Qwen3.5-Omni:全面扩展,迈向原生全模态 AGI
Qwen3.5-Omni: Scaling Up, Toward Native Omni-Modal AGI
Qwen Studio 发布,集成聊天机器人、图像视频理解、图像生成、文档处理、网页搜索、工具使用及 Artifacts 功能,提供全模态 AI 一站式解决方案。
阿里发布Qwen3.5-Omni多模态模型,迈向原生全模态AGI
Qwen3.5-Omni is Qwen’s latest generation of fully omnimodal LLM, supporting the understanding of text, images, audio, and audio-visual content. Both the Thinker and Talker in Qwen3.5-Omni adopt the Hybrid-Attention MoE. Qwen3.5-Omni series includes Instruct versions in three sizes: Plus, Flash, and Light, with support for 256k long-context input. The model can process more than 10 hours of audio input and over 400 seconds of 720P audio-visual input at 1 FPS. It is natively pretrained in an omnimodal manner on massive amounts of text, visual data, and more than 100 million hours of audio-visual data, demonstrating outstanding full-modality perception and generation capabilities. Compared with Qwen3-Omni, Qwen3.5-Omni offers significantly enhanced multilingual capabilities, supporting speech recognition in 113 languages/dialects and speech generation in 36 languages/dialects. It is currently available via the Offline API and Realtime API.
Offline
Qwen3.5-Omni-Plus has achieved SOTA results on 215 audio and audio-visual understanding, reasoning, and interaction subtasks/benchmarks, covering 3 audio-visual benchmarks, 5 audio benchmarks, 8 ASR benchmarks, 156 language-specific S2TT tasks, and 43 language-specific ASR tasks. In particular, **it surpasses Gemini-3.1 Pro across general audio understanding, reasoning, recognition, translation, and dialogue, while its overall audio-visual understanding reaches the level of Gemini-3.1 Pro.**Meanwhile, its visual and text capabilities match those of Qwen3.5 models of the same size. One of Qwen3.5-Omni-Plus’s standout features is its audio and audio-visual captioning capability, which can generate controllable, detailed, and structured captions, as well as screenplay-level fine-grained descriptions, including automatic segmentation, timestamp annotation, and detailed descriptions of characters and their relationship to audio. In addition, through native multimodal scaling, we observed the emergence of a new capability in omnimodal models: directly performing coding based on audio-visual instructions, which we call Audio-Visual Vibe Coding; all of the above features are available through the Offline API. We strongly encourage users to read the Demo Section.
Realtime
Beyond its strong base capabilities, we further focused on enhancing the interactive abilities of Qwen3.5-Omni. First, we support semantic interruption by developing native turn-taking intent recognition based on Omni, which avoids interruptions caused by backchanneling and meaningless background noise; this capability is already natively supported in the API. Second, we natively support WebSearch and complex FunctionCall capabilities, enabling the model to autonomously decide whether to invoke WebSearch to respond to users’ real-time questions. Third, we support end-to-end voice control and dialogue, allowing the model to follow instructions like a human and freely control aspects such as speaking volume, speed, and emotion. Fourth, Qwen3.5-Omni supports voice cloning, allowing users to upload a voice to customize the AI Assistant’s voice; all of the above features are available through the Realtime API. Users can also modify the system prompt to change the model’s behavior, such as its conversational style or identity. Fifth, to address speech instability in streaming voice interaction caused by differences in text and speech token encoding efficiency—such as omissions, misreadings, or unclear pronunciation of numbers—we propose ARIA (Adaptive Rate Interleave Alignment), a technique that dynamically aligns text and speech units. While preserving real-time performance, ARIA significantly improves the naturalness and robustness of speech synthesis. We strongly encourage users to read the Demo Section to experience the model’s latest capabilities.
Performance#
Demo1 Audio-Visual
Below we present the comprehensive evaluation of our models against frontier models in a wide range of evaluation tasks, covering different tasks and modalities.
Audio-Visual#
| Gemini-3.1 Pro | Qwen3.5-Omni-Flash | Qwen3.5-Omni-Plus | |
|---|---|---|---|
| Text Query QA | |||
| DailyOmni | 82.7 | 81.8 | 84.6 |
| WorldSense | 65.5 | 57.9 | 62.8 |
| AVUT | 85.6 | 81.4 | 85.0 |
| AV-SpeakerBench | 75.1 | 65.2 | 71.3 |
| VideoMME (with audio) | 89.0 | 79.3 | 83.7 |
| Audio Query QA | |||
| QualcommInteractive | 66.2 | 66.3 | 68.5 |
| Caption | |||
| Omni-Cloze | 57.2 | 63.0 | 64.8 |
| Agent (tool use) | |||
| OmniGAIA | 68.9 | 33.9 | 57.2 |
VideoMME (with audio): We evaluate our model with use_audio_in_video=True.
OmniGAIA:We evaluate our model without a thinking prompt and without formatting. All results are evaluated using deepseek-v3.2-thinking.
Audio#
| Gemini-3.1-Pro | Qwen3.5-Omni-Flash | Qwen3.5-Omni-Plus | |
|---|---|---|---|
| Audio Understanding | |||
| MMAU | 81.1 | 80.4 | 82.2 |
| MMAR | 83.7 | 74.0 | 80.0 |
| MMSU | 81.3 | 72.2 | 82.8 |
| RUL-MuchoMusic | 59.6 | 60.5 | 72.4 |
| SongFormBench-HarmonixSet(acc | hr.5f | hr3f) | 75.6 |
| SongFormBench-CN(acc | hr.5f | hr3f) | 78.1 |
| Dialogue | |||
| VoiceBench | 88.9 | 87.8 | 93.1 |
| URO-Bench-Pro(U | R | O) | 69.1 |
| SpeechRole | 124.2 | 119.8 | 123.5 |
| WildSpeech-Bench | 76.3 | 72.2 | 75.4 |
| S2TT | |||
| Fleurs xx⇄zh (top59) | 29.5 | 26.9 | 30.2 |
| Fleurs xx⇄en (top59) | 34.6 | 32.0 | 35.4 |
| Fleurs xx⇄zh/en (top59) | 32.1 | 29.4 | 32.8 |
| ASR | |||
| Fleurs(top60) | 7.32 | 10.75 | 6.55 |
| CV15(zh | yue | zh-tw) | 8.59 |
| CV15(en) | 8.73 | 5.90 | 4.83 |
| Librispeech(clean | other) | 3.36 | 4.41 |
| Wenetspeech(net | meeting) | 11.53 | 14.21 |
| Kespeech | 23.67 | 4.47 | 3.46 |
| MIR-1K(vocal-only) | 8.76 | 4.94 | 4.56 |
| Opencpop | 6.83 | 1.11 | 1.49 |
SongFormBench: We use a unified prompt defining an SRT-like output timestamp format and a closed vocabulary for evaluation. The vocabulary follows the 'SongForm-HX-8Class' specified in the official codebase.
URO-Bench-Pro: We use the pro track of URO-Bench and denote the three evaluation dimensions as follows: U for Understanding, R for Reasoning, and O for Oral Conversation. We use GenStyle-en, GenStyle-zh, Multilingual tasks for oral dimension.
ASR performance is evaluated using WER/CER, where lower values indicate better performance.
Fleurs: The top59 languages are English, Chinese, Cantonese, Korean, Japanese, Vietnamese, Thai, Malay, German, Russian, Italian, French, Spanish, Portuguese, Dutch, Indonesian, Turkish, Arabic, Polish, Hindi, Urdu, Filipino, Persian, Czech, Greek, Swedish, Hebrew, Danish, Finnish, Norwegian, Icelandic, Bengali, Punjabi, Javanese, Marathi, Swahili, Ukrainian, Gujarati, Kannada, Azerbaijani, Malayalam, Cebuano, Kazakh, Romanian, Hungarian, Bulgarian, Belarusian, Catalan, Tamil, Croatian, Bosnian, Slovak, Galician, Kyrgyz, Macedonian, Slovenian, Latvian, Estonian, and Asturian; compared with the top60 list, Afrikaans is excluded because the Fleurs S2TT test set does not cover this language.
MIR-1K: Transcription is converted into Simplified Chinese.
Visual#
| Qwen3.5-Plus-NoThinking | Qwen3.5-Omni-Flash | Qwen3.5-Omni-Plus | |
|---|---|---|---|
| STEM and Puzzle | |||
| MMMU | 81.0 | 76.9 | 80.1 |
| MMMU-Pro | 73.8 | 68.2 | 73.9 |
| MathVision | 73.6 | 65.4 | 73.0 |
| Mathvista (mini) | 86.9 | 82.9 | 86.1 |
| DynaMath | 84.2 | 79.3 | 83.8 |
| ZEROBench | 6 | 1 | 5 |
| ZEROBench_sub | 31.1 | 26.0 | 34.4 |
| General VQA | |||
| RealWorldQA | 79.1 | 77.5 | 84.1 |
| MMStar | 80.3 | 75.7 | 79.4 |
| MMBench EN-DEV-v1.1 | 93.8 | 88.8 | 92.8 |
| SimpleVQA | 66.1 | 54.4 | 65.3 |
| Text Recognition and Document Understanding | |||
| CharXiv (RQ) | 74.2 | 64.4 | 72.5 |
| CC-OCR | 83.0 | 80.8 | 83.4 |
| AI2D_TEST | 92.1 | 89.0 | 91.2 |
| MMLongBench-Doc | 59.7 | 53.6 | 57.5 |
| OCRBench | 91.4 | 89.1 | 91.3 |
| Spatial Intelligence | |||
| ERQA | 53.8 | 50.0 | 54.8 |
| CountBench | 95.1 | 88.2 | 95.1 |
| RefCOCO(avg) | 95.2 | 92.6 | 95.0 |
| ODInW13 | 50.3 | 46.8 | 49.5 |
| EmbSpatialBench | 83.4 | 82.7 | 85.4 |
| Video Understanding | |||
| VideoMME(w/o sub.) | 81.0 | 77.0 | 81.9 |
| MLVU(M-Avg) | 85.1 | 81.9 | 86.8 |
| MVBench | 76.7 | 70.8 | 79.0 |
| LVBench | 68.6 | 65.7 | 71.2 |
| MMVU | 67.1 | 62.7 | 67.5 |
| MME-VideoOCR | 74.2 | 70.5 | 77.0 |
| Medical VQA | |||
| SLAKE | 82.8 | 73.1 | 84.7 |
| PMC-VQA | 62.4 | 58.7 | 62.7 |
| MedXpertQA-MM | 55.3 | 44.8 | 54.7 |
Text#
| Qwen3.5-Plus-NoThinking | Qwen3.5-Omni-Flash | Qwen3.5-Omni-Plus | |
|---|---|---|---|
| Knowledge | |||
| MMLU-Pro | 86.8 | 79.9 | 85.9 |
| MMLU-Redux | 94.3 | 90.0 | 94.2 |
| SuperGPQA | 67.4 | 54.9 | 66.4 |
| C-Eval | 92.3 | 86.0 | 92.0 |
| Instruction Following | |||
| IFEval | 89.7 | 85.2 | 89.7 |
| IFBench | 51.1 | 38.4 | 52.6 |
| Long Context | |||
| AA-LCR | 62.0 | 46.0 | 57.0 |
| LongBench v2 | 60.2 | 46.4 | 59.6 |
| STEM | |||
| GPQA | 85.9 | 76.4 | 83.9 |
| Reasoning | |||
| LiveCodeBench v6 | 67.1 | 56.6 | 65.6 |
| HMMT Nov 25 | 86.2 | 59.0 | 84.4 |
| IMOAnswerBench | 68.3 | 51.5 | 65.5 |
| General Agent | |||
| BFCL-V4 | 66.1 | 55.3 | 63.3 |
| TAU2Bench | 82.7 | 78.0 | 81.0 |
Qwen3.5-Plus-NoThinking: we use Qwen3.5-Plus-NoThinking as the primary baseline, since all models here are evaluated in the same no-thinking setting.
TAU2-Bench: we follow the official setup except for the airline domain, where all models are evaluated by applying the fixes proposed in the Claude Opus 4.5 system card.
Speech-Generation#
| ElevenLabs | Gemini-2.5 Pro | GPT-Audio | Minimax | Qwen3.5-Omni-Plus | |
|---|---|---|---|---|---|
| Custom Voice Stability | |||||
| Seed-zh | 13.08 | 2.42 | 1.11 | 1.19 | 1.07 |
| Seed-en | 1.17 | 1.18 | 1.16 | 1.35 | 1.35 |
| Seed-hard | 27.70 | 11.57 | 8.19 | 8.62 | 6.24 |
| Public-Multilingual-avg (20 lang) | 12.62 | 2.72 | 2.65 | 2.16 | 2.06 |
| Inhouse-Multilingual-avg (9 lang) | 20.63 | 6.61 | 6.72 | 11.71 | 5.82 |
Stability is measured by Word Error Rate (WER, ↓).
"Public-Multilingual-avg" refers to the average performance on the public TTS-Multilingual-Test-Set, covering 20 languages.
"Inhouse-Multilingual-avg" refers to the average performance on an internal multilingual test set built upon Fleurs, covering 9 languages.
The performance is tested with the following APIs: ElevenLabs-Multilingual-V2 (9YHcvj6GT2YYXdXww), Gemini-2.5 Pro-Preview-TTS (Achernar), GPT-Audio-2025-08-28 (Alloy) and Minimax-Speech-2.8-HD (English_expressive_narrator) in March 2026.
| ElevenLabs | Minimax | Ground-Truth | Qwen3.5-Omni-Plus | |
|---|---|---|---|---|
| Voice Clone Stability | ||||
| Public-Multilingual-avg (20 lang) | 10.29 | 2.52 | - | 1.87 |
| Inhouse-Multilingual-avg (9 lang) | - | - | 9.68 | 7.04 |
| Voice Clone Similarity | ||||
| Public-Multilingual-avg (20 lang) | 0.65 | 0.76 | - | 0.79 |
| Inhouse-Multilingual-avg (9 lang) | - | - | - | 0.80 |
Stability is measured by Word Error Rate (WER, ↓) and similarity is measured by Cosine Similarity (SIM, ↑).
"Public-Multilingual-avg" refers to the average performance on the public TTS-Multilingual-Test-Set, covering 20 languages.
"Inhouse-Multilingual-avg" refers to the average performance on an internal multilingual test set built upon Fleurs, covering 9 languages.
Empty cells (-) indicate scores not yet available or not applicable.
Architecture#
Qwen3.5-Omni continues to adopt the Thinker-Talker architecture. The Thinker receives visual and audio signals through the Vision Encoder and AuT, while audio-visual signals are interleaved and encoded with positional information using TMRoPE. The Thinker is responsible for processing omnimodal signals and outputting text, while the Talker receives multimodal inputs and text outputs from the Thinker to perform contextual speech generation. Speech representations are encoded with the RVQ method proposed in Qwen3-Omni, replacing the computationally heavy DiT operations. Thanks to the chunk-wise streaming input design and the streaming Talker design, the entire model supports realtime interaction. Unlike the dual-track Talker input in the previous generation Qwen3-Omni, the Talker adopts ARIA (Adaptive Rate Interleave Alignment) in its input organization to dynamically align text and speech units and then interleave them, thereby avoiding speech instability caused by differences in text and speech token encoding efficiency, such as omissions, misreadings, or unclear pronunciation of numbers.
Qwen3.5-Omni vs Qwen3-Omni#
| Qwen3-Omni | Qwen3.5-Omni | |
|---|---|---|
| Backbone | MoE | Hybrid-MoE |
| Sequence Length | 32k | 256k Audio: 10 hours Audio-Visual (FPS=1): 400 seconds |
| Captioning Capability | Audio | Audio-Visual |
| Intelligent Semantic Interruption | Not Supported | Supported |
| WebSearch/Tool | Not Supported | Supported |
| Voice Control | Not Supported | Supported |
| Voice Clone | Not Supported | Supported |
| Talker | Dual-Track Autoregression | Interleave |
| Text-Audio Tokenizer Rate | Fixed (1:1) | ARIA (Adaptive Rate Interleaved Alignment) |
| Speech Recognition | * 11 Multilingual Languages: Chinese, English, German, French, Italian, Thai, Korean, Japanese, Russian, Spanish, and Portuguese * 8 Chinese Dialects: Sichuanese, Shanghainese, Cantonese, Southern Min, Shaanxi dialect, Nanjing dialect, Tianjin dialect, and Beijing dialect | * 74 Multilingual Languages: Afrikaans, Arabic, Asturian, Azerbaijani, Basque, Belarusian, Bengali, Bosnian, Bulgarian, Cantonese, Catalan, Cebuano, Chinese, Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Interlingua, Italian, Japanese, Javanese, Kannada, Kazakh, Korean, Kyrgyz, Lingala, Latvian, Lithuanian, Macedonian, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Norwegian Bokmål, Norwegian Nynorsk, Oriya, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tajiki, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uyghur, and Vietnamese * 39 Chinese Dialects: Northeastern Mandarin, Guizhou dialect, Guangdong Cantonese, Henan dialect, Hong Kong Cantonese, Shanghainese, Shaanxi dialect, Tianjin dialect, Taiwanese Mandarin, Yunnan dialect, Anhui dialect, Fujian dialect, Gansu dialect, Guangdong Mandarin, Hubei dialect, Hunan dialect, Jiangxi dialect, Shandong dialect, Shanxi dialect, Sichuanese, Guangxi dialect, Hainan dialect, Chongqing dialect, Changsha dialect, Hangzhou dialect, Hefei dialect, Yinchuan dialect, Zhengzhou dialect, Shenyang dialect, Wenzhou dialect, Wuhan dialect, Kunming dialect, Taiyuan dialect, Nanchang dialect, Jinan dialect, Lanzhou dialect, Nanjing dialect, Hakka, and Southern Min |
| Speech Synthesis | * 29 Multilingual Languages: Chinese, English, German, Italian, Portuguese, Spanish, Japanese, Korean, French, Russian, Thai, Indonesian, Arabic, Vietnamese, Turkish, Finnish, Polish, Hindi, Dutch, Czech, Urdu, Tagalog, Swedish, Danish, Hebrew, Icelandic, Malay, Norwegian, and Persian * 7 Chinese Dialects: Sichuanese, Beijing dialect, Tianjin dialect, Nanjing dialect, Shaanxi dialect, Cantonese, and Southern Min |
Demo#
General Audio-Visual Understanding#
With audio-visual input, Qwen3.5-Omni-Plus can follow instructions to generate accurate, fine-grained, structured, and timestamped captions for scenarios such as audio-video analysis, shot breakdown, and content moderation.
Documentary: Complex Scenes + Animals + Sound Effects Analysis - Video Studio#
Storyline#
00:00.000 – 00:02.500
The screen is black, then a deep, low-frequency whoosh swells as the camera drifts in from space toward Earth. The planet’s night-side hemisphere glitters with city lights while the sun crests over its limb, bathing the atmosphere in a brilliant blue halo. A faint orchestral pad begins to build beneath the whoosh.
00:02.500 – 00:36.800
A rapid-fire montage of wildlife and natural scenes unfolds, each lasting roughly one second, accompanied by swelling cinematic strings, brass accents, and assorted animal sound effects that punctuate every cut:
• High-altitude aerial of cloud-wreathed mountains; wind rush layered under the score.
• Lush rainforest canopy shrouded in mist; distant bird calls echo.
• Hummingbird hovers at orange blossoms; high-pitched wing buzz audible.
• Flock of seagulls wheels across a clear sky; sharp cries pierce the music.
• Underwater vortex of schooling fish; muffled aquatic whooshes.
• Surfer rides a turquoise wave; splash and surf hiss.
• Hammerhead shark cruises above sandy seabed; bubbling ambience.
• Brown pelicans glide over muddy water; soft wing beats.
• Hippopotamus surfaces, jaws agape; guttural snort.
• Brown bear wades through a river; water splashes.
• Bald eagle lands on a stump; talon scrape and caw.
• Bison herd trots across snowy grassland; heavy hoof thuds.
• Iguana flicks forked tongue; faint rustle.
• Wildebeest thunder across rolling plains; pounding hooves.
• Elephant calf splashes in a watering hole; trumpeting call.
• Pronghorn antelope sprint through desert scrub; wind rush.
• Cheetah cub pads forward; soft paw taps.
• Red fox pounces into tall grass; muted thump.
• Lioness yawns widely; resonant growl.
• Polar bear roars amid snow; icy wind.
• Tiger snarls; fierce roar reverberates.
Throughout, the orchestra rises toward a triumphant climax.
00:36.800 – 00:41.000
The view returns to Earth from orbit. White, all-caps text fades in over the globe: “LIFE IN THE ANIMAL KINGDOM.” The music resolves into a sustained chord, then gently subsides.
00:41.000 – 00:44.000
Against the rotating Earth, the single word “Royalty” appears in white serif type at left, lingering briefly before dissolving. Ambient synth tones replace the earlier orchestral swell.
00:44.000 – 00:49.000
Screen cuts to black. Large block letters spelling “LION” materialize; each glyph is filled with moving close-ups of lion fur, eyes, and muzzle. A crystalline chime rings out, followed by a deep bass note. As the letters fade, an extreme close-up of a male lion’s face fills the frame. Flies crawl near its nose while it blinks languidly. In the lower-right corner, small white text reads “Panthera leo.” Night insects chirp softly beneath a subdued musical bed.
00:49.000 – 01:04.000
Narration begins in a calm, mid-range male voice with a neutral British accent: “Lions, majestic creatures known for their regal appearance and powerful presence, have long captivated our imagination.” Visuals alternate between the resting male lion on green grass—shaking its mane—and a lioness weaving through dense foliage. Gentle strings and light percussion underscore the narration; cicadas hum in the background.
01:04.000 – 01:24.000
As the narrator explains lions’ adaptability to savannas, grasslands, woodlands, and semi-deserts, footage shows a lioness striding through tall grass at dusk beneath a pink-tinged sky, then a tight shot of another lioness panting lightly. Music grows more rhythmic; distant lion roars blend with the score.
01:24.000 – 01:40.000
While the narrator notes lions’ distribution across sub-Saharan Africa and India’s Gir Forest, the image cuts to a CGI Earth rotating to center on Africa. Dozens of glowing red dots bloom across the continent, marking populations. Orchestral strings surge, then taper.
01:40.000 – 01:54.000
Back on the ground, an extreme close-up captures a male lion’s amber eye blinking slowly. The narrator describes the species’ robust body, broad head, prominent male mane, and tawny coat. Cut to a male lion lying in dry grass, meticulously licking its forepaw; subtle licking sounds mix with soft ambient music.
01:54.000 – 02:11.000
A lioness stands half-hidden in golden savanna grass, scanning the horizon where a distant herd grazes. The narrator remarks that the fur’s coloration provides effective camouflage. Wind rustles through the grass; the score remains gentle and observational.
02:11.000 – 02:32.000
Golden-hour light bathes a male lion with a dark, full mane as he walks purposefully through mixed green and dry brush. The narrator explains that manes vary in color and size, signify maturity, and aid in attracting mates. Music introduces brighter melodic phrases; occasional bird calls are heard.
02:32.000 – 02:40.000
On a reddish dirt track flanked by sparse vegetation, a majestic male lion strides toward camera while several small birds flutter around his paws. Warm sunset hues dominate the palette; the orchestral bed maintains a steady, dignified rhythm.
02:40.000 – 02:51.000
A lioness leads a procession of at least eight playful cubs through lush grass dotted with trees. The narrator states that lions are social animals living in prides of related females and offspring. Cubs scamper, tumble, and glance curiously at the lens. Light, uplifting strings accompany the scene; faint cub mews are audible.
02:51.000 – 03:00.000
Final tableau: two adult male lions recline side by side on sandy ground amid dry shrubs under a pale sky. One gazes ahead; the other rests its chin on its paws, occasionally blinking. The narrator has finished; only soft ambient music and distant insect chirps remain. The image holds, then gently fades to silence and black.
Visible Text#
00:37.000 – 00:40.000
“LIFE IN THE ANIMAL KINGDOM”: white, clean sans-serif capitals; centered horizontally, slightly above mid-frame; superimposed over the orbital Earth; fades in and out smoothly.
00:41.000 – 00:43.000
“Royalty”: white serif word; positioned left-center over the rotating Earth; static during its brief appearance, then fades.
00:44.000 – 00:48.000
“LION”: very large bold sans-serif capitals filling most of the frame against black; each letter contains animated close-ups of lion facial features; no outline; appears via quick fade-in, holds, then dissolves.
00:52.000 – 00:56.000
“Panthera leo”: small white sans-serif text; lower-right corner of an extreme close-up of a male lion’s face; fades in and out without movement.
(No additional textual elements appear outside these intervals.)
Speakers and Transcript#
Speaker profiles:
Narrator – Adult male, neutral British English accent, warm baritone timbre, measured pacing, informative and documentary tone.
00:56.796 – 01:04.716
Speaker: Narrator
State: Calm, authoritative; medium volume over soft ambient music and insect ambience.
Content: “Lions, majestic creatures known for their regal appearance and powerful presence, have long captivated our imagination.”
01:14.316 – 01:23.916
Speaker: Narrator
State: Steady, explanatory; slight emphasis on key terms; orchestral underscore rising gently.
Content: “They are highly adaptable animals and can be found in a variety of habitats, ranging from savannas and grasslands to woodlands and semi-desert areas.”
01:31.724 – 01:38.444
Speaker: Narrator
State: Informative, neutral; music momentarily subdued to foreground speech.
Content: “They are spread across Sub-Saharan Africa, and a small population lives in the Gir Forest of India.”
01:43.404 – 01:53.804
Speaker: Narrator
State: Descriptive, slightly emphatic on physical traits; strings swell behind voice.
Content: “Lions are characterized by their distinctive appearance, including a robust body, a broad head with a prominent mane in males, and a sleek tawny coat.”
02:05.644 – 02:10.604
Speaker: Narrator
State: Matter-of-fact; softer musical bed, light wind noise underneath.
Content: “The coloration of their fur serves as effective camouflage in their natural environment.”
02:18.508 – 02:24.188
Speaker: Narrator
State: Engaged, mildly enthusiastic; brighter musical motif begins.
Content: “Male lions are easily recognized by their impressive manes, which vary in color and size.”
02:27.788 – 02:32.108
Speaker: Narrator
State: Continues seamlessly; slight crescendo in score.
Content: “The mane is a sign of maturity and plays a role in attracting mates.”
02:42.764 – 02:50.444
Speaker: Narrator
State: Concluding, warm; uplifting strings accompany visuals of cubs.
Content: “They are social animals and are often found in groups known as prides, typically consisting of related females and their offspring.”
(There are no other human speakers; all remaining audible elements are music, animal sounds, or environmental ambience.)
Additional Notes on Audio Design#
- Music: Predominantly orchestral with cinematic scope—strings, brass, and percussion—modulating in intensity to match visual pacing. It begins with a dramatic swell during the montage, recedes to ambient textures under narration, and ends with a gentle, reflective cadence.
- Sound Effects: Carefully synchronized animal calls (roars, chirps, splashes) punctuate corresponding visuals, enhancing realism without overpowering narration.
- Mixing: Narration is consistently foregrounded; music ducks subtly whenever speech occurs, ensuring clarity. Environmental sounds are balanced to add depth yet remain secondary.
Thematic and Cultural Context#
The piece functions as a concise nature-documentary vignette introducing lions as emblematic “royalty” of the animal kingdom. By juxtaposing a global montage of diverse species with focused lion imagery and authoritative narration, it situates lions within broader ecological and cultural narratives of majesty, adaptation, and social structure. The use of orbital Earth shots and scientific nomenclature (“Panthera leo”) underscores a modern, educational intent, while the sweeping score evokes awe and reverence typical of contemporary wildlife filmmaking.
Film: Multi-Character, Multi-Shot Audio-Visual Analysis with Complex Sound Effects#
Storyline#
00:00.000 – 00:04.796
A flat, dark-green screen fills the frame. Centered white capital letters present the Motion Picture Association of America’s standard preview-approval notice. Two smaller white website addresses sit along the lower edge. No music is heard yet; the soundtrack is silent, giving the text full attention.
00:04.796 – 00:06.465
The green card cuts to black. A single, deep orchestral hit with metallic overtones blooms, establishing a tense, cinematic atmosphere.
00:06.465 – 00:10.219
Nighttime aerial footage of a sprawling metropolis glides past. Skyscrapers glitter with gold, blue, and red lights; the camera slowly dollies toward one illuminated tower. A gravelly male voice, close-miked and intimate, murmurs, “Come in close.” The orchestral bed swells beneath his words.
00:10.219 – 00:17.768
The scene snaps to the interior of a packed theatre. From a high angle, four performers—three men and one woman—stand on a glossy black circular stage ringed by white neon. A spotlight picks out a man in a dark suit and fedora who tips his hat toward the crowd. The camera cuts to the audience: an elderly Black man in a fedora watches intently. Back on stage, the female performer in a short black dress stands inside a glowing yellow rectangular frame while the fedora-wearing man gestures theatrically. The narrator continues, “Because the more you think you see… the easier it’ll be to fool you.” A sharp percussive sting punctuates the last phrase. A close-up shows a young man with tousled hair flipping a playing card; the Ace of Hearts materialises.
00:17.768 – 00:21.188
A blue lens flare sweeps across black, revealing the silver Summit Entertainment logo with its stylised mountain peak. The orchestral score surges. The view then cuts to a sweeping night shot of the Las Vegas Strip, dominated by the gold-lit Paris Las Vegas Eiffel Tower and neon signs for “BALLY’S,” “MIRAGE,” and “THE LINQ.”
00:21.188 – 00:30.113
Inside the theatre again, the four magicians—now in coordinated dark suits—stride onto the stage. The audience roars. A blue tarp is whipped away to unveil a transparent, steel-framed teleportation booth. A digital clock on the booth’s side reads “0:00.” The lead magician in a pale suit announces, “Ladies and gentlemen, for our final trick, we are going to rob a bank. On the count of three, you will be teleported through space and time to your bank in Paris.” The crowd gasps. He counts, “One, two, three!” A bassy whoosh and crackling energy sound accompany the booth’s blue flash.
00:30.113 – 00:37.955
Cut to Paris: the façade of the opulent “CRÉDIT RÉPUBLICAIN” bank at night. Inside its vault, the four magicians—now in formal evening wear—stand before a massive circular door. Back in the theatre, the female magician addresses the audience: “Everyone in this room was a victim of hard times. Some of you lost your homes, your cars. And so tonight, we’re gonna return some of that money back to you.” A torrent of euro banknotes erupts from the stage, fluttering over cheering spectators who reach up to catch the cash.
00:37.955 – 00:46.255
The magicians bow amid the falling money. The lead man proclaims, “Thank you, everyone. We are the Four Horsemen. Good night!” The audience erupts in applause. The scene shifts to a marble-floored bank interior where stern men in suits, led by an older white-haired gentleman, descend a grand staircase. He declares, “Your bank was the distraction while they set up the real trick. I was a $140 million distraction.” His voice drips with smug satisfaction.
00:46.255 – 00:55.514
A dim bar: the older Black man in a fedora confers with the white-haired banker, exchanging knowing glances. In a bright, sterile vault corridor, a technician in a white jumpsuit wheels carts of cash. The fedora-wearing elder muses, “Who doesn’t love a good magic trick?” A metallic crash and a shout of “FBI!” cut to a modern office where an agent yells, “Hands where I can see ’em!”
00:55.514 – 01:03.397
On the Las Vegas Strip, a suited man with a phone asks incredulously, “Did you say magicians robbed a bank?” A split-screen montage shows each Horseman in separate interrogation rooms, coolly toying with cards or handcuffs. One smirks, “You have what we in the business like to call ‘nothing up your sleeve.’” The camera lingers on his confident grin.
01:03.397 – 01:12.614
The interrogation intensifies. The same magician leans forward, voice low but taunting: “Because if you did, it means that you and the FBI and your friends at Interpol actually believe in magic.” A sudden slam of his cuffed hands on the table startles the interrogator. He concludes, “First rule of magic: always be the smartest guy in the room.” A quick montage flashes: a fedora tips, the Horsemen bow on stage, and the older Black man whispers, “Wanna know how they did it? Say the magic word.”
01:12.614 – 01:24.001
A female agent in a car remarks, “A year ago, these guys were a bunch of street magicians.” Cut to the Horsemen performing atop a skyscraper against a glittering cityscape. She continues, “Now they’re pulling off amazing robberies and not keeping a single cent for themselves.” The older Black man, now in a tuxedo, intones, “You do realize this is a game played out on a global scale.”
01:24.001 – 01:34.469
Scenes intercut rapidly: a white-jumpsuited trio approaches a vault; an armored truck in a parking garage explodes, spilling money; the Horsemen study a holographic blueprint of a vault; the female agent warns, “We are dealing with something far bigger than us.” The white-haired banker growls, “Expose them now and destroy them.”
01:34.469 – 01:45.605
Action escalates: a sleek private jet soars above clouds; a red sports car rockets across the Eiffel Tower’s iron lattice; a man in a dark room brandishes a card marked “THE TOWER / LA MAISON DIEU.” The fedora-wearing elder states, “Vegas was just a start. This trick was designed a long time ago.”
01:45.605 – 02:00.203
A woman with reddish-brown hair pleads, “We’re all here for the same reason.” The elder Black man counters grimly, “We cannot quit now.” A suited gunman levels a pistol in a sparse room. On stage, one Horseman is yanked upward into a blinding spotlight, vanishing. The orchestral score reaches a thunderous peak.
02:00.203 – 02:11.006
The elder Black man’s voice overlays frenetic images: “Whatever is about to follow, whatever this grand trick is… it’s really going to amaze.” A red car bursts through the roof of a graffiti-covered building, showering the night sky with cash. The audience, including the fedora-wearing elder and a blonde woman, stare upward, mouths agape.
02:11.006 – 02:17.554
The narrator delivers the final caution: “Look closely, because the closer you think you are, the less you’ll actually see.” The screen cuts to black, then a brilliant blue lens flare reveals the metallic title “NOW YOU SEE ME,” its letters gleaming with chrome reflections.
02:17.554 – 02:24.603
Against a dark backdrop, bold white text announces “MAY 31,” followed by the Facebook logo and “NowYouSeeMeMovie,” the hashtag “#NowYouSeeMe,” and a small copyright line. The music resolves with a last resonant chord, then silence as the image fades to black.
Visible Text#
00:00.000 – 00:04.796
“THE FOLLOWING PREVIEW HAS BEEN APPROVED FOR” – white uppercase sans-serif, centered on dark-green background
“APPROPRIATE AUDIENCES” – larger, bold white uppercase, centered
“BY THE MOTION PICTURE ASSOCIATION OF AMERICA, INC.” – white uppercase, centered
“www.filmratings.com” – small white lowercase, bottom left
“www.mpaa.org” – small white lowercase, bottom right
00:17.768 – 00:19.500
“SUMMIT ENTERTAINMENT” – silver uppercase serif beneath stylised mountain logo, centre screen
“A LIONSGATE COMPANY” – smaller silver uppercase, centred below main logo
00:19.500 – 00:21.188
“BALLY’S” – large red neon letters on hotel façade, upper left of frame
“MIRAGE” – white neon letters on adjacent building, mid-left
“THE LINQ” – white neon letters on right-side building
00:21.188 – 00:30.113
“0:00” – red seven-segment digits on small black display affixed to teleportation booth, stage right
00:28.000 – 00:30.000
“CRÉDIT RÉPUBLICAIN” – gold serif capitals on stone bank façade, Paris night scene
01:40.000 – 01:41.500
“THE TOWER” – black uppercase at top of tarot-style card
“LA MAISON DIEU” – black uppercase at bottom of same card
02:17.554 – 02:19.000
“NOW YOU SEE ME” – large metallic silver 3-D letters with blue lens-flare glow, centred on black
02:20.000 – 02:22.000
“MAY 31” – large white uppercase, centred
Facebook “f” logo followed by “NowYouSeeMeMovie” – white, centred below date
“#NowYouSeeMe” – white, centred below social line
“© 2013 SUMMIT ENTERTAINMENT, LLC. ALL RIGHTS RESERVED.” – very small white uppercase, bottom centre
Lionsgate stylised “L” logo – small white, bottom right
Speakers and Transcript#
Speaker profiles:
Narrator – older male, deep gravelly American voice, measured, ominous tone
Lead Magician – mid-30s male, clear mid-range American accent, showman confidence, energetic delivery
Female Magician – late-20s female, bright American accent, persuasive, enthusiastic tone
Older Banker – late-60s male, refined American accent, authoritative, smug
Fedora Elder – late-60s Black male, resonant baritone, calm, philosophical
Interrogator – 40s male, firm American accent, forceful, impatient
Street-Magician Interrogated – early-30s male, relaxed American accent, sardonic, playful
Female Agent – 30s female, steady American accent, analytical, concerned
00:08.364 – 00:09.004
Speaker: Narrator
State: low volume, intimate, ominous
Content: “Come in close.”
00:10.364 – 00:17.724
Speaker: Narrator
State: slow, cautionary, gravelly
Content: “Because the more you think you see, the easier it’ll be to fool you.”
00:19.644 – 00:23.004
Speaker: Lead Magician
State: projected, theatrical excitement
Content: “Ladies and gentlemen, for our final trick, we are going to rob a bank.”
00:23.884 – 00:29.484
Speaker: Lead Magician
State: ringing announcement, rising cadence
Content: “On the count of three, you will be teleported through space and time to your bank in Paris.”
00:29.724 – 00:31.244
Speaker: Lead Magician
State: loud, rhythmic countdown
Content: “One, two, three.”
00:31.244 – 00:36.444
Speaker: Female Magician
State: earnest, compassionate
Content: “Everyone in this room was a victim of hard times.”
00:36.604 – 00:40.764
Speaker: Female Magician
State: rallying, hopeful
Content: “Some of you lost your homes, your cars, and so tonight, we’re gonna return some of that money back to you.”
00:44.044 – 00:44.684
Speaker: Lead Magician
State: grateful, upbeat
Content: “Thank you, everyone.”
00:44.844 – 00:46.284
Speaker: Lead Magician
State: triumphant proclamation
Content: “We are the Four Horsemen.”
00:46.284 – 00:47.004
Speaker: Lead Magician
State: cheerful sign-off
Content: “Good night.”
00:48.780 – 00:52.860
Speaker: Older Banker
State: controlled, explanatory
Content: “Your bank was the distraction while they set up the real trick.”
00:53.100 – 00:57.260
Speaker: Older Banker
State: boastful, self-satisfied
Content: “I was a hundred and forty million dollar distraction.”
00:57.260 – 01:00.140
Speaker: Fedora Elder
State: amused, reflective
Content: “Who doesn’t love a good magic trick?”
01:01.100 – 01:02.540
Speaker: Interrogator
State: loud command, tense
Content: “FBI! Hands where I can see ’em.”
01:02.940 – 01:04.140
Speaker: Street-Magician Interrogated
State: sarcastic disbelief
Content: “I don’t think I heard you correctly.”
01:04.220 – 01:05.900
Speaker: Street-Magician Interrogated
State: incredulous, taunting
Content: “Did you say magicians robbed a bank?”
01:06.060 – 01:07.260
Speaker: Street-Magician Interrogated
State: smug, challenging
Content: “You are going to be played.”
01:07.260 – 01:10.540
Speaker: Street-Magician Interrogated
State: lecturing, playful
Content: “You have what we in the business like to call nothing up your sleeve.”
01:10.700 – 01:15.340
Speaker: Street-Magician Interrogated
State: mocking, confident
Content: “Because if you did, it means that you and the FBI and your friends at Interpol actually believe in magic.”
01:18.380 – 01:19.660
Speaker: Street-Magician Interrogated
State: didactic, crisp
Content: “First rule of magic, always be the smartest guy in the room.”
01:23.660 – 01:24.540
Speaker: Fedora Elder
State: teasing, conspiratorial
Content: “Wanna know how they did it?”
01:24.780 – 01:25.740
Speaker: Fedora Elder
State: inviting, playful
Content: “Say the magic word.”
01:26.700 – 01:28.940
Speaker: Female Agent
State: analytical, matter-of-fact
Content: “A year ago, these guys were a bunch of street magicians.”
01:31.180 – 01:34.860
Speaker: Female Agent
State: impressed, wary
Content: “Now they’re pulling off amazing robberies and not keeping a single cent for themselves.”
01:35.980 – 01:40.300
Speaker: Fedora Elder
State: grave, explanatory
Content: “You do realize this is a game played out on a global scale.”
01:40.780 – 01:41.900
Speaker: Fedora Elder
State: ominous, foreboding
Content: “Vegas was just a start.”
01:42.460 – 01:45.260
Speaker: Fedora Elder
State: reflective, portentous
Content: “This trick was designed a long time ago.”
01:46.220 – 01:48.460
Speaker: Female Agent
State: concerned, urgent
Content: “We are dealing with something far bigger than us.”
01:49.100 – 01:50.540
Speaker: Red-haired Woman
State: resolute, earnest
Content: “We’re all here for the same reason.”
01:50.620 – 01:51.980
Speaker: Fedora Elder
State: determined, forceful
Content: “We cannot quit now.”
01:53.660 – 01:56.060
Speaker: Older Banker
State: cold, commanding
Content: “Expose them now and destroy them.”
02:01.036 – 02:09.196
Speaker: Fedora Elder
State: anticipatory, grandiose
Content: “Whatever is about to follow, whatever this grand trick is, is really going to amaze.”
02:11.116 – 02:17.356
Speaker: Narrator
State: slow, cautionary, resonant
Content: “Look closely, because the closer you think you are, the less you’ll actually see.”
Additional Notes on Cinematic Style and Themes#
- Visual Aesthetics: The trailer juxtaposes sleek, high-contrast stage performances bathed in blue and gold lighting with gritty urban nightscapes and sterile government interiors, reinforcing the duality of spectacle versus clandestine operations.
- Motifs: Recurrent images of playing cards, vault doors, falling money, and iconic landmarks (Eiffel Tower, Las Vegas Strip) underscore themes of illusion, wealth redistribution, and global stakes.
- Sound Design: A hybrid orchestral-electronic score drives momentum, punctuated by metallic impacts, whooshes, and crowd roars that synchronize tightly with visual reveals, enhancing the sense of grand illusion.
- Narrative Arc: The trailer establishes the Four Horsemen as charismatic magician-thieves who expose corruption by stealing from the wealthy and gifting money to the public, while law enforcement and powerful financiers scramble to unmask them, setting up a cat-and-mouse game on an international scale.
Game: Determine Whether Violent Scenes Are Present Based on Audio-Visual Content#
Demo1 Demo
markdown
Please provide a detailed description of the video. It must explicitly include two specific analytical sections to identify content that is inappropriate for minors.
### Section 1: Compliance Alert (Summary)
Provide a table summarizing all flagged segments for quick review:
| Time Range | Category | Risk Level | Justification |
| :--- | :--- | :--- | :--- |
| xx:xx - xx:xx | e.g., Violence | High | e.g., Realistic physical assault observed. |
### Section 2: Summary of Safety Findings
Provide a final assessment on whether the video is suitable for minors, citing the most critical timestamps and explaining the overall risk profile. Apply a zero-tolerance policy; if there is ambiguity regarding whether a scene is harmful, err on the side of caution.
---
## Output Format:
```markdown
## Compliance Alert (Summary)
| Time Range | Category | Risk Level | Justification |
| :--- | :--- | :--- | :--- |
## Summary of Safety Findings
<Paragraphs detailing the safety assessment.>
Qwen3.5-Omni-Plus
This video is a compilation of gameplay from Mortal Kombat 11 Ultimate, focusing on the game’s special finishing moves called Fatalities. It begins by showing the main menu, where the player selects the Fatalities mode. They then choose a character from the roster, such as Shang Tsung, Shao Kahn, or The Joker, and enter a one-on-one fight. After winning the match, the player performs a unique, graphic finishing move on their defeated opponent. Each Fatality is a cinematic sequence of extreme violence, showing characters being torn apart, decapitated, frozen and shattered, or disintegrated. The video cycles through several different characters, each demonstrating their own distinct and brutal method of killing their opponent. The presentation is stylized with dark, dramatic lighting and slow-motion effects to highlight the gore. The video concludes after showcasing a series of these Fatalities.
Section 1: Compliance Alert (Summary)#
| Time Range | Category | Risk Level | Justification |
|---|---|---|---|
| 01:25 - 01:49 | Graphic Violence | High | A character is impaled, set on fire, and then ripped in half vertically, showing internal organs and a skeleton. |
| 02:26 - 02:47 | Graphic Violence | High | A character is impaled through the chest, lifted into the air, and then ripped apart at the waist, causing a massive spray of blood. |
| 03:15 - 03:38 | Graphic Violence | High | A character is frozen solid with ice powers and then shattered into many pieces. |
| 04:13 - 04:37 | Graphic Violence | High | A character’s soul is ripped out of their body and then violently torn apart by a spiritual entity. |
| 05:03 - 05:29 | Graphic Violence | High | A character is blown up by an explosive cake and then decapitated by a spinning blade. |
| 05:56 - 06:24 | Graphic Violence | High | A character is punched so hard their head explodes, and their body is then split in half. |
| 06:53 - 07:19 | Graphic Violence | High | A character is launched into the air and shredded by helicopter rotor blades, resulting in dismemberment. |
Section 2: Summary of Safety Findings#
This video is not suitable for minors. The entire video consists of gameplay from Mortal Kombat 11 Ultimate, a game rated for mature audiences due to its extreme content. The video showcases multiple Fatalities, which are graphic and sadistic finishing moves. These scenes depict realistic and brutal violence, including dismemberment, decapitation, evisceration, and characters being set on fire or shattered. The high level of blood and gore, combined with the detailed and cinematic nature of the violence, makes the content inappropriate for anyone under the age of 18.
Short Video: Daily Life Short Video Analysis#
Storyline#
00:00.000 – 00:04.300
The clip opens with a hand-held, wide-angle selfie shot. A Black man in his twenties, sporting short curly hair and a bright magenta-yellow-red striped long-sleeve top, fringed magenta skirt, matching leg warmers, and large fluffy magenta ankle attachments, fills most of the frame. Behind him stretches a sun-bleached, sandy clearing under a blue sky mottled with white clouds. Dozens of onlookers—men, women, and children in casual clothes—form a loose semicircle, many holding up smartphones. Farther back, several drummers stand beside tall, cylindrical drums. The performer shouts excitedly toward the lens, “I’m here in Ivory Coast and I’m about to do the hardest dance in the world. Let’s go!” His voice is loud, breathy, and exuberant. As he finishes, a dense wall of polyrhythmic hand-drumming erupts, accompanied by scattered cheers and whoops from the crowd.
00:04.300 – 00:15.000
The camera swings outward into a medium-wide view that now frames two principal dancers. On the left is the striped-shirt host; on the right stands a masked dancer whose face is painted solid green beneath an ornate headdress topped with red-green-white feathers. This second figure wears a flowing cape and skirt in vertical bands of green, white, and orange, plus striped leg wraps and shaggy brown ankle rattles that jingle with every step. Both men launch into vigorous footwork—rapid stamping, hopping, and spinning—sending puffs of dust into the air. The drum ensemble behind them pounds out layered rhythms while the audience claps and yells encouragement. Around 00:09 a male spectator near the camera calls out, “Why?” in playful disbelief. At roughly 00:12 the masked dancer drops into a low squat, nearly touching the ground, then springs back up, prompting louder applause.
00:15.000 – 00:18.000
The host abruptly pivots the phone back to himself for another close-up. Breathing hard, sweat glistening on his forehead, he gasps, “It is very hard. It’s even hard for me.” His grin mixes exhaustion with exhilaration. Drumbeats continue underneath, slightly muffled by his proximity to the microphone.
00:18.000 – 00:26.000
The viewpoint widens again. The two lead dancers re-engage, circling each other. The masked performer brandishes a short wooden stick, slashing it through the air in time with the drums, while the host mirrors some of the steps but struggles to keep pace, occasionally stumbling and laughing at himself. Dust swirls around their feet; sunlight flashes off metallic ornaments on the costume. The crowd’s energy rises—shouts, whistles, and rhythmic clapping blend with the relentless percussion.
00:26.000 – 00:31.000
In a climactic flourish, the masked dancer spins rapidly, cape flaring like a multicolored wheel. He closes the distance and enfolds the host in a brief celebratory hug. The host throws his head back, mouth open in a joyous shout, eyes squeezed shut. Camera shake intensifies as both men laugh and sway together amid thunderous drumming and roaring approval from spectators.
00:31.000 – 00:32.000
Still locked in the embrace, the host leans toward the lens and bellows, “W’s in the chat! Like and sub!” His tone is triumphant and teasing, aimed at an online audience. Immediately after the final word, the image freezes for a split second and the video cuts to black, ending the clip.
Visible Text#
No visible text, captions, graphics, or on-screen typography appear at any point in the video.
Speakers and Transcript#
Speaker profiles:
Host – Male, mid-20s, Black, African-American English accent; voice loud, energetic, occasionally breathless; primary on-camera performer in striped outfit.
Spectator – Unseen male voice from crowd; casual tone, brief interjection.
00:00.000 – 00:04.240
Speaker: Host
State: Shouting, highly enthusiastic, projecting over ambient noise
Content: “I’m here in Ivory Coast and I’m about to do the hardest dance in the world. Let’s go!”
00:09.000 – 00:10.000
Speaker: Spectator
State: Playful exclamation, raised voice
Content: “Why?”
00:15.200 – 00:17.600
Speaker: Host
State: Breathless, strained yet amused
Content: “It is very hard. It’s even hard for me.”
00:31.000 – 00:32.000
Speaker: Host
State: Triumphant shout, promotional tone
Content: “W’s in the chat! Like and sub!”
Additional Notes on Audio-Visual Style#
• Music: Continuous live West African drumming featuring multiple djembe-style drums producing interlocking high, mid, and low tones; tempo fast and steady, no melodic instruments detected.
• Ambient sound: Frequent crowd cheers, claps, whistles; occasional individual shouts; no discernible wind or traffic noise, indicating an outdoor but relatively sheltered setting.
• Cinematography: Entirely handheld smartphone footage; frequent rapid pans between selfie close-ups and wider shots; slight fisheye distortion suggests a wide-angle lens attachment.
• Lighting: Bright natural daylight with strong overhead sun casting short shadows; colors appear saturated, enhancing the vividness of costumes and surroundings.
• Cultural context: Costumes, masks, and drumming strongly evoke traditional Ivorian ceremonial dance forms such as those associated with the Goli or Zaouli traditions, though the presence of social-media slang (“W’s in the chat,” “Like and sub”) indicates a contemporary, internet-savvy performance intended for online sharing.
来源:Qwen:Blog Retrieval(API) · qwen.ai