bart-base-chinese
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探索 AI 模型 39
bart-large-chinese
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查看详情 ↗elasticbert-large
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查看详情 ↗MOSS-Audio-4B-Instruct
OpenMOSS-TeamMOSS-Audio is an open-source **audio understanding model** from MOSI.AI, the OpenMOSS team, and Shanghai Innovation Institute. It performs unified modeling over complex real-world audio, supporting **speech understanding, environmental sound understanding, music understanding, audio captioning, time-aware QA, and complex reasoning**. In this release, we provide **four models**: **MOSS-Audio-4B-Instruct**, **MOSS-Audio-4B-Thinking**, **MOSS-Audio-8B-Instruct**, and **MOSS-Audio-8B-Thinking**. The Instruct variants are optimized for direct instruction following, while the Thinking variants provide stronger chain-of-thought reasoning capabilities. Understanding audio requires more than simply transcribing words — it demands the ability to perceive acoustic cues, recognize speakers and emotions, interpret environmental sounds, reason over temporal context, and handle complex multi-step inference. **MOSS-Audio** is built to unify these capabilities within a single model. MOSS-Audio follows a modular design comprising three components: an audio encoder, a modality adapter, and a large language model. Raw audio is first encoded by **MOSS-Audio-Encoder** into continuous temporal representations at **12.5 Hz**, which are then projected into the language model's embedding space through the adapter and finally consumed by the LLM for auto-regressive text generation.
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查看详情 ↗MOSS-Audio-4B-Thinking
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查看详情 ↗MOSS-Audio-8B-Instruct
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查看详情 ↗MOSS-Audio-8B-Thinking
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查看详情 ↗MOSS-Audio-Tokenizer
OpenMOSS-TeamThis 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.
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查看详情 ↗MOSS-Audio-Tokenizer-Nano
OpenMOSS-TeamThis repository contains the Hugging Face remote-code implementation and weights for **MOSS-Audio-Tokenizer-Nano**, the lightweight audio tokenizer used by **MOSS-TTS-Nano**. MOSS-Audio-Tokenizer-Nano is a compact discrete audio tokenizer based on the **Cat** (**C**ausal **A**udio **T**okenizer with **T**ransformer) architecture from MOSS-Audio-Tokenizer: Scaling Audio Tokenizers for Future Audio Foundation Models. The checkpoint in this repository has **21,969,664 parameters** (approximately **22M**), making it much smaller than the full-size MOSS-Audio-Tokenizer while preserving the 48 kHz stereo tokenizer interface used by the MOSS-TTS family. - **Small model size**: approximately **22M parameters**, including about 10.45M encoder parameters, 10.45M decoder parameters, and 1.07M quantizer parameters. - **Native high-resolution audio**: supports **48 kHz** input and output with **2-channel stereo** audio, helping reduce compression loss and improve listening quality. - **Low-frame-rate discrete codes**: compresses 48 kHz stereo audio into a **12.5 Hz** token stream with a downsample rate of 7,680 samples. - **Variable bitrate reconstruction**: uses a residual quantizer stack with **16 codebooks** and 1,024 entries per codebook. Each codebook contributes about **0.125 kbps**, for an inference range from **0.125 kbps to 2 kbps**. - **Transformer-based tokenizer**: uses causal Transformer blocks and supports low-latency streaming encode/decode. - **MOSS-TTS family interface**: designed as the audio tokenizer backbone for MOSS-TTS-Nano and compatible MOSS-TTS-family workflows.
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查看详情 ↗MOSS-Audio-Tokenizer-v2
OpenMOSS-TeamThis 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.
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查看详情 ↗MOSS-Image-Base
OpenMOSS-TeamMOSS-Image-Base is the base text-to-image model in the **MOSS-Image** family from OpenMOSS. It supports natural-language prompts, tag sequences, and JSON layouts for controlling text placement and object arrangement. Use Python 3.10 or later and an NVIDIA GPU with CUDA support. Install a PyTorch 2.9 or later build compatible with your CUDA environment, then install: The model and custom pipeline code are downloaded and cached automatically. You can also pass a complete local model directory to `from_pretrained`. The transformer and text encoder use bfloat16; the VAE and initial noise use float32. The bundled VAE uses the FLUX.2 small decoder.
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查看详情 ↗MOSS-Image-SFT
OpenMOSS-TeamMOSS-Image-SFT is a supervised fine-tuned text-to-image model in the **MOSS-Image** family from OpenMOSS, derived from MOSS-Image-Base. It uses the same architecture and inference pipeline, supporting natural-language prompts, tag sequences, and JSON layouts for controlling text placement and object arrangement. Use Python 3.10 or later and an NVIDIA GPU with CUDA support. Install a PyTorch 2.9 or later build compatible with your CUDA environment, then install: The model and custom pipeline code are downloaded and cached automatically. You can also pass a complete local model directory to `from_pretrained`. The transformer and text encoder use bfloat16; the VAE and initial noise use float32. The bundled VAE uses the FLUX.2 small decoder.
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查看详情 ↗MOSS-Music-8B-Instruct
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查看详情 ↗MOSS-Music-8B-Thinking
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查看详情 ↗MOSS-SoundEffect
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查看详情 ↗MOSS-SoundEffect-v2.0
OpenMOSS-Team**MOSS-SoundEffect v2.0** is a text-to-audio model with a Diffusion Transformer (DiT) backbone trained with the Flow Matching objective, paired with a DAC VAE and a Qwen3 text encoder. It generates high-fidelity environmental, urban, creature, and human-action sound effects from natural-language prompts, with controllable duration up to 30 seconds at 48 kHz. Within the MOSS-TTS Family, MOSS-SoundEffect is the dedicated **text-to-sound** model — the family member that turns natural-language captions into non-speech audio (ambience, urban scenes, creatures, human actions, short music-like clips). v2.0 supersedes the v1 discrete-token autoregressive backbone (`MossTTSDelay`) with a continuous-latent **Diffusion Transformer + Flow Matching** design. We recommend a clean, isolated Python 3.12 environment to avoid dependency conflicts with the top-level MOSS-TTS environment.
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查看详情 ↗MOSS-Transcribe-Diarize
OpenMOSS-TeamMOSS-Transcribe-Diarize 0.9B is an end-to-end audio understanding model for long-form multi-speaker transcription, diarization, timestamps, and acoustic event awareness. It supports transcription and diarization across **50+** languages, single-pass inference on audio recordings up to **90 minutes** long, and custom hotword prompting for domain-specific terms. Given an audio or video file, the model generates a compact speaker-aware transcript in one pass, including timestamps and anonymous speaker labels such as `[S01]`, `[S02]`, and beyond.
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查看详情 ↗MOSS-Transcribe-preview-2B
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查看详情 ↗MOSS-TTS
OpenMOSS-TeamMOSS‑TTS Family is an open‑source **speech and sound generation model family** from MOSI.AI and the OpenMOSS team. It is designed for **high‑fidelity**, **high‑expressiveness**, and **complex real‑world scenarios**, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS. When a single piece of audio needs to **sound like a real person**, **pronounce every word accurately**, **switch speaking styles across content**, **remain stable over tens of minutes**, and **support dialogue, role‑play, and real‑time interaction**, a single TTS model is often not enough. The **MOSS‑TTS Family** breaks the workflow into five production‑ready models that can be used independently or composed into a complete pipeline. We train **MossTTSDelay** and **MossTTSLocal** as complementary baselines under one training/evaluation setup: **Delay** emphasizes long-context stability, inference speed, and production readiness, while **Local** emphasizes lightweight flexibility and strong objective performance for streaming-oriented systems. Together they provide reproducible references for deployment and research.
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查看详情 ↗MOSS-TTS-GGUF
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查看详情 ↗MOSS-TTS-Local-Transformer
OpenMOSS-TeamMOSS‑TTS Family is an open‑source **speech and sound generation model family** from MOSI.AI and the OpenMOSS team. It is designed for **high‑fidelity**, **high‑expressiveness**, and **complex real‑world scenarios**, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS. When a single piece of audio needs to **sound like a real person**, **pronounce every word accurately**, **switch speaking styles across content**, **remain stable over tens of minutes**, and **support dialogue, role‑play, and real‑time interaction**, a single TTS model is often not enough. The **MOSS‑TTS Family** breaks the workflow into five production‑ready models that can be used independently or composed into a complete pipeline. - **MOSS‑TTS**: The flagship production model featuring high fidelity and optimal zero-shot voice cloning. It supports **long-speech generation**, **fine-grained control over Pinyin, phonemes, and duration**, as well as **multilingual/code-switched synthesis**. - **MOSS‑TTSD**: A spoken dialogue generation model for expressive, multi-speaker, and ultra-long dialogues. The new **v1.0 version** achieves **industry-leading performance on objective metrics** and **outperformed top closed-source models like Doubao and Gemini 2.5-pro** in subjective evaluations. You can visit the MOSS-TTSD repository for details. - **MOSS‑VoiceGenerator**: An open-source voice design model capable of generating diverse voices and styles directly from text prompts, **without any reference speech**. It unifies voice design, style control, and synthesis, functioning independently or as a design layer for downstream TTS. Its performance **surpasses other top-tier voice design models in arena ratings**. - **MOSS‑TTS‑Realtime**: A multi-turn context-aware model for real-time voice agents. It uses incremental synthesis to ensure natural and coherent replies, making it **ideal for building low-latency voice agents when paired with text models**. The TTFB (Time To First Byte) of MOSS-TTS-Realtime reaches 180 ms, and the $T_{\text{LLM-first-sentence}} + T_{\text{MOSS-TTS-Realtime-TTFB}}$ is 377 ms. - **MOSS‑SoundEffect**: A content creation model specialized in **sound effect generation** with wide category coverage and controllable duration. It generates audio for natural environments, urban scenes, biological
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查看详情 ↗MOSS-TTS-Local-Transformer-v1.5
OpenMOSS-Team**MOSS-TTS-Local-Transformer-v1.5** is continued from MOSS-TTS-Local-Transformer-v1.0. It preserves the main 1.0 capabilities, including zero-shot voice cloning, long-form speech generation, token-level duration control, Pinyin/IPA pronunciation control, multilingual synthesis, and code-switching. For the full 1.0 feature walkthrough, input schema, and evaluation tables, please refer to the MOSS-TTS-Local-Transformer-v1.0 README. Compared with MOSS-TTS-Local-Transformer-v1.0, v1.5 focuses on the following improvements: - **Higher-fidelity stereo audio modeling**: v1.5 uses MOSS-Audio-Tokenizer-v2 as the audio tokenizer, supporting native 48 kHz stereo input and output for richer spatial detail and more natural perceived audio quality. Since the codec output is stereo, save the `[channels, samples]` tensor returned by `processor.decode(...)` directly. - **Stronger multilingual synthesis with language tags**: when the `language` field is omitted, v1.5 may improve some languages and regress slightly on others compared with 1.0. When the language is specified, v1.5 is stronger than 1.0 on almost all supported languages. Set the tag when building the user message, for example `processor.build_user_message(text=text_fr, language="French")`. - **More stable voice cloning**: v1.5 improves speaker similarity and reduces cloning variance, making repeated generations more consistent. - **Better long-reference, short-text cloning**: v1.5 handles scenarios where the reference audio is much longer than the target text more reliably than 1.0. - **More stable punctuation-following prosody**: v1.5 follows punctuation-driven pauses more closely, especially in long sentences. - **Explicit pause control**: v1.5 supports inline pause markers such as `"[pause 3.2s]"`. For example, `我今天学习了一首中国的古诗，它的名字是[pause 3.2s]静夜思！` inserts an explicit 3.2s pause before `静夜思`. MOSS-TTS Local Transformer v1.5 supports **31 languages**. It keeps the 20 languages supported by MOSS-TTS-Local-Transformer-v1.0 and extends multilingual continued training to additional languages including Cantonese, Dutch, Finnish, Hindi, Macedonian, Malay, Romanian, Swahili, Tagalog, Thai, and Vietnamese.
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。