Overview: detailed descriptions of sound
Qwen3-Omni-30B-A3B-Captioner is a specialized audio-description model fine-tuned from Qwen3-Omni-30B-A3B-Instruct. It turns an audio clip into a detailed textual account of what is heard. Qwen designed it for varied material, including speech, environmental sound, music, cinematic effects and mixtures of several sound sources. This makes it useful for preparing a searchable description of an audio asset or reviewing the sound layers in a short media segment.
Speech and non-speech understanding
The maker describes analysis of multiple speakers, multilingual expressions, vocal emotion and the intentions suggested by delivery. Its examples go beyond a transcript: they describe how voices sound, the language of speech and the surrounding acoustic scene. For non-speech audio, the model describes environmental atmosphere, real-world sounds, musical layers and dynamic details in film or media.
Descriptions of mood, speaker intention or cultural context are interpretations of the audible material. Treat them separately from directly checkable speech and sound events. A caption can help a reviewer notice a change in tone or a background layer; it is not a measurement of a person’s private emotions or a proof of the physical source of an ambiguous sound.
The Captioner edition and its input contract
This checkpoint is single-turn. It accepts exactly one audio input per inference, no text prompt, and returns text only. It contains the thinker component for audio understanding; the published configuration explicitly disables audio output. Do not transfer the conversational, image, video or speech-generation features of other Qwen3-Omni editions to Captioner because their names share the same family.
The absence of a text prompt matters in practice. Prepare the audio segment that you want described and submit that segment as the entire task. Do not append an instruction asking for a custom summary, translation, speaker answer or output schema. If a workflow needs those transformations, they belong in a separate stage working on the returned description, rather than being assumed controls of this captioning checkpoint.
Preparing useful audio
Qwen recommends limiting a clip to no more than 30 seconds for fine-grained description. This is a quality recommendation: excessively long audio can reduce perception of detail. It is not presented as a universal hard rejection threshold. For a longer recording, select a meaningful short segment around the event or scene being reviewed. Keep its source identifier and start time outside the model request so the returned caption can later be traced to the original recording.
Preserve the sound layers relevant to the question. A voice-focused excerpt should still include the background when that background affects the intended description; a music or sound-effects asset should retain its important transition rather than be cut at an arbitrary boundary. Processing clips separately helps retain local detail but does not establish automatic continuity or understanding of the whole recording across requests.
What the textual result is useful for
The maker’s cookbook shows captions that describe speech content and delivery, then the surrounding scene; other examples describe music, effects, changes in intensity and the sequence of vocal events. An audio catalogue can store that returned text alongside the asset name and time range. A media reviewer can use it as a draft sound log. An editor can compare the caption with the clip to identify whether a description captures the intended layers. These are application examples, not evidence that the model has listened to any particular user asset in advance.
The output is a descriptive caption, not a promised word-for-word transcript, diarization table, timestamped subtitle file or generated soundtrack. Where exact spoken wording or timing is required, check the caption against the audio and use an appropriate dedicated workflow for the required artifact. A richly written description may include inferred scene or genre information, so preserve uncertainty when the sound itself does not identify the source unambiguously.
Generation examples and limits
The maker’s standalone example sets a 32,768-token model context and a 16,384-token generation allowance, using temperature 0.6, top_p 0.95 and top_k 20. Its cookbook uses an 8,192-token generation allowance with the same sampling values. These are example configurations, not separately declared maximum caption lengths. The underlying text configuration lists 65,536 positions, which should not be converted into a duration guarantee for audio.
Generic utility code or configuration fields shared with the Omni family do not override Captioner’s explicit one-audio, no-text, text-only contract. The useful result is a detailed description of a short audible scene; check speech quotations, speaker counts and inferred sound sources before using it as an authoritative record.
", "aboutText": "Overview: detailed descriptions of sound\n\nQwen3-Omni-30B-A3B-Captioner is a specialized audio-description model fine-tuned from Qwen3-Omni-30B-A3B-Instruct. It turns an audio clip into a detailed textual account of what is heard. Qwen designed it for varied material, including speech, environmental sound, music, cinematic effects and mixtures of several sound sources. This makes it useful for preparing a searchable description of an audio asset or reviewing the sound layers in a short media segment.\n\nSpeech and non-speech understanding\n\nThe maker describes analysis of multiple speakers, multilingual expressions, vocal emotion and the intentions suggested by delivery. Its examples go beyond a transcript: they describe how voices sound, the language of speech and the surrounding acoustic scene. For non-speech audio, the model describes environmental atmosphere, real-world sounds, musical layers and dynamic details in film or media.\n\nDescriptions of mood, speaker intention or cultural context are interpretations of the audible material. Treat them separately from directly checkable speech and sound events. A caption can help a reviewer notice a change in tone or a background layer; it is not a measurement of a person’s private emotions or a proof of the physical source of an ambiguous sound.\n\nThe Captioner edition and its input contract\n\nThis checkpoint is single-turn. It accepts exactly one audio input per inference, no text prompt, and returns text only. It contains the thinker component for audio understanding; the published configuration explicitly disables audio output. Do not transfer the conversational, image, video or speech-generation features of other Qwen3-Omni editions to Captioner because their names share the same family.\n\nThe absence of a text prompt matters in practice. Prepare the audio segment that you want described and submit that segment as the entire task. Do not append an instruction asking for a custom summary, translation, speaker answer or output schema. If a workflow needs those transformations, they belong in a separate stage working on the returned description, rather than being assumed controls of this captioning checkpoint.\n\nPreparing useful audio\n\nQwen recommends limiting a clip to no more than 30 seconds for fine-grained description. This is a quality recommendation: excessively long audio can reduce perception of detail. It is not presented as a universal hard rejection threshold. For a longer recording, select a meaningful short segment around the event or scene being reviewed. Keep its source identifier and start time outside the model request so the returned caption can later be traced to the original recording.\n\nPreserve the sound layers relevant to the question. A voice-focused excerpt should still include the background when that background affects the intended description; a music or sound-effects asset should retain its important transition rather than be cut at an arbitrary boundary. Processing clips separately helps retain local detail but does not establish automatic continuity or understanding of the whole recording across requests.\n\nWhat the textual result is useful for\n\nThe maker’s cookbook shows captions that describe speech content and delivery, then the surrounding scene; other examples describe music, effects, changes in intensity and the sequence of vocal events. An audio catalogue can store that returned text alongside the asset name and time range. A media reviewer can use it as a draft sound log. An editor can compare the caption with the clip to identify whether a description captures the intended layers. These are application examples, not evidence that the model has listened to any particular user asset in advance.\n\nThe output is a descriptive caption, not a promised word-for-word transcript, diarization table, timestamped subtitle file or generated soundtrack. Where exact spoken wording or timing is required, check the caption against the audio and use an appropriate dedicated workflow for the required artifact. A richly written description may include inferred scene or genre information, so preserve uncertainty when the sound itself does not identify the source unambiguously.\n\nGeneration examples and limits\n\nThe maker’s standalone example sets a 32,768-token model context and a 16,384-token generation allowance, using temperature 0.6, top_p 0.95 and top_k 20. Its cookbook uses an 8,192-token generation allowance with the same sampling values. These are example configurations, not separately declared maximum caption lengths. The underlying text configuration lists 65,536 positions, which should not be converted into a duration guarantee for audio.\n\nGeneric utility code or configuration fields shared with the Omni family do not override Captioner’s explicit one-audio, no-text, text-only contract. The useful result is a detailed description of a short audible scene; check speech quotations, speaker counts and inferred sound sources before using it as an authoritative record.", "editorialOverlay": { "copy_fields": { "practical_html": "The model description requires audio input, but this selected route declares text input. A ready-to-run generation example is not provided for this mismatch.
Technical example in English
Describe one short audio asset\n\nManufacturer workflow only: select one short audio clip whose sound layers need a description. Submit exactly that audio input with no text prompt through the documented manufacturer workflow. Keep the asset identifier outside the request. The selected Ailoot text-only route does not provide this audio-input workflow.A detailed text caption covering audible speech, background sounds, music and event order when present, with uncertain sound sources or intentions kept distinct from direct audible observations.Compare the caption with the same clip for sound sequence and sources; reconcile the Ailoot text-input mismatch before use. Do not send this preparation note as a text prompt.Qwen/Qwen3-Omni-30B-A3B-CaptionerQwen3-Omni-30B-A3B-Captioner describes speech, music, environmental sound and cinematic audio as text. This specialized single-turn edition accepts one audio input without a text prompt; Qwen recommends clips of no more than 30 seconds to retain fine detail.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Overview: detailed descriptions of sound
Qwen3-Omni-30B-A3B-Captioner is a specialized audio-description model fine-tuned from Qwen3-Omni-30B-A3B-Instruct. It turns an audio clip into a detailed textual account of what is heard. Qwen designed it for varied material, including speech, environmental sound, music, cinematic effects and mixtures of several sound sources. This makes it useful for preparing a searchable description of an audio asset or reviewing the sound layers in a short media segment.
Speech and non-speech understanding
The maker describes analysis of multiple speakers, multilingual expressions, vocal emotion and the intentions suggested by delivery. Its examples go beyond a transcript: they describe how voices sound, the language of speech and the surrounding acoustic scene. For non-speech audio, the model describes environmental atmosphere, real-world sounds, musical layers and dynamic details in film or media.
Descriptions of mood, speaker intention or cultural context are interpretations of the audible material. Treat them separately from directly checkable speech and sound events. A caption can help a reviewer notice a change in tone or a background layer; it is not a measurement of a person’s private emotions or a proof of the physical source of an ambiguous sound.
The Captioner edition and its input contract
This checkpoint is single-turn. It accepts exactly one audio input per inference, no text prompt, and returns text only. It contains the thinker component for audio understanding; the published configuration explicitly disables audio output. Do not transfer the conversational, image, video or speech-generation features of other Qwen3-Omni editions to Captioner because their names share the same family.
The absence of a text prompt matters in practice. Prepare the audio segment that you want described and submit that segment as the entire task. Do not append an instruction asking for a custom summary, translation, speaker answer or output schema. If a workflow needs those transformations, they belong in a separate stage working on the returned description, rather than being assumed controls of this captioning checkpoint.
Preparing useful audio
Qwen recommends limiting a clip to no more than 30 seconds for fine-grained description. This is a quality recommendation: excessively long audio can reduce perception of detail. It is not presented as a universal hard rejection threshold. For a longer recording, select a meaningful short segment around the event or scene being reviewed. Keep its source identifier and start time outside the model request so the returned caption can later be traced to the original recording.
Preserve the sound layers relevant to the question. A voice-focused excerpt should still include the background when that background affects the intended description; a music or sound-effects asset should retain its important transition rather than be cut at an arbitrary boundary. Processing clips separately helps retain local detail but does not establish automatic continuity or understanding of the whole recording across requests.
What the textual result is useful for
The maker’s cookbook shows captions that describe speech content and delivery, then the surrounding scene; other examples describe music, effects, changes in intensity and the sequence of vocal events. An audio catalogue can store that returned text alongside the asset name and time range. A media reviewer can use it as a draft sound log. An editor can compare the caption with the clip to identify whether a description captures the intended layers. These are application examples, not evidence that the model has listened to any particular user asset in advance.
The output is a descriptive caption, not a promised word-for-word transcript, diarization table, timestamped subtitle file or generated soundtrack. Where exact spoken wording or timing is required, check the caption against the audio and use an appropriate dedicated workflow for the required artifact. A richly written description may include inferred scene or genre information, so preserve uncertainty when the sound itself does not identify the source unambiguously.
Generation examples and limits
The maker’s standalone example sets a 32,768-token model context and a 16,384-token generation allowance, using temperature 0.6, top_p 0.95 and top_k 20. Its cookbook uses an 8,192-token generation allowance with the same sampling values. These are example configurations, not separately declared maximum caption lengths. The underlying text configuration lists 65,536 positions, which should not be converted into a duration guarantee for audio.
Generic utility code or configuration fields shared with the Omni family do not override Captioner’s explicit one-audio, no-text, text-only contract. The useful result is a detailed description of a short audible scene; check speech quotations, speaker counts and inferred sound sources before using it as an authoritative record.
POST /v1/chat/completionstexttext| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits Qwen/Qwen3-Omni-30B-A3B-Captioner | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits Minimum items: 1 | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits Examples: system, user, assistant, tool, function Conditions: These roles are explicitly represented by Source compatibility types; the exact model role set remains model-specific. Model-specific | Default— |
Fieldmessages[].content | Typestring|content_part[]|null | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_calls | Typetool_call[] | RequirementOptional | Values & limits | Default— |
Fieldmessages[].tool_call_id | Typestring | RequirementOptional | Values & limits Applies when: role=tool; use actual returned ID | Default— |
Fieldmessages[].name | Typestring | RequirementOptional | Values & limits | Default— |
Fieldmessages[].reasoning_content | Typestring | RequirementOptional | Values & limits Applies when: Round-trip actual returned reasoning where this model uses it | Default— |
Fieldmessages[].reasoning_details | Typeobject|array | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmax_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldmax_completion_tokens | Typeinteger | RequirementOptional | Values & limits Minimum: 1 Model-specific | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Fieldstream_options.include_usage | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtemperature | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtop_p | Typenumber | RequirementOptional | Values & limits Model-specific | Default— |
Fieldstop | Typestring|string[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools | Typefunction_tool[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldtools[].type | Typestring | RequirementOptional | Values & limits function Model-specific | Default— |
Fieldtools[].function.name | Typestring | RequirementRequired | Values & limits Applies when: function tool supplied | Default— |
Fieldtools[].function.description | Typestring | RequirementOptional | Values & limits | Default— |
Fieldtools[].function.parameters | TypeJSON_schema | RequirementOptional | Values & limits | Default— |
Fieldtool_choice | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldparallel_tool_calls | Typeboolean | RequirementOptional | Values & limits Model-specific | Default— |
Fieldresponse_format | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning_effort | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldreasoning | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldservice_tier | Typestring | RequirementOptional | Values & limits Model-specific | Default— |
Fieldinstructions | Typestring | RequirementOptional | Values & limits Compatibility field | Default— |
Fieldfunctions | Typefunction[] | RequirementOptional | Values & limits Model-specific | Default— |
Fieldfunction_call | Typestring|object | RequirementOptional | Values & limits Model-specific | Default— |
Fieldprompt_cache_options | Typeobject | RequirementOptional | Values & limits Model-specific | Default— |
Fieldmodalities | Typestring[] | RequirementOptional | Values & limits text | Default— |
Fieldprovider | Typeobject | RequirementOptional | Values & limits Conditions: provider.options is rejected on this Ultra text route | Default— |
Fieldcache_control.ttl | Typestring | RequirementOptional | Values & limits Conditions: 1h is rejected in actual prompt-cache locations | Default— |
Text · UTF8 text
messages[].content
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages |
Parametermaterial_types | Contracttext |
Parametermessages | Contractmessage array with role/content shape |
Parameterfile_material | Contractactual UTF-8 document inserted as literal text; no file ID inferred |
Parameteroptional_controls | Contractstream, temperature, top_p, stop, max_tokens, max_completion_tokens, reasoning, response_format, tools, tool_choice |
Parametercontrol_scope | Contractmodel-specific; user function tools differ from hosted tools |
Parameteroutput_modalities | Contracttext |
Parameterhosted_operations | Contractfalse |
Parametermultimodal_input_binding | Contractnull |
Lifecycle: synchronous_or_streamed
Send exact model ID with messages.
Context length is not an output cap.
A text file may supply literal text; image/audio/file-object inputs need exact binding.
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0008 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
The model description requires audio input, but this selected route declares text input. A ready-to-run generation example is not provided for this mismatch.
manufacturer: audio → text
route: text → text
json or SSE
Keep every complete returned response, file and content part.
Keep every choice and its content/tool calls/reasoning. In SSE assemble deltas by choice/tool index, retain the final finish reason and usage.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldchoices[].index | Typeinteger |
Fieldchoices[].message.content | Typestring|null |
Fieldchoices[].message.tool_calls | Typetool_call[] |
Fieldchoices[].message.reasoning_content | Typestring |
Fieldchoices[].message.reasoning_details | Typeobject|array |
Fieldchoices[].finish_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra Qwen/Qwen3-Omni-30B-A3B-Captioner
Use the method paths shown above.
Qwen/Qwen3-Omni-30B-A3B-CaptionerQwen3-Omni-30B-A3B-Captioner describes speech, music, environmental sound and cinematic audio as text. This specialized single-turn edition accepts one audio input without a text prompt; Qwen recommends clips of no more than 30 seconds to retain fine detail.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Overview: detailed descriptions of sound
Qwen3-Omni-30B-A3B-Captioner is a specialized audio-description model fine-tuned from Qwen3-Omni-30B-A3B-Instruct. It turns an audio clip into a detailed textual account of what is heard. Qwen designed it for varied material, including speech, environmental sound, music, cinematic effects and mixtures of several sound sources. This makes it useful for preparing a searchable description of an audio asset or reviewing the sound layers in a short media segment.
Speech and non-speech understanding
The maker describes analysis of multiple speakers, multilingual expressions, vocal emotion and the intentions suggested by delivery. Its examples go beyond a transcript: they describe how voices sound, the language of speech and the surrounding acoustic scene. For non-speech audio, the model describes environmental atmosphere, real-world sounds, musical layers and dynamic details in film or media.
Descriptions of mood, speaker intention or cultural context are interpretations of the audible material. Treat them separately from directly checkable speech and sound events. A caption can help a reviewer notice a change in tone or a background layer; it is not a measurement of a person’s private emotions or a proof of the physical source of an ambiguous sound.
The Captioner edition and its input contract
This checkpoint is single-turn. It accepts exactly one audio input per inference, no text prompt, and returns text only. It contains the thinker component for audio understanding; the published configuration explicitly disables audio output. Do not transfer the conversational, image, video or speech-generation features of other Qwen3-Omni editions to Captioner because their names share the same family.
The absence of a text prompt matters in practice. Prepare the audio segment that you want described and submit that segment as the entire task. Do not append an instruction asking for a custom summary, translation, speaker answer or output schema. If a workflow needs those transformations, they belong in a separate stage working on the returned description, rather than being assumed controls of this captioning checkpoint.
Preparing useful audio
Qwen recommends limiting a clip to no more than 30 seconds for fine-grained description. This is a quality recommendation: excessively long audio can reduce perception of detail. It is not presented as a universal hard rejection threshold. For a longer recording, select a meaningful short segment around the event or scene being reviewed. Keep its source identifier and start time outside the model request so the returned caption can later be traced to the original recording.
Preserve the sound layers relevant to the question. A voice-focused excerpt should still include the background when that background affects the intended description; a music or sound-effects asset should retain its important transition rather than be cut at an arbitrary boundary. Processing clips separately helps retain local detail but does not establish automatic continuity or understanding of the whole recording across requests.
What the textual result is useful for
The maker’s cookbook shows captions that describe speech content and delivery, then the surrounding scene; other examples describe music, effects, changes in intensity and the sequence of vocal events. An audio catalogue can store that returned text alongside the asset name and time range. A media reviewer can use it as a draft sound log. An editor can compare the caption with the clip to identify whether a description captures the intended layers. These are application examples, not evidence that the model has listened to any particular user asset in advance.
The output is a descriptive caption, not a promised word-for-word transcript, diarization table, timestamped subtitle file or generated soundtrack. Where exact spoken wording or timing is required, check the caption against the audio and use an appropriate dedicated workflow for the required artifact. A richly written description may include inferred scene or genre information, so preserve uncertainty when the sound itself does not identify the source unambiguously.
Generation examples and limits
The maker’s standalone example sets a 32,768-token model context and a 16,384-token generation allowance, using temperature 0.6, top_p 0.95 and top_k 20. Its cookbook uses an 8,192-token generation allowance with the same sampling values. These are example configurations, not separately declared maximum caption lengths. The underlying text configuration lists 65,536 positions, which should not be converted into a duration guarantee for audio.
Generic utility code or configuration fields shared with the Omni family do not override Captioner’s explicit one-audio, no-text, text-only contract. The useful result is a detailed description of a short audible scene; check speech quotations, speaker counts and inferred sound sources before using it as an authoritative record.
POST /v1/messagestextjson, sse| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits Qwen/Qwen3-Omni-30B-A3B-Captioner | Default— |
Fieldmessages | Typemessage[] | RequirementRequired | Values & limits | Default— |
Fieldmessages[].role | Typestring | RequirementRequired | Values & limits user, assistant | Default— |
Fieldmessages[].content | Typestring | RequirementRequired | Values & limits | Default— |
Fieldmax_tokens | Typeinteger | RequirementRequired | Values & limits | Default— |
Fieldstream | Typeboolean | RequirementOptional | Values & limits | Defaultfalse |
Text · UTF-8 text
messages[].content string
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, messages, max_tokens |
Parameteroptional_controls | Contractstream |
Parametermaterial_types | Contracttext |
Parametertext_file_material | ContractRead the actual UTF-8 document and put its text in the indicated input or message content string. |
Lifecycle: synchronous_or_streamed
Tariff references do not add request options. Supported parameters are listed for the selected method.
| Configuration | Rate schedule | Charge |
|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Charge Input · Input tokens $0.0002 / 1K tokens Output · Output tokens $0.0008 / 1K tokens Input · Cache read $0 / 1K tokens Input · Cache write $0 / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
The model description requires audio input, but this selected route declares text input. A ready-to-run generation example is not provided for this mismatch.
manufacturer: audio → text
route: text → json, sse
JSON or SSE
Keep every complete returned response, file and content part.
Keep all content blocks, thinking signatures and actual tool-use IDs. Assemble all SSE block deltas; a first text block is not the complete output.
Keep request/response IDs and headers. No generic text job retrieval URL is supplied.
| Field | Type |
|---|---|
Fieldid | Typestring |
Fieldmodel | Typestring |
Fieldcontent[] | Typetext|thinking|tool_use|other content block |
Fieldstop_reason | Typestring|null |
Fieldusage | Typeobject |
| Response | Condition | Next step |
|---|---|---|
Response400 · invalid_request_error | ConditionInvalid required fields, material representation or parameter value | Next stepCorrect the indicated fields before a new creation request. |
Ailoot Ultra Qwen/Qwen3-Omni-30B-A3B-Captioner
Use the method paths shown above.
The model description requires audio input, but this selected route declares text input. A ready-to-run generation example is not provided for this mismatch.
Technical example in English
Describe one short audio asset\n\nManufacturer workflow only: select one short audio clip whose sound layers need a description. Submit exactly that audio input with no text prompt through the documented manufacturer workflow. Keep the asset identifier outside the request. The selected Ailoot text-only route does not provide this audio-input workflow.A detailed text caption covering audible speech, background sounds, music and event order when present, with uncertain sound sources or intentions kept distinct from direct audible observations.Compare the caption with the same clip for sound sequence and sources; reconcile the Ailoot text-input mismatch before use. Do not send this preparation note as a text prompt.