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Use the multimodalart/higgs-audio-v3-tts Gradio Space via API. Provides Python, JavaScript, and cURL usage examples.
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Pointer skill for pk-speak / pi-pk-speak. Use when the user wants spoken replies, wake-word listening, session-manager control, deterministic short voice routes like `PK one` / `PK1` vs `PK two` / `PK2`, Telegram control, QR-based Android setup via `/pk-remote`, or the browser remote. Start from the bridge doc and then jump to README and source as needed.
How agents execute work for Kade. Use when starting substantive or multi-step work, discovering workspace and repository instructions, selecting skills or tools, assembling minimal context, delegating or handing off work, verifying results, or synchronizing state. Do not use for simple direct answers, Kade's profile or governance (use kade-hq), or repository-specific implementation truth (use repo-local instructions).
Canonical human-context and governance skill for working with Kade (pk). Selects relevant communication, attention, autonomy, session-state, decision, commitment, and memory rules without forcing planning behavior onto ordinary execution.
Survey any codebase as a senior advisor and produce prioritized, self-contained implementation plans for OTHER models/agents to execute. Strictly read-only on source code — never implements, fixes, or refactors anything itself. Use when asked to audit a codebase, find improvement opportunities (bugs, security, performance, test coverage, tech debt, migrations, DX), suggest features or where to take the project next (roadmap, product direction), or generate handoff plans for another agent to implement.
Use when a CLI agent should speak naturally with pk-speak, wrap a command with spoken lifecycle notices, start the phone gateway/tray, print Android setup QR codes, or explain the current realtime voice options. Keep normal replies concise and speak only useful status, questions, approvals, and completion summaries.
Use when the task should consult, capture, validate, evolve, or retire reusable workflow shortcuts through the local `llm-wiki-skills` MCP server or its CLI. This packet-owned wrapper explains the full tool lifecycle in depth and is installable into ~/.agents/skills, ~/.codex/skills, ~/.claude/skills, and ~/.pi/agent/skills.
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| name | multimodalart-higgs-audio-v3-tts |
| description | Use the multimodalart/higgs-audio-v3-tts Gradio Space via API. Provides Python, JavaScript, and cURL usage examples. |
This skill describes how to use the multimodalart/higgs-audio-v3-tts Gradio Space programmatically.
/transcribeParameters:
reference_audio [Audio]: filepath (required)Returns:
Reference transcript (auto-filled on upload, improves cloning) [Textbox]: strPython:
from gradio_client import Client, handle_file
client = Client("multimodalart/higgs-audio-v3-tts")
result = client.predict(
reference_audio=handle_file('https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav'),
api_name="/transcribe",
)
print(result)
JavaScript:
import { Client } from "@gradio/client";
const response_0 = await fetch("https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav");
const exampleAudio = await response_0.blob();
const client = await Client.connect("multimodalart/higgs-audio-v3-tts");
const result = await client.predict("/transcribe", {
reference_audio: exampleAudio,
});
console.log(result.data);
cURL:
FILE_PATH=$(curl -s -X POST https://multimodalart-higgs-audio-v3-tts.hf.space/upload -F 'files=@/path/to/your/file' | tr -d '[]" ')
curl -X POST https://multimodalart-higgs-audio-v3-tts.hf.space/call/v2/transcribe -s -H "Content-Type: application/json" \
-d '{"reference_audio": {"path": "'$FILE_PATH'", "meta": {"_type": "gradio.FileData"}}}' \
| awk -F'"' '{ print $4}' \
| read EVENT_ID; curl -N https://multimodalart-higgs-audio-v3-tts.hf.space/call/transcribe/$EVENT_ID
/synthesizeParameters:
text [Textbox]: str (required)reference_audio [Audio]: filepath (required)reference_text [Textbox]: str (required)temperature [Slider]: float, default: 0.7top_p [Slider]: float, default: 0.95top_k [Slider]: float, default: 50max_new_tokens [Slider]: float, default: 2048seed [Number]: int, default: -1Returns:
Generated speech [Audio]: filepathPython:
from gradio_client import Client, handle_file
client = Client("multimodalart/higgs-audio-v3-tts")
result = client.predict(
text="Hello!!",
reference_audio=handle_file('https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav'),
reference_text="Hello!!",
temperature=0.7,
top_p=0.95,
top_k=50,
max_new_tokens=2048,
seed=-1,
api_name="/synthesize",
)
print(result)
JavaScript:
import { Client } from "@gradio/client";
const response_0 = await fetch("https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav");
const exampleAudio = await response_0.blob();
const client = await Client.connect("multimodalart/higgs-audio-v3-tts");
const result = await client.predict("/synthesize", {
text: "Hello!!",
reference_audio: exampleAudio,
reference_text: "Hello!!",
temperature: 0.7,
top_p: 0.95,
top_k: 50,
max_new_tokens: 2048,
seed: -1,
});
console.log(result.data);
cURL:
FILE_PATH=$(curl -s -X POST https://multimodalart-higgs-audio-v3-tts.hf.space/upload -F 'files=@/path/to/your/file' | tr -d '[]" ')
curl -X POST https://multimodalart-higgs-audio-v3-tts.hf.space/call/v2/synthesize -s -H "Content-Type: application/json" \
-d '{"text": "Hello!!", "reference_audio": {"path": "'$FILE_PATH'", "meta": {"_type": "gradio.FileData"}}, "reference_text": "Hello!!", "temperature": 0.7, "top_p": 0.95, "top_k": 50, "max_new_tokens": 2048, "seed": -1}' \
| awk -F'"' '{ print $4}' \
| read EVENT_ID; curl -N https://multimodalart-higgs-audio-v3-tts.hf.space/call/synthesize/$EVENT_ID