Orpheus 3B 0.1 Fine-Tuned is Canopy Labs’ English text-to-speech model for everyday synthesis. It belongs to the Orpheus family built on a Llama 3B backbone and focuses on natural intonation, rhythm and expressive delivery. The input is the text to be spoken, with a selected speaker; the output is speech audio. The manufacturer also lists zero-shot voice cloning for this exact fine-tuned model. Its published fine-tuned synthesis example uses preset speakers, while reference-conditioned text–speech prompting is documented separately for the pretrained edition. This fine-tuned edition is distinct from the pretrained model and the separately released multilingual research models.
The manufacturer lists eight English preset voices: tara, leah, jess, leo, dan, mia, zac and zoe. Its prompt convention places the speaker name before the spoken text, separated by a colon. The reference speech-generation workflow can add that formatting when the voice is supplied separately. Select a speaker for the intended piece and keep that choice consistent across connected passages.
For a short explainer, write the final narration rather than an instruction asking the model to invent the explanation. A useful spoken line is: “Choose the folder you want to share, check who can open it, and save the changes.” For dialogue, prepare each speaker’s line explicitly and retain the punctuation that helps the delivery. An abbreviation, date or product name should be written in the form you want the listener to hear, particularly when its written shorthand would be ambiguous.
Orpheus documents the tags <laugh>, <chuckle>, <sigh>, <cough>, <sniffle>, <groan>, <yawn> and <gasp>. They give the text an explicit nonverbal event at the relevant point. A short conversational passage can place a chuckle before a light remark or a sigh before a reflective sentence. Use the cue where it belongs in the performance and keep the actual words of the line clear.
These are speech-generation cues, not a general instruction language for editing an existing recording. For a voice-over, begin with readable copy and add only the expressive events the scene needs. Listen to the complete line to judge whether the event, emphasis and pacing support the meaning. A narration task may call for a steady voice, while a character line may benefit from a specific nonverbal cue.
The manufacturer supplies both a non-streaming notebook and a streaming speech example. Streaming returns successive audio chunks as the passage is generated. In the published example, those chunks are written into a mono WAV file at 24,000 Hz with 16-bit samples. Those file settings describe the reference workflow; the useful result is a playable recording whose words, speaker and delivery can be checked against the script.
For a longer narration, prepare coherent passages with clear sentence boundaries and keep a copy of the final script beside the audio. Review the start and end of each passage, the pronunciation of important names and the transition between adjacent recordings. The manufacturer’s sampling guidance specifies a repetition penalty of at least 1.1 for stable generation in its reference implementation and notes that sampling choices can affect speaking speed. The sample’s configured model length is an inference setting, rather than an established maximum narration duration.
This profile concerns the fine-tuned English model with its documented preset speakers. The pretrained model has its own text-and-speech conditioning workflow; multilingual releases have language-specific speakers and tags. Selecting this English edition does not combine those separate profiles. A practical deliverable is a spoken script, an intentional preset voice and audio that has been reviewed for the wording and performance required by the piece.
", "aboutText": "Orpheus 3B 0.1 Fine-Tuned is Canopy Labs’ English text-to-speech model for everyday synthesis. It belongs to the Orpheus family built on a Llama 3B backbone and focuses on natural intonation, rhythm and expressive delivery. The input is the text to be spoken, with a selected speaker; the output is speech audio. The manufacturer also lists zero-shot voice cloning for this exact fine-tuned model. Its published fine-tuned synthesis example uses preset speakers, while reference-conditioned text–speech prompting is documented separately for the pretrained edition. This fine-tuned edition is distinct from the pretrained model and the separately released multilingual research models.\n\n### Choose a speaker and prepare the line\n\nThe manufacturer lists eight English preset voices: tara, leah, jess, leo, dan, mia, zac and zoe. Its prompt convention places the speaker name before the spoken text, separated by a colon. The reference speech-generation workflow can add that formatting when the voice is supplied separately. Select a speaker for the intended piece and keep that choice consistent across connected passages.\n\nFor a short explainer, write the final narration rather than an instruction asking the model to invent the explanation. A useful spoken line is: “Choose the folder you want to share, check who can open it, and save the changes.” For dialogue, prepare each speaker’s line explicitly and retain the punctuation that helps the delivery. An abbreviation, date or product name should be written in the form you want the listener to hear, particularly when its written shorthand would be ambiguous.\n\n### Expressive and nonverbal cues\n\nOrpheus documents the tagscanopylabs/orpheus-3b-0.1-ftCanopy Labs’ Orpheus 3B fine-tuned English speech model turns written lines into expressive audio. Eight preset voices and nonverbal tags support narration, dialogue and spoken application responses.
This description covers the model’s capabilities. Available in Ailoot inputs, outputs and options are listed for the selected method below.
Orpheus 3B 0.1 Fine-Tuned is Canopy Labs’ English text-to-speech model for everyday synthesis. It belongs to the Orpheus family built on a Llama 3B backbone and focuses on natural intonation, rhythm and expressive delivery. The input is the text to be spoken, with a selected speaker; the output is speech audio. The manufacturer also lists zero-shot voice cloning for this exact fine-tuned model. Its published fine-tuned synthesis example uses preset speakers, while reference-conditioned text–speech prompting is documented separately for the pretrained edition. This fine-tuned edition is distinct from the pretrained model and the separately released multilingual research models.
The manufacturer lists eight English preset voices: tara, leah, jess, leo, dan, mia, zac and zoe. Its prompt convention places the speaker name before the spoken text, separated by a colon. The reference speech-generation workflow can add that formatting when the voice is supplied separately. Select a speaker for the intended piece and keep that choice consistent across connected passages.
For a short explainer, write the final narration rather than an instruction asking the model to invent the explanation. A useful spoken line is: “Choose the folder you want to share, check who can open it, and save the changes.” For dialogue, prepare each speaker’s line explicitly and retain the punctuation that helps the delivery. An abbreviation, date or product name should be written in the form you want the listener to hear, particularly when its written shorthand would be ambiguous.
Orpheus documents the tags <laugh>, <chuckle>, <sigh>, <cough>, <sniffle>, <groan>, <yawn> and <gasp>. They give the text an explicit nonverbal event at the relevant point. A short conversational passage can place a chuckle before a light remark or a sigh before a reflective sentence. Use the cue where it belongs in the performance and keep the actual words of the line clear.
These are speech-generation cues, not a general instruction language for editing an existing recording. For a voice-over, begin with readable copy and add only the expressive events the scene needs. Listen to the complete line to judge whether the event, emphasis and pacing support the meaning. A narration task may call for a steady voice, while a character line may benefit from a specific nonverbal cue.
The manufacturer supplies both a non-streaming notebook and a streaming speech example. Streaming returns successive audio chunks as the passage is generated. In the published example, those chunks are written into a mono WAV file at 24,000 Hz with 16-bit samples. Those file settings describe the reference workflow; the useful result is a playable recording whose words, speaker and delivery can be checked against the script.
For a longer narration, prepare coherent passages with clear sentence boundaries and keep a copy of the final script beside the audio. Review the start and end of each passage, the pronunciation of important names and the transition between adjacent recordings. The manufacturer’s sampling guidance specifies a repetition penalty of at least 1.1 for stable generation in its reference implementation and notes that sampling choices can affect speaking speed. The sample’s configured model length is an inference setting, rather than an established maximum narration duration.
This profile concerns the fine-tuned English model with its documented preset speakers. The pretrained model has its own text-and-speech conditioning workflow; multilingual releases have language-specific speakers and tags. Selecting this English edition does not combine those separate profiles. A practical deliverable is a spoken script, an intentional preset voice and audio that has been reviewed for the wording and performance required by the piece.
POST /v1/audio/speechtextaudio| Field | Type | Requirement | Values & limits | Default |
|---|---|---|---|---|
Fieldmodel | Typestring | RequirementRequired | Values & limits canopylabs/orpheus-3b-0.1-ft | Default— |
Fieldinput | Typestring | RequirementRequired | Values & limits Minimum length: 1 Unit: Unicode characters | Default— |
Fieldvoice | Typestring | RequirementRequired | Values & limits tara, leah, jess, leo, dan, mia, zac | Default— |
Fieldresponse_format | Typestring | RequirementRequired | Values & limits mp3 | Default— |
text_to_speech · Required: input
| Parameter | Contract |
|---|---|
Parametercontent_type | Contractapplication/json |
Parameterrequired | Contractmodel, input, voice, response_format |
Parametervoice_ids | Contracttara, leah, jess, leo, dan, mia, zac |
Parameterresponse_format | Contractmp3 |
Parametermax_input_characters | Contractnull |
Parameterother_options | Contractfalse |
Parameterinput | Contractexact UTF-8 text; Unicode character count without normalization |
Parametermaterial_types | Contracttext |
Lifecycle: synchronous_result_then_owned_library
GET /v1/media/tasks/{task_id} · actual returned task_id or media library record
GET /v1/media/assets/{asset_id}/content
GET /v1/media/assets
GET /v1/media/assets/{asset_id}
Select exact voice from this model profile.
Input characters and audio seconds are different meters.
Tariff references do not add request options. Supported parameters are listed for the selected method.
Match each charge to the conditions shown. Use one rate for each set of alternative configurations.
| Configuration | Rate schedule | Billable quantity | Billing unit | Charge |
|---|---|---|---|---|
| ConfigurationBase token rates | Rate scheduleBase rate | Billable quantity— | Billing unit— | Charge Input · Input tokens — / 1K tokens Output · Output tokens — / 1K tokens Input · Cache read — / 1K tokens Input · Cache write — / 1K tokens Input · Cache write, 1 hour — / 1K tokens |
| ConfigurationUltra | Rate schedule— | Billable quantityEach Unicode code point counts as one character, including Han characters. | Billing unitper 1000 input character | Charge Input · input character $0.03 per 1000 input character |
Model-level reference schedules do not change method selection, quotes or charges. Active charges use the selected Ailoot method rates.
| Configuration | Billing conditions | Charge |
|---|---|---|
| ConfigurationCanopy Labs: Orpheus 3B · Default configuration | Billing conditionsDefault configuration | Charge Input · Input characters $0.000014 per input character |
Ailoot reserves an estimated amount before a request. After the result, it settles the final charge from measured usage. Streaming settlement uses the final usage record. How reservation and settlement work →
set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_VOICE_ID:?Choose an accepted voice ID from this exact model profile}"\nexport AILOOT_VOICE_ID\npython3 - <<'PY_REQUEST'\nimport json,os\nfrom pathlib import Path\np=json.loads('{"model": "canopylabs/orpheus-3b-0.1-ft", "input": "Good afternoon! This is a short voice take."}')\nvoice=os.environ["AILOOT_VOICE_ID"]\nallowed=json.loads('["tara", "leah", "jess", "leo", "dan", "mia", "zac"]')\nif voice not in allowed: raise SystemExit("Choose an accepted voice ID from this exact model profile")\np["voice"]=voice\np["response_format"]="mp3"\nPath("request.json").write_text(json.dumps(p,ensure_ascii=False))\nPY_REQUEST\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/audio/speech \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -H 'Content-Type: application/json' \\\n --data-binary @request.json -D response-headers.txt -o speech.mp3\nmp3
GET /v1/media/tasks/{task_id}GET /v1/media/assets/{asset_id}/contentGET /v1/media/assetsGET /v1/media/assets/{asset_id}set -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\ncurl --fail-with-body --silent --show-error https://api.ailoot.ai/v1/media/assets \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o media-assets.json\nset -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_MEDIA_TASK_ID:?Set the actual ID returned by the response or media-library metadata}"\nexport AILOOT_MEDIA_TASK_ID\nresource_id=$(python3 -c 'import os,urllib.parse;print(urllib.parse.quote(os.environ["AILOOT_MEDIA_TASK_ID"],safe=""))')\ncurl --fail-with-body --silent --show-error "https://api.ailoot.ai/v1/media/tasks/$resource_id" \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o media-task.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("media-task.json").read_text())))\nPY_RESULT\nset -eu\n: "${AILOOT_API_KEY:?Set your Ailoot API key}"\n: "${AILOOT_ASSET_ID:?Set the actual ID returned by the response or media-library metadata}"\nexport AILOOT_ASSET_ID\nresource_id=$(python3 -c 'import os,urllib.parse;print(urllib.parse.quote(os.environ["AILOOT_ASSET_ID"],safe=""))')\ncurl --fail-with-body --silent --show-error "https://api.ailoot.ai/v1/media/assets/$resource_id" \\\n -H "Authorization: Bearer $AILOOT_API_KEY" -o media-asset.json\npython3 - <<'PY_RESULT'\nimport json\nfrom pathlib import Path\nprint(json.dumps(json.loads(Path("media-asset.json").read_text())))\nPY_RESULT\nKeep every complete returned response, file and content part.
Keep the complete binary result and headers. If JSON/multiple outputs are returned, retain every data item and fetch each actual signed asset URL.
Retain original operation_id/X-Client-Request-ID for reconciliation without resubmission; no dedicated audio poll route.
| Field | Type |
|---|---|
Fieldbinary audio | Typebytes |
Fieldresponse headers | Typeheaders |
| 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. |
Responseserver_error | ConditionThe returned response says the operation or result requires a check | Next stepKeep the original returned operation/task ID, request headers and response; continue supported reads of that original operation. Do not resubmit the creation request. |
Ailoot Ultra canopylabs/orpheus-3b-0.1-ft
Use the method paths shown above.