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AI 모델 535

• whisper-medium.en-fine-tuned-for-ATC↗

  jacktol

• PhoWhisper-base↗

  vinai

• faster-distil-whisper-large-v2↗

  Systran

• PhoWhisper-small↗

  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:

• ivrit-ai-whisper-large-v3-turbo-mlx↗

  mlx-community

• whisper-large-v3-turbo-german-ct2↗

  jimmymeister

• faster-whisper-base.en↗

  Systran

  This repository contains the conversion of openai/whisper-base.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.

• Malaysian-whisper-large-v3-turbo-v3↗

  mesolitica

  1. Distilled from Whisper Large V3 on Malaysian and Science context. 2. Better translation for Malay, Manglish, Mandarin, Tamil and Science context. 3. Word level timestamp, introduced ` ` token, **a new task!** 1. Finetune on mesolitica/Malaysian-STT-Whisper - Revision 267552e0f093068519a816112c2741939d057f48 - WanDB at https://wandb.ai/huseinzol05/malaysian-whisper-large-v3-turbo-v3 2. Stage 2, Annealing on 5% from mesolitica/Malaysian-STT-Whisper and 100% from mesolitica/Malaysian-STT-Whisper-Stage2 - Revision 5f6ca0596f01527ec2d013662cfc168d5f754461 - WanDB at https://wandb.ai/huseinzol05/malaysian-whisper-large-v3-turbo-v3-stage2 1. assembly.mp3

• whisper-large-v3↗

  OpenAI

  Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3 has the same architecture as the previous large and large-v2 models, except for the following minor differences: 1. The spectrogram input uses 128 Mel frequency bins instead of 80 2. A new language token for Cantonese

• MOSS-Audio-Tokenizer↗

  OpenMOSS-Team

  This is the code for MOSS-Audio-Tokenizer presented in MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models. **MOSSAudioTokenizer** is a unified discrete audio tokenizer based on the **Cat** (**C**ausal **A**udio **T**okenizer with **T**ransformer) architecture. Scaling to 1.6 billion parameters, it functions as a unified discrete interface, delivering both lossless-quality reconstruction and high-level semantic alignment. * **Extreme Compression & Variable Bitrate**: It compresses 24kHz raw audio into a remarkably low frame rate of 12.5Hz. Utilizing a 32-layer Residual Vector Quantizer (RVQ), it supports high-fidelity reconstruction across a wide range of bitrates, from 0.125kbps to 4kbps. * **Pure Transformer Architecture**: The model features a "CNN-free" homogeneous architecture built entirely from Causal Transformer blocks. With 1.6B combined parameters (Encoder + Decoder), it ensures exceptional scalability and supports low-latency streaming inference. * **Large-Scale General Audio Training**: Trained on 3 million hours of diverse audio data, the model excels at encoding and reconstructing all audio domains, including speech, sound effects, and music. * **Unified Semantic-Acoustic Representation**: While achieving state-of-the-art reconstruction quality, Cat produces discrete tokens that are "semantic-rich," making them ideal for downstream tasks like speech understanding (ASR) and generation (TTS). * **Fully Trained From Scratch**: Cat does not rely on any pretrained encoders (such as HuBERT or Whisper) or distillation from teacher models. All representations are learned autonomously from raw data. * **End-to-End Joint Optimization**: All components—including the encoder, quantizer, decoder, discriminator, and a decoder-only LLM for semantic alignment—are optimized jointly in a single unified training pipeline.