hallo
fudan-generative-ai基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
言語から画像、オープンウェイトからAPIまで。能力と実際の費用を比較。
次のアイデアに合うAIを見つけましょう。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
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基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基本情報を収録・詳細は順次追加
This model is part of the **FunASR** ecosystem — one industrial-grade open-source toolkit for **ASR · VAD · punctuation · speaker diarization · emotion / event · LLM-ASR**. A Star really helps the project (and keeps you updated): **Voice Activity Detection** — accurately detect speech segments in audio, essential for long-audio processing pipelines. FSMN-VAD uses a Feedforward Sequential Memory Network to detect speech/non-speech boundaries with high precision and low latency. It supports both streaming and offline modes.
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基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
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**Fun-CosyVoice 3.0** is an advanced text-to-speech (TTS) system based on large language models (LLM), surpassing its predecessor (CosyVoice 2.0) in content consistency, speaker similarity, and prosody naturalness. It is designed for zero-shot multilingual speech synthesis in the wild. - **Language Coverage**: Covers 9 common languages (Chinese, English, Japanese, Korean, German, Spanish, French, Italian, Russian), 18+ Chinese dialects/accents (Guangdong, Minnan, Sichuan, Dongbei, Shan3xi, Shan1xi, Shanghai, Tianjin, Shandong, Ningxia, Gansu, etc.) and meanwhile supports both multi-lingual/cross-lingual zero-shot voice cloning. - **Content Consistency & Naturalness**: Achieves state-of-the-art performance in content consistency, speaker similarity, and prosody naturalness. - **Pronunciation Inpainting**: Supports pronunciation inpainting of Chinese Pinyin and English CMU phonemes, providing more controllability and thus suitable for production use. - **Text Normalization**: Supports reading of numbers, special symbols and various text formats without a traditional frontend module. - **Bi-Streaming**: Support both text-in streaming and audio-out streaming, and achieves latency as low as 150ms while maintaining high-quality audio output. - **Instruct Support**: Supports various instructions such as languages, dialects, emotions, speed, volume, etc. Optionally, you can unzip `ttsfrd` resource and install `ttsfrd` package for better text normalization performance. Notice that this step is not necessary. If you do not install `ttsfrd` package, we will use wetext by default.
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基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
This model is part of the **FunASR** ecosystem — one industrial-grade open-source toolkit for **ASR · VAD · punctuation · speaker diarization · emotion / event · LLM-ASR**. A Star really helps the project (and keeps you updated): Fun-ASR-MLT-Nano-2512 is the 800M-parameter multilingual checkpoint in the Fun-ASR family. It is trained on hundreds of thousands of hours of multilingual speech and supports 31 languages, with emphasis on East and Southeast Asian languages. For the Chinese, English, and Japanese checkpoint trained on tens of millions of hours, use Fun-ASR-Nano-2512. **This repository hosts the MLT checkpoint.** Its supported-language scope differs from the standard Fun-ASR-Nano checkpoint.
翻訳待ち・原文を表示
This model is part of the **FunASR** ecosystem — one industrial-grade open-source toolkit for **ASR · VAD · punctuation · speaker diarization · emotion / event · LLM-ASR**. A Star really helps the project (and keeps you updated): > ⚡ **CPU / edge — no GPU, no Python:** run Fun-ASR-Nano as a single self-contained binary via **llama.cpp / GGUF** (like whisper.cpp), with built-in VAD. Prebuilt binaries + one-command model download → Fun-ASR-Nano-GGUF · runtime · guide Fun-ASR is an end-to-end speech recognition large model launched by Tongyi Lab. It is trained on tens of millions of hours of real speech data, possessing powerful contextual understanding capabilities and industry adaptability. It supports low-latency real-time transcription and covers 31 languages. It excels in vertical domains such as education and finance, accurately recognizing professional terminology and industry expressions, effectively addressing challenges like "hallucination" generation and language confusion, achieving "clear hearing, understanding meaning, and accurate writing."
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Standalone audio.cpp GGUF builds of FunAudioLLM/Fun-ASR-Nano-2512-hf. Each file embeds the model configuration, processor configuration, tokenizer, chat template, and the audio.cpp model package specification. The source checkpoint is pinned to revision `854d88f94205cd17d2afdb24332130d86fbe654a`. The source `model.safetensors` SHA256 is `335ca3e74917f1156690400e2c344350112950165789cf78ce3d0a367affd821`. Fun-ASR-Nano currently provides offline multilingual ASR. It does not expose streaming or timestamp output. On CUDA, audio.cpp keeps the Q8_0 encoder and adaptor weights native and loads decoder weights as BF16 by default for stable logits. An explicit `fun_asr_nano.decoder_weight_type` session option overrides that default.
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This is the Hugging Face Transformers-compatible version of Fun-ASR-Nano-2512. Fun-ASR-Nano is an end-to-end speech recognition model by FunAudioLLM, trained on tens of millions of hours of real speech data. This checkpoint supports Chinese, English, and Japanese; its Chinese coverage includes 7 dialect groups and 26 regional accents. For 31-language recognition, use the separate Fun-ASR-MLT-Nano-2512 checkpoint. Start with the released **Transformers 5.17.0** package and this native `-hf` checkpoint. A repository clone, custom model code and `trust_remote_code=True` are not required for this path. The original toolkit checkpoint and the vLLM/GGUF artifacts are different formats, not interchangeable model names.
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提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。