+
"pasteurises": "pasteurizes",
|
| 1164 |
+
"pasteurising": "pasteurizing",
|
| 1165 |
+
"patronise": "patronize",
|
| 1166 |
+
"patronised": "patronized",
|
| 1167 |
+
"patronises": "patronizes",
|
| 1168 |
+
"patronising": "patronizing",
|
| 1169 |
+
"patronisingly": "patronizingly",
|
| 1170 |
+
"pedalled": "pedaled",
|
| 1171 |
+
"pedalling": "pedaling",
|
| 1172 |
+
"pedestrianisation": "pedestrianization",
|
| 1173 |
+
"pedestrianise": "pedestrianize",
|
| 1174 |
+
"pedestrianised": "pedestrianized",
|
| 1175 |
+
"pedestrianises": "pedestrianizes",
|
| 1176 |
+
"pedestrianising": "pedestrianizing",
|
| 1177 |
+
"penalise": "penalize",
|
| 1178 |
+
"penalised": "penalized",
|
| 1179 |
+
"penalises": "penalizes",
|
| 1180 |
+
"penalising": "penalizing",
|
| 1181 |
+
"pencilled": "penciled",
|
| 1182 |
+
"pencilling": "penciling",
|
| 1183 |
+
"personalise": "personalize",
|
| 1184 |
+
"personalised": "personalized",
|
| 1185 |
+
"personalises": "personalizes",
|
| 1186 |
+
"personalising": "personalizing",
|
| 1187 |
+
"pharmacopoeia": "pharmacopeia",
|
| 1188 |
+
"pharmacopoeias": "pharmacopeias",
|
| 1189 |
+
"philosophise": "philosophize",
|
| 1190 |
+
"philosophised": "philosophized",
|
| 1191 |
+
"philosophises": "philosophizes",
|
| 1192 |
+
"philosophising": "philosophizing",
|
| 1193 |
+
"philtre": "filter",
|
| 1194 |
+
"philtres": "filters",
|
| 1195 |
+
"phoney": "phony",
|
| 1196 |
+
"plagiarise": "plagiarize",
|
| 1197 |
+
"plagiarised": "plagiarized",
|
| 1198 |
+
"plagiarises": "plagiarizes",
|
| 1199 |
+
"plagiarising": "plagiarizing",
|
| 1200 |
+
"plough": "plow",
|
| 1201 |
+
"ploughed": "plowed",
|
| 1202 |
+
"ploughing": "plowing",
|
| 1203 |
+
"ploughman": "plowman",
|
| 1204 |
+
"ploughmen": "plowmen",
|
| 1205 |
+
"ploughs": "plows",
|
| 1206 |
+
"ploughshare": "plowshare",
|
| 1207 |
+
"ploughshares": "plowshares",
|
| 1208 |
+
"polarisation": "polarization",
|
| 1209 |
+
"polarise": "polarize",
|
| 1210 |
+
"polarised": "polarized",
|
| 1211 |
+
"polarises": "polarizes",
|
| 1212 |
+
"polarising": "polarizing",
|
| 1213 |
+
"politicisation": "politicization",
|
| 1214 |
+
"politicise": "politicize",
|
| 1215 |
+
"politicised": "politicized",
|
| 1216 |
+
"politicises": "politicizes",
|
| 1217 |
+
"politicising": "politicizing",
|
| 1218 |
+
"popularisation": "popularization",
|
| 1219 |
+
"popularise": "popularize",
|
| 1220 |
+
"popularised": "popularized",
|
| 1221 |
+
"popularises": "popularizes",
|
| 1222 |
+
"popularising": "popularizing",
|
| 1223 |
+
"pouffe": "pouf",
|
| 1224 |
+
"pouffes": "poufs",
|
| 1225 |
+
"practise": "practice",
|
| 1226 |
+
"practised": "practiced",
|
| 1227 |
+
"practises": "practices",
|
| 1228 |
+
"practising": "practicing",
|
| 1229 |
+
"praesidium": "presidium",
|
| 1230 |
+
"praesidiums": "presidiums",
|
| 1231 |
+
"pressurisation": "pressurization",
|
| 1232 |
+
"pressurise": "pressurize",
|
| 1233 |
+
"pressurised": "pressurized",
|
| 1234 |
+
"pressurises": "pressurizes",
|
| 1235 |
+
"pressurising": "pressurizing",
|
| 1236 |
+
"pretence": "pretense",
|
| 1237 |
+
"pretences": "pretenses",
|
| 1238 |
+
"primaeval": "primeval",
|
| 1239 |
+
"prioritisation": "prioritization",
|
| 1240 |
+
"prioritise": "prioritize",
|
| 1241 |
+
"prioritised": "prioritized",
|
| 1242 |
+
"prioritises": "prioritizes",
|
| 1243 |
+
"prioritising": "prioritizing",
|
| 1244 |
+
"privatisation": "privatization",
|
| 1245 |
+
"privatisations": "privatizations",
|
| 1246 |
+
"privatise": "privatize",
|
| 1247 |
+
"privatised": "privatized",
|
| 1248 |
+
"privatises": "privatizes",
|
| 1249 |
+
"privatising": "privatizing",
|
| 1250 |
+
"professionalisation": "professionalization",
|
| 1251 |
+
"professionalise": "professionalize",
|
| 1252 |
+
"professionalised": "professionalized",
|
| 1253 |
+
"professionalises": "professionalizes",
|
| 1254 |
+
"professionalising": "professionalizing",
|
| 1255 |
+
"programme": "program",
|
| 1256 |
+
"programmes": "programs",
|
| 1257 |
+
"prologue": "prolog",
|
| 1258 |
+
"prologues": "prologs",
|
| 1259 |
+
"propagandise": "propagandize",
|
| 1260 |
+
"propagandised": "propagandized",
|
| 1261 |
+
"propagandises": "propagandizes",
|
| 1262 |
+
"propagandising": "propagandizing",
|
| 1263 |
+
"proselytise": "proselytize",
|
| 1264 |
+
"proselytised": "proselytized",
|
| 1265 |
+
"proselytiser": "proselytizer",
|
| 1266 |
+
"proselytisers": "proselytizers",
|
| 1267 |
+
"proselytises": "proselytizes",
|
| 1268 |
+
"proselytising": "proselytizing",
|
| 1269 |
+
"psychoanalyse": "psychoanalyze",
|
| 1270 |
+
"psychoanalysed": "psychoanalyzed",
|
| 1271 |
+
"psychoanalyses": "psychoanalyzes",
|
| 1272 |
+
"psychoanalysing": "psychoanalyzing",
|
| 1273 |
+
"publicise": "publicize",
|
| 1274 |
+
"publicised": "publicized",
|
| 1275 |
+
"publicises": "publicizes",
|
| 1276 |
+
"publicising": "publicizing",
|
| 1277 |
+
"pulverisation": "pulverization",
|
| 1278 |
+
"pulverise": "pulverize",
|
| 1279 |
+
"pulverised": "pulverized",
|
| 1280 |
+
"pulverises": "pulverizes",
|
| 1281 |
+
"pulverising": "pulverizing",
|
| 1282 |
+
"pummelled": "pummel",
|
| 1283 |
+
"pummelling": "pummeled",
|
| 1284 |
+
"pyjama": "pajama",
|
| 1285 |
+
"pyjamas": "pajamas",
|
| 1286 |
+
"pzazz": "pizzazz",
|
| 1287 |
+
"quarrelled": "quarreled",
|
| 1288 |
+
"quarrelling": "quarreling",
|
| 1289 |
+
"radicalise": "radicalize",
|
| 1290 |
+
"radicalised": "radicalized",
|
| 1291 |
+
"radicalises": "radicalizes",
|
| 1292 |
+
"radicalising": "radicalizing",
|
| 1293 |
+
"rancour": "rancor",
|
| 1294 |
+
"randomise": "randomize",
|
| 1295 |
+
"randomised": "randomized",
|
| 1296 |
+
"randomises": "randomizes",
|
| 1297 |
+
"randomising": "randomizing",
|
| 1298 |
+
"rationalisation": "rationalization",
|
| 1299 |
+
"rationalisations": "rationalizations",
|
| 1300 |
+
"rationalise": "rationalize",
|
| 1301 |
+
"rationalised": "rationalized",
|
| 1302 |
+
"rationalises": "rationalizes",
|
| 1303 |
+
"rationalising": "rationalizing",
|
| 1304 |
+
"ravelled": "raveled",
|
| 1305 |
+
"ravelling": "raveling",
|
| 1306 |
+
"realisable": "realizable",
|
| 1307 |
+
"realisation": "realization",
|
| 1308 |
+
"realisations": "realizations",
|
| 1309 |
+
"realise": "realize",
|
| 1310 |
+
"realised": "realized",
|
| 1311 |
+
"realises": "realizes",
|
| 1312 |
+
"realising": "realizing",
|
| 1313 |
+
"recognisable": "recognizable",
|
| 1314 |
+
"recognisably": "recognizably",
|
| 1315 |
+
"recognisance": "recognizance",
|
| 1316 |
+
"recognise": "recognize",
|
| 1317 |
+
"recognised": "recognized",
|
| 1318 |
+
"recognises": "recognizes",
|
| 1319 |
+
"recognising": "recognizing",
|
| 1320 |
+
"reconnoitre": "reconnoiter",
|
| 1321 |
+
"reconnoitred": "reconnoitered",
|
| 1322 |
+
"reconnoitres": "reconnoiters",
|
| 1323 |
+
"reconnoitring": "reconnoitering",
|
| 1324 |
+
"refuelled": "refueled",
|
| 1325 |
+
"refuelling": "refueling",
|
| 1326 |
+
"regularisation": "regularization",
|
| 1327 |
+
"regularise": "regularize",
|
| 1328 |
+
"regularised": "regularized",
|
| 1329 |
+
"regularises": "regularizes",
|
| 1330 |
+
"regularising": "regularizing",
|
| 1331 |
+
"remodelled": "remodeled",
|
| 1332 |
+
"remodelling": "remodeling",
|
| 1333 |
+
"remould": "remold",
|
| 1334 |
+
"remoulded": "remolded",
|
| 1335 |
+
"remoulding": "remolding",
|
| 1336 |
+
"remoulds": "remolds",
|
| 1337 |
+
"reorganisation": "reorganization",
|
| 1338 |
+
"reorganisations": "reorganizations",
|
| 1339 |
+
"reorganise": "reorganize",
|
| 1340 |
+
"reorganised": "reorganized",
|
| 1341 |
+
"reorganises": "reorganizes",
|
| 1342 |
+
"reorganising": "reorganizing",
|
| 1343 |
+
"revelled": "reveled",
|
| 1344 |
+
"reveller": "reveler",
|
| 1345 |
+
"revellers": "revelers",
|
| 1346 |
+
"revelling": "reveling",
|
| 1347 |
+
"revitalise": "revitalize",
|
| 1348 |
+
"revitalised": "revitalized",
|
| 1349 |
+
"revitalises": "revitalizes",
|
| 1350 |
+
"revitalising": "revitalizing",
|
| 1351 |
+
"revolutionise": "revolutionize",
|
| 1352 |
+
"revolutionised": "revolutionized",
|
| 1353 |
+
"revolutionises": "revolutionizes",
|
| 1354 |
+
"revolutionising": "revolutionizing",
|
| 1355 |
+
"rhapsodise": "rhapsodize",
|
| 1356 |
+
"rhapsodised": "rhapsodized",
|
| 1357 |
+
"rhapsodises": "rhapsodizes",
|
| 1358 |
+
"rhapsodising": "rhapsodizing",
|
| 1359 |
+
"rigour": "rigor",
|
| 1360 |
+
"rigours": "rigors",
|
| 1361 |
+
"ritualised": "ritualized",
|
| 1362 |
+
"rivalled": "rivaled",
|
| 1363 |
+
"rivalling": "rivaling",
|
| 1364 |
+
"romanticise": "romanticize",
|
| 1365 |
+
"romanticised": "romanticized",
|
| 1366 |
+
"romanticises": "romanticizes",
|
| 1367 |
+
"romanticising": "romanticizing",
|
| 1368 |
+
"rumour": "rumor",
|
| 1369 |
+
"rumoured": "rumored",
|
| 1370 |
+
"rumours": "rumors",
|
| 1371 |
+
"sabre": "saber",
|
| 1372 |
+
"sabres": "sabers",
|
| 1373 |
+
"saltpetre": "saltpeter",
|
| 1374 |
+
"sanitise": "sanitize",
|
| 1375 |
+
"sanitised": "sanitized",
|
| 1376 |
+
"sanitises": "sanitizes",
|
| 1377 |
+
"sanitising": "sanitizing",
|
| 1378 |
+
"satirise": "satirize",
|
| 1379 |
+
"satirised": "satirized",
|
| 1380 |
+
"satirises": "satirizes",
|
| 1381 |
+
"satirising": "satirizing",
|
| 1382 |
+
"saviour": "savior",
|
| 1383 |
+
"saviours": "saviors",
|
| 1384 |
+
"savour": "savor",
|
| 1385 |
+
"savoured": "savored",
|
| 1386 |
+
"savouries": "savories",
|
| 1387 |
+
"savouring": "savoring",
|
| 1388 |
+
"savours": "savors",
|
| 1389 |
+
"savoury": "savory",
|
| 1390 |
+
"scandalise": "scandalize",
|
| 1391 |
+
"scandalised": "scandalized",
|
| 1392 |
+
"scandalises": "scandalizes",
|
| 1393 |
+
"scandalising": "scandalizing",
|
| 1394 |
+
"sceptic": "skeptic",
|
| 1395 |
+
"sceptical": "skeptical",
|
| 1396 |
+
"sceptically": "skeptically",
|
| 1397 |
+
"scepticism": "skepticism",
|
| 1398 |
+
"sceptics": "skeptics",
|
| 1399 |
+
"sceptre": "scepter",
|
| 1400 |
+
"sceptres": "scepters",
|
| 1401 |
+
"scrutinise": "scrutinize",
|
| 1402 |
+
"scrutinised": "scrutinized",
|
| 1403 |
+
"scrutinises": "scrutinizes",
|
| 1404 |
+
"scrutinising": "scrutinizing",
|
| 1405 |
+
"secularisation": "secularization",
|
| 1406 |
+
"secularise": "secularize",
|
| 1407 |
+
"secularised": "secularized",
|
| 1408 |
+
"secularises": "secularizes",
|
| 1409 |
+
"secularising": "secularizing",
|
| 1410 |
+
"sensationalise": "sensationalize",
|
| 1411 |
+
"sensationalised": "sensationalized",
|
| 1412 |
+
"sensationalises": "sensationalizes",
|
| 1413 |
+
"sensationalising": "sensationalizing",
|
| 1414 |
+
"sensitise": "sensitize",
|
| 1415 |
+
"sensitised": "sensitized",
|
| 1416 |
+
"sensitises": "sensitizes",
|
| 1417 |
+
"sensitising": "sensitizing",
|
| 1418 |
+
"sentimentalise": "sentimentalize",
|
| 1419 |
+
"sentimentalised": "sentimentalized",
|
| 1420 |
+
"sentimentalises": "sentimentalizes",
|
| 1421 |
+
"sentimentalising": "sentimentalizing",
|
| 1422 |
+
"sepulchre": "sepulcher",
|
| 1423 |
+
"sepulchres": "sepulchers",
|
| 1424 |
+
"serialisation": "serialization",
|
| 1425 |
+
"serialisations": "serializations",
|
| 1426 |
+
"serialise": "serialize",
|
| 1427 |
+
"serialised": "serialized",
|
| 1428 |
+
"serialises": "serializes",
|
| 1429 |
+
"serialising": "serializing",
|
| 1430 |
+
"sermonise": "sermonize",
|
| 1431 |
+
"sermonised": "sermonized",
|
| 1432 |
+
"sermonises": "sermonizes",
|
| 1433 |
+
"sermonising": "sermonizing",
|
| 1434 |
+
"sheikh": "sheik",
|
| 1435 |
+
"shovelled": "shoveled",
|
| 1436 |
+
"shovelling": "shoveling",
|
| 1437 |
+
"shrivelled": "shriveled",
|
| 1438 |
+
"shrivelling": "shriveling",
|
| 1439 |
+
"signalise": "signalize",
|
| 1440 |
+
"signalised": "signalized",
|
| 1441 |
+
"signalises": "signalizes",
|
| 1442 |
+
"signalising": "signalizing",
|
| 1443 |
+
"signalled": "signaled",
|
| 1444 |
+
"signalling": "signaling",
|
| 1445 |
+
"smoulder": "smolder",
|
| 1446 |
+
"smouldered": "smoldered",
|
| 1447 |
+
"smouldering": "smoldering",
|
| 1448 |
+
"smoulders": "smolders",
|
| 1449 |
+
"snivelled": "sniveled",
|
| 1450 |
+
"snivelling": "sniveling",
|
| 1451 |
+
"snorkelled": "snorkeled",
|
| 1452 |
+
"snorkelling": "snorkeling",
|
| 1453 |
+
"snowplough": "snowplow",
|
| 1454 |
+
"snowploughs": "snowplow",
|
| 1455 |
+
"socialisation": "socialization",
|
| 1456 |
+
"socialise": "socialize",
|
| 1457 |
+
"socialised": "socialized",
|
| 1458 |
+
"socialises": "socializes",
|
| 1459 |
+
"socialising": "socializing",
|
| 1460 |
+
"sodomise": "sodomize",
|
| 1461 |
+
"sodomised": "sodomized",
|
| 1462 |
+
"sodomises": "sodomizes",
|
| 1463 |
+
"sodomising": "sodomizing",
|
| 1464 |
+
"solemnise": "solemnize",
|
| 1465 |
+
"solemnised": "solemnized",
|
| 1466 |
+
"solemnises": "solemnizes",
|
| 1467 |
+
"solemnising": "solemnizing",
|
| 1468 |
+
"sombre": "somber",
|
| 1469 |
+
"specialisation": "specialization",
|
| 1470 |
+
"specialisations": "specializations",
|
| 1471 |
+
"specialise": "specialize",
|
| 1472 |
+
"specialised": "specialized",
|
| 1473 |
+
"specialises": "specializes",
|
| 1474 |
+
"specialising": "specializing",
|
| 1475 |
+
"spectre": "specter",
|
| 1476 |
+
"spectres": "specters",
|
| 1477 |
+
"spiralled": "spiraled",
|
| 1478 |
+
"spiralling": "spiraling",
|
| 1479 |
+
"splendour": "splendor",
|
| 1480 |
+
"splendours": "splendors",
|
| 1481 |
+
"squirrelled": "squirreled",
|
| 1482 |
+
"squirrelling": "squirreling",
|
| 1483 |
+
"stabilisation": "stabilization",
|
| 1484 |
+
"stabilise": "stabilize",
|
| 1485 |
+
"stabilised": "stabilized",
|
| 1486 |
+
"stabiliser": "stabilizer",
|
| 1487 |
+
"stabilisers": "stabilizers",
|
| 1488 |
+
"stabilises": "stabilizes",
|
| 1489 |
+
"stabilising": "stabilizing",
|
| 1490 |
+
"standardisation": "standardization",
|
| 1491 |
+
"standardise": "standardize",
|
| 1492 |
+
"standardised": "standardized",
|
| 1493 |
+
"standardises": "standardizes",
|
| 1494 |
+
"standardising": "standardizing",
|
| 1495 |
+
"stencilled": "stenciled",
|
| 1496 |
+
"stencilling": "stenciling",
|
| 1497 |
+
"sterilisation": "sterilization",
|
| 1498 |
+
"sterilisations": "sterilizations",
|
| 1499 |
+
"sterilise": "sterilize",
|
| 1500 |
+
"sterilised": "sterilized",
|
| 1501 |
+
"steriliser": "sterilizer",
|
| 1502 |
+
"sterilisers": "sterilizers",
|
| 1503 |
+
"sterilises": "sterilizes",
|
| 1504 |
+
"sterilising": "sterilizing",
|
| 1505 |
+
"stigmatisation": "stigmatization",
|
| 1506 |
+
"stigmatise": "stigmatize",
|
| 1507 |
+
"stigmatised": "stigmatized",
|
| 1508 |
+
"stigmatises": "stigmatizes",
|
| 1509 |
+
"stigmatising": "stigmatizing",
|
| 1510 |
+
"storey": "story",
|
| 1511 |
+
"storeys": "stories",
|
| 1512 |
+
"subsidisation": "subsidization",
|
| 1513 |
+
"subsidise": "subsidize",
|
| 1514 |
+
"subsidised": "subsidized",
|
| 1515 |
+
"subsidiser": "subsidizer",
|
| 1516 |
+
"subsidisers": "subsidizers",
|
| 1517 |
+
"subsidises": "subsidizes",
|
| 1518 |
+
"subsidising": "subsidizing",
|
| 1519 |
+
"succour": "succor",
|
| 1520 |
+
"succoured": "succored",
|
| 1521 |
+
"succouring": "succoring",
|
| 1522 |
+
"succours": "succors",
|
| 1523 |
+
"sulphate": "sulfate",
|
| 1524 |
+
"sulphates": "sulfates",
|
| 1525 |
+
"sulphide": "sulfide",
|
| 1526 |
+
"sulphides": "sulfides",
|
| 1527 |
+
"sulphur": "sulfur",
|
| 1528 |
+
"sulphurous": "sulfurous",
|
| 1529 |
+
"summarise": "summarize",
|
| 1530 |
+
"summarised": "summarized",
|
| 1531 |
+
"summarises": "summarizes",
|
| 1532 |
+
"summarising": "summarizing",
|
| 1533 |
+
"swivelled": "swiveled",
|
| 1534 |
+
"swivelling": "swiveling",
|
| 1535 |
+
"symbolise": "symbolize",
|
| 1536 |
+
"symbolised": "symbolized",
|
| 1537 |
+
"symbolises": "symbolizes",
|
| 1538 |
+
"symbolising": "symbolizing",
|
| 1539 |
+
"sympathise": "sympathize",
|
| 1540 |
+
"sympathised": "sympathized",
|
| 1541 |
+
"sympathiser": "sympathizer",
|
| 1542 |
+
"sympathisers": "sympathizers",
|
| 1543 |
+
"sympathises": "sympathizes",
|
| 1544 |
+
"sympathising": "sympathizing",
|
| 1545 |
+
"synchronisation": "synchronization",
|
| 1546 |
+
"synchronise": "synchronize",
|
| 1547 |
+
"synchronised": "synchronized",
|
| 1548 |
+
"synchronises": "synchronizes",
|
| 1549 |
+
"synchronising": "synchronizing",
|
| 1550 |
+
"synthesise": "synthesize",
|
| 1551 |
+
"synthesised": "synthesized",
|
| 1552 |
+
"synthesiser": "synthesizer",
|
| 1553 |
+
"synthesisers": "synthesizers",
|
| 1554 |
+
"synthesises": "synthesizes",
|
| 1555 |
+
"synthesising": "synthesizing",
|
| 1556 |
+
"syphon": "siphon",
|
| 1557 |
+
"syphoned": "siphoned",
|
| 1558 |
+
"syphoning": "siphoning",
|
| 1559 |
+
"syphons": "siphons",
|
| 1560 |
+
"systematisation": "systematization",
|
| 1561 |
+
"systematise": "systematize",
|
| 1562 |
+
"systematised": "systematized",
|
| 1563 |
+
"systematises": "systematizes",
|
| 1564 |
+
"systematising": "systematizing",
|
| 1565 |
+
"tantalise": "tantalize",
|
| 1566 |
+
"tantalised": "tantalized",
|
| 1567 |
+
"tantalises": "tantalizes",
|
| 1568 |
+
"tantalising": "tantalizing",
|
| 1569 |
+
"tantalisingly": "tantalizingly",
|
| 1570 |
+
"tasselled": "tasseled",
|
| 1571 |
+
"technicolour": "technicolor",
|
| 1572 |
+
"temporise": "temporize",
|
| 1573 |
+
"temporised": "temporized",
|
| 1574 |
+
"temporises": "temporizes",
|
| 1575 |
+
"temporising": "temporizing",
|
| 1576 |
+
"tenderise": "tenderize",
|
| 1577 |
+
"tenderised": "tenderized",
|
| 1578 |
+
"tenderises": "tenderizes",
|
| 1579 |
+
"tenderising": "tenderizing",
|
| 1580 |
+
"terrorise": "terrorize",
|
| 1581 |
+
"terrorised": "terrorized",
|
| 1582 |
+
"terrorises": "terrorizes",
|
| 1583 |
+
"terrorising": "terrorizing",
|
| 1584 |
+
"theatre": "theater",
|
| 1585 |
+
"theatregoer": "theatergoer",
|
| 1586 |
+
"theatregoers": "theatergoers",
|
| 1587 |
+
"theatres": "theaters",
|
| 1588 |
+
"theorise": "theorize",
|
| 1589 |
+
"theorised": "theorized",
|
| 1590 |
+
"theorises": "theorizes",
|
| 1591 |
+
"theorising": "theorizing",
|
| 1592 |
+
"tonne": "ton",
|
| 1593 |
+
"tonnes": "tons",
|
| 1594 |
+
"towelled": "toweled",
|
| 1595 |
+
"towelling": "toweling",
|
| 1596 |
+
"toxaemia": "toxemia",
|
| 1597 |
+
"tranquillise": "tranquilize",
|
| 1598 |
+
"tranquillised": "tranquilized",
|
| 1599 |
+
"tranquilliser": "tranquilizer",
|
| 1600 |
+
"tranquillisers": "tranquilizers",
|
| 1601 |
+
"tranquillises": "tranquilizes",
|
| 1602 |
+
"tranquillising": "tranquilizing",
|
| 1603 |
+
"tranquillity": "tranquility",
|
| 1604 |
+
"tranquillize": "tranquilize",
|
| 1605 |
+
"tranquillized": "tranquilized",
|
| 1606 |
+
"tranquillizer": "tranquilizer",
|
| 1607 |
+
"tranquillizers": "tranquilizers",
|
| 1608 |
+
"tranquillizes": "tranquilizes",
|
| 1609 |
+
"tranquillizing": "tranquilizing",
|
| 1610 |
+
"tranquilly": "tranquility",
|
| 1611 |
+
"transistorised": "transistorized",
|
| 1612 |
+
"traumatise": "traumatize",
|
| 1613 |
+
"traumatised": "traumatized",
|
| 1614 |
+
"traumatises": "traumatizes",
|
| 1615 |
+
"traumatising": "traumatizing",
|
| 1616 |
+
"travelled": "traveled",
|
| 1617 |
+
"traveller": "traveler",
|
| 1618 |
+
"travellers": "travelers",
|
| 1619 |
+
"travelling": "traveling",
|
| 1620 |
+
"travelog": "travelogue",
|
| 1621 |
+
"travelogs": "travelogues",
|
| 1622 |
+
"trialled": "trialed",
|
| 1623 |
+
"trialling": "trialing",
|
| 1624 |
+
"tricolour": "tricolor",
|
| 1625 |
+
"tricolours": "tricolors",
|
| 1626 |
+
"trivialise": "trivialize",
|
| 1627 |
+
"trivialised": "trivialized",
|
| 1628 |
+
"trivialises": "trivializes",
|
| 1629 |
+
"trivialising": "trivializing",
|
| 1630 |
+
"tumour": "tumor",
|
| 1631 |
+
"tumours": "tumors",
|
| 1632 |
+
"tunnelled": "tunneled",
|
| 1633 |
+
"tunnelling": "tunneling",
|
| 1634 |
+
"tyrannise": "tyrannize",
|
| 1635 |
+
"tyrannised": "tyrannized",
|
| 1636 |
+
"tyrannises": "tyrannizes",
|
| 1637 |
+
"tyrannising": "tyrannizing",
|
| 1638 |
+
"tyre": "tire",
|
| 1639 |
+
"tyres": "tires",
|
| 1640 |
+
"unauthorised": "unauthorized",
|
| 1641 |
+
"uncivilised": "uncivilized",
|
| 1642 |
+
"underutilised": "underutilized",
|
| 1643 |
+
"unequalled": "unequaled",
|
| 1644 |
+
"unfavourable": "unfavorable",
|
| 1645 |
+
"unfavourably": "unfavorably",
|
| 1646 |
+
"unionisation": "unionization",
|
| 1647 |
+
"unionise": "unionize",
|
| 1648 |
+
"unionised": "unionized",
|
| 1649 |
+
"unionises": "unionizes",
|
| 1650 |
+
"unionising": "unionizing",
|
| 1651 |
+
"unorganised": "unorganized",
|
| 1652 |
+
"unravelled": "unraveled",
|
| 1653 |
+
"unravelling": "unraveling",
|
| 1654 |
+
"unrecognisable": "unrecognizable",
|
| 1655 |
+
"unrecognised": "unrecognized",
|
| 1656 |
+
"unrivalled": "unrivaled",
|
| 1657 |
+
"unsavoury": "unsavory",
|
| 1658 |
+
"untrammelled": "untrammeled",
|
| 1659 |
+
"urbanisation": "urbanization",
|
| 1660 |
+
"urbanise": "urbanize",
|
| 1661 |
+
"urbanised": "urbanized",
|
| 1662 |
+
"urbanises": "urbanizes",
|
| 1663 |
+
"urbanising": "urbanizing",
|
| 1664 |
+
"utilisable": "utilizable",
|
| 1665 |
+
"utilisation": "utilization",
|
| 1666 |
+
"utilise": "utilize",
|
| 1667 |
+
"utilised": "utilized",
|
| 1668 |
+
"utilises": "utilizes",
|
| 1669 |
+
"utilising": "utilizing",
|
| 1670 |
+
"valour": "valor",
|
| 1671 |
+
"vandalise": "vandalize",
|
| 1672 |
+
"vandalised": "vandalized",
|
| 1673 |
+
"vandalises": "vandalizes",
|
| 1674 |
+
"vandalising": "vandalizing",
|
| 1675 |
+
"vaporisation": "vaporization",
|
| 1676 |
+
"vaporise": "vaporize",
|
| 1677 |
+
"vaporised": "vaporized",
|
| 1678 |
+
"vaporises": "vaporizes",
|
| 1679 |
+
"vaporising": "vaporizing",
|
| 1680 |
+
"vapour": "vapor",
|
| 1681 |
+
"vapours": "vapors",
|
| 1682 |
+
"verbalise": "verbalize",
|
| 1683 |
+
"verbalised": "verbalized",
|
| 1684 |
+
"verbalises": "verbalizes",
|
| 1685 |
+
"verbalising": "verbalizing",
|
| 1686 |
+
"victimisation": "victimization",
|
| 1687 |
+
"victimise": "victimize",
|
| 1688 |
+
"victimised": "victimized",
|
| 1689 |
+
"victimises": "victimizes",
|
| 1690 |
+
"victimising": "victimizing",
|
| 1691 |
+
"videodisc": "videodisk",
|
| 1692 |
+
"videodiscs": "videodisks",
|
| 1693 |
+
"vigour": "vigor",
|
| 1694 |
+
"visualisation": "visualization",
|
| 1695 |
+
"visualisations": "visualizations",
|
| 1696 |
+
"visualise": "visualize",
|
| 1697 |
+
"visualised": "visualized",
|
| 1698 |
+
"visualises": "visualizes",
|
| 1699 |
+
"visualising": "visualizing",
|
| 1700 |
+
"vocalisation": "vocalization",
|
| 1701 |
+
"vocalisations": "vocalizations",
|
| 1702 |
+
"vocalise": "vocalize",
|
| 1703 |
+
"vocalised": "vocalized",
|
| 1704 |
+
"vocalises": "vocalizes",
|
| 1705 |
+
"vocalising": "vocalizing",
|
| 1706 |
+
"vulcanised": "vulcanized",
|
| 1707 |
+
"vulgarisation": "vulgarization",
|
| 1708 |
+
"vulgarise": "vulgarize",
|
| 1709 |
+
"vulgarised": "vulgarized",
|
| 1710 |
+
"vulgarises": "vulgarizes",
|
| 1711 |
+
"vulgarising": "vulgarizing",
|
| 1712 |
+
"waggon": "wagon",
|
| 1713 |
+
"waggons": "wagons",
|
| 1714 |
+
"watercolour": "watercolor",
|
| 1715 |
+
"watercolours": "watercolors",
|
| 1716 |
+
"weaselled": "weaseled",
|
| 1717 |
+
"weaselling": "weaseling",
|
| 1718 |
+
"westernisation": "westernization",
|
| 1719 |
+
"westernise": "westernize",
|
| 1720 |
+
"westernised": "westernized",
|
| 1721 |
+
"westernises": "westernizes",
|
| 1722 |
+
"westernising": "westernizing",
|
| 1723 |
+
"womanise": "womanize",
|
| 1724 |
+
"womanised": "womanized",
|
| 1725 |
+
"womaniser": "womanizer",
|
| 1726 |
+
"womanisers": "womanizers",
|
| 1727 |
+
"womanises": "womanizes",
|
| 1728 |
+
"womanising": "womanizing",
|
| 1729 |
+
"woollen": "woolen",
|
| 1730 |
+
"woollens": "woolens",
|
| 1731 |
+
"woollies": "woolies",
|
| 1732 |
+
"woolly": "wooly",
|
| 1733 |
+
"worshipped": "worshiped",
|
| 1734 |
+
"worshipper": "worshiper",
|
| 1735 |
+
"worshipping": "worshiping",
|
| 1736 |
+
"yodelled": "yodeled",
|
| 1737 |
+
"yodelling": "yodeling",
|
| 1738 |
+
"yoghourt": "yogurt",
|
| 1739 |
+
"yoghourts": "yogurts",
|
| 1740 |
+
"yoghurt": "yogurt",
|
| 1741 |
+
"yoghurts": "yogurts"
|
| 1742 |
+
}
|
whisper/whisper-large-v3-ct2-float16/preprocessor_config.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"chunk_length": 30,
|
| 3 |
+
"feature_extractor_type": "WhisperFeatureExtractor",
|
| 4 |
+
"feature_size": 128,
|
| 5 |
+
"hop_length": 160,
|
| 6 |
+
"n_fft": 400,
|
| 7 |
+
"n_samples": 480000,
|
| 8 |
+
"nb_max_frames": 3000,
|
| 9 |
+
"padding_side": "right",
|
| 10 |
+
"padding_value": 0.0,
|
| 11 |
+
"processor_class": "WhisperProcessor",
|
| 12 |
+
"return_attention_mask": false,
|
| 13 |
+
"sampling_rate": 16000
|
| 14 |
+
}
|
whisper/whisper-large-v3-ct2-float16/special_tokens_map.json
ADDED
|
@@ -0,0 +1,133 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
| 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 |
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"<|tr|>",
|
| 14 |
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"<|pl|>",
|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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"<|id|>",
|
| 21 |
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"<|hi|>",
|
| 22 |
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|
| 23 |
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"<|vi|>",
|
| 24 |
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"<|he|>",
|
| 25 |
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"<|uk|>",
|
| 26 |
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"<|el|>",
|
| 27 |
+
"<|ms|>",
|
| 28 |
+
"<|cs|>",
|
| 29 |
+
"<|ro|>",
|
| 30 |
+
"<|da|>",
|
| 31 |
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"<|hu|>",
|
| 32 |
+
"<|ta|>",
|
| 33 |
+
"<|no|>",
|
| 34 |
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"<|th|>",
|
| 35 |
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"<|ur|>",
|
| 36 |
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"<|hr|>",
|
| 37 |
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"<|bg|>",
|
| 38 |
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|
| 39 |
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"<|la|>",
|
| 40 |
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|
| 41 |
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|
| 42 |
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"<|cy|>",
|
| 43 |
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|
| 44 |
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|
| 45 |
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"<|fa|>",
|
| 46 |
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"<|lv|>",
|
| 47 |
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"<|bn|>",
|
| 48 |
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"<|sr|>",
|
| 49 |
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"<|az|>",
|
| 50 |
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|
| 51 |
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"<|kn|>",
|
| 52 |
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"<|et|>",
|
| 53 |
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"<|mk|>",
|
| 54 |
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"<|br|>",
|
| 55 |
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"<|eu|>",
|
| 56 |
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|
| 57 |
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"<|hy|>",
|
| 58 |
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"<|ne|>",
|
| 59 |
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"<|mn|>",
|
| 60 |
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|
| 61 |
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"<|kk|>",
|
| 62 |
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|
| 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 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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"<|fo|>",
|
| 84 |
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"<|ht|>",
|
| 85 |
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"<|ps|>",
|
| 86 |
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"<|tk|>",
|
| 87 |
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"<|nn|>",
|
| 88 |
+
"<|mt|>",
|
| 89 |
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"<|sa|>",
|
| 90 |
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"<|lb|>",
|
| 91 |
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"<|my|>",
|
| 92 |
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"<|bo|>",
|
| 93 |
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"<|tl|>",
|
| 94 |
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"<|mg|>",
|
| 95 |
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"<|as|>",
|
| 96 |
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"<|tt|>",
|
| 97 |
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"<|haw|>",
|
| 98 |
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"<|ln|>",
|
| 99 |
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"<|ha|>",
|
| 100 |
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"<|ba|>",
|
| 101 |
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"<|jw|>",
|
| 102 |
+
"<|su|>",
|
| 103 |
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"<|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 |
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"rstrip": false,
|
| 116 |
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"single_word": false
|
| 117 |
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},
|
| 118 |
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"eos_token": {
|
| 119 |
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"content": "<|endoftext|>",
|
| 120 |
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"lstrip": false,
|
| 121 |
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"normalized": false,
|
| 122 |
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"rstrip": false,
|
| 123 |
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"single_word": false
|
| 124 |
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},
|
| 125 |
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"pad_token": "<|endoftext|>",
|
| 126 |
+
"unk_token": {
|
| 127 |
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"content": "<|endoftext|>",
|
| 128 |
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"lstrip": false,
|
| 129 |
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"normalized": false,
|
| 130 |
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"rstrip": false,
|
| 131 |
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"single_word": false
|
| 132 |
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}
|
| 133 |
+
}
|
whisper/whisper-large-v3-ct2-float16/tokenizer.json
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whisper/whisper-large-v3-ct2-float16/tokenizer_config.json
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whisper/whisper-large-v3-ct2-float16/vocab.json
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