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AI 模型 535

• faster-whisper-tiny-int8↗

  rhasspy

• sophiwhisper-llm↗

  HadjEmir

• Liliths-Whisper-L3.3-70b-0.2a-i1-GGUF↗

  mradermacher

• whisper-base-GGUF↗

  oxide-lab

• CrisperWhisper2.0_turbo↗

  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.`

• anime-whisper-ct2-fp16↗

  quantumcookie

• anime-whisper↗

  litagin

  **Anime Whisper** は、特に日本語のアニメ調演技セリフのドメインに特化した日本語音声認識モデルです。 このモデルは kotoba-whisper-v2.0 をベースモデルとして、約5,300時間373万ファイルのアニメ調の音声・台本データセット Galgame_Speech_ASR_16kHz でファインチューニングしたものです。 特にアニメ演技音声ドメインに特化していますが、それ以外の音声でも、他のモデルにはない特徴や高い性能を持っています。 **このモデルは初期プロンプト(initial prompt) との相性が悪いようで、設定をするとハルシネーションが起きて質が極めて劣化します。初期プロンプトを設定せずにご使用ください。** **Using an initial prompt causes a decline in quality, with the model exhibiting hallucinations and significantly degraded performance. Please avoid using an initial prompt.**

• MOSS-Audio-Tokenizer-v2↗

  OpenMOSS-Team

  This is the code for the 48khz stereo version of MOSS-Audio-Tokenizer presented in MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models. **MOSS-Audio-Tokenizer-v2** is a unified discrete audio tokenizer based on the **Cat** (**C**ausal **A**udio **T**okenizer with **T**ransformer) architecture. Scaling to 2 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 48kHz stereo audio into a remarkably low frame rate of 12.5Hz. Utilizing a 32-layer Residual Vector Quantization stack, it supports high-fidelity reconstruction across a wide range of bitrates. * **Pure Transformer Architecture**: The model features a "CNN-free" homogeneous architecture built entirely from Causal Transformer blocks. With 2B 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.

• ultravox-v0_6-llama-3_1-8b↗

  fixie-ai

  Ultravox is a multimodal Speech LLM built around a pretrained LLM (Llama, Gemma, Qwen, etc) and a speech encoder (whisper-large-v3-turbo) backbone. Ultravox is a multimodal model that can consume both speech and text as input (e.g., a text system prompt and voice user message). The input to the model is given as a text prompt with a special ` ` pseudo-token, and the model processor will replace this magic token with embeddings derived from the input audio. Using the merged embeddings as input, the model will then generate output text as usual. In v0.6 series, ultravox models are trained on expanded Hindi speech data, resulting in significantly improved speech understanding performance on Hindi and modest degradation on other languages. Additionally, the v0.6 models are also trained on noise datasets for improved noise robustness and the ability to output a special string ``((noise))`` if the input audio is too noisy or doesn't contain clear speech.

• kb-whisper-small↗

  KBLab