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AI モデル 535

• PhoWhisper-large↗

  vinai

  We introduce **PhoWhisper** in five versions for Vietnamese automatic speech recognition. PhoWhisper's robustness is achieved through fine-tuning the multilingual Whisper on an 844-hour dataset that encompasses diverse Vietnamese accents. Our experimental study demonstrates state-of-the-art performances of PhoWhisper on benchmark Vietnamese ASR datasets. Please **cite** our PhoWhisper paper when it is used to help produce published results or is incorporated into other software:

• whisper-small-cantonese↗

  alvanlii

• whisper-large-v3-turbo-asr-fp16↗

  mlx-community

  This model was converted to MLX format from `./whisper-large-v3-turbo` using mlx-audio version **0.2.10**. Refer to the original model card for more details on the model.

• whisper-tiny↗

  onnx-community

  https://huggingface.co/openai/whisper-tiny with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

• whisper-large-v3-sk↗

  kinit

• whisper-small-khmer↗

  seanghay

• whisper-medium-mlx↗

  mlx-community

• distil-whisper-large-v3-int8-ov↗

  OpenVINO

  This is distil-large-v3 model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to INT8 by NNCF. The original model is distributed under mit license. More details can be found in original model card. Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.

• faster-whisper-tiny.en↗

  Systran

  This repository contains the conversion of openai/whisper-tiny.en to the CTranslate2 model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as faster-whisper. Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the `compute_type` option in CTranslate2.

• CrisperWhisper2.0_large↗

  nyralabs

  **The most accurate verbatim speech recognition you can run in production: controllable, multilingual, and timed to the word.** Most speech-to-text systems never actually decide whether to write down what was *said* or what was *meant*. They inherit that choice from their training data and apply it inconsistently. CrisperWhisper 2.0 makes it an explicit, controllable choice. One recording, two transcripts: > **Verbatim**, exactly what was said, in one consistent format: > `[um] so we we need to, to reschedule the th- thursday meeting to [uh] march third at nine thirty [laughter]` > > **Intended**, the clean version the speaker meant, with numbers, dates, > and emails formatted the way you'd write them: > `So we need to reschedule the Thursday meeting to March 3 at 9:30.`