Speech Ai Examples — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Speech Ai Examples (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Production AI APIs for speech, text, image, and LLM inference. Available as REST endpoints and MCP servers for AI agents.
Base URL: https://apim-ai-apis.azure-api.net Full API reference for LLMs: llms-full.txt | llms.txt
| Product | Endpoints | Latency | Notes |
|---|---|---|---|
| Pronunciation Assessment | /v1/pronunciation/assess/base64 | <500ms | 17MB ONNX, per-phoneme scoring (39 ARPAbet) |
| Text-to-Speech | /v1/tts/synthesize | <1s | 12 voices (American + British), 24kHz WAV |
| Speech-to-Text | /v1/stt/transcribe/base64 | <500ms | Compact 17MB model, English, word timestamps |
| Whisper Pro | /v1/whisper/transcribe/base64 | <3s | 99 languages, speaker diarization |
| NLP Suite | /v1/nlp/{toxicity,sentiment,entities,pii,language} | <50ms | CPU-only, ONNX, 5 endpoints |
| Image Processing | /v1/image/{remove-background,upscale,restore-face}/base64 | <3s | GPU (A10), BiRefNet + ESRGAN + GFPGAN |
| LLM Gateway | /v1/chat/completions | varies | 113+ models, OpenAI-compatible, streaming |
Include ONE of these headers in every request:
Ocp-Apim-Subscription-Key: YOUR_KEY
Authorization: Bearer YOUR_KEY
api-key: YOUR_KEYGet API keys at the portal (GitHub sign-in, purchase credits, create key).
from openai import OpenAI
client = OpenAI(
base_url="https://apim-ai-apis.azure-api.net/v1",
api_key="YOUR_KEY"
)
response = client.chat.completions.create(
model="claude-sonnet",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)import requests, base64
audio_b64 = base64.b64encode(open("audio.wav", "rb").read()).decode()
r = requests.post(
"https://apim-ai-apis.azure-api.net/v1/pronunciation/assess/base64",
headers={"Ocp-Apim-Subscription-Key": "YOUR_KEY"},
json={"audio": audio_b64, "text": "Hello world", "format": "wav"}
)
print(r.json()["overallScore"]) # 0-100import requests
headers = {"Ocp-Apim-Subscription-Key": "YOUR_KEY"}
base = "https://apim-ai-apis.azure-api.net/v1/nlp"
# Sentiment
r = requests.post(f"{base}/sentiment", headers=headers, json={"text": "I love this!"})
print(r.json()) # {"label": "positive", "score": 0.9987}
# PII detection with redaction
r = requests.post(f"{base}/pii", headers=headers, json={"text": "Email [email protected]", "redact": True})
print(r.json()["redacted_text"]) # "Email [EMAIL]"import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://apim-ai-apis.azure-api.net/v1",
apiKey: "YOUR_KEY"
});
const res = await client.chat.completions.create({
model: "claude-sonnet",
messages: [{ role: "user", content: "Hello!" }]
});
console.log(res.choices[0].message.content);curl -X POST https://apim-ai-apis.azure-api.net/v1/image/remove-background/base64 \
-H "Ocp-Apim-Subscription-Key: YOUR_KEY" \
-H "Content-Type: application/json" \
-d "{\"image\": \"$(base64 -i photo.jpg)\"}"| Model | Alias | Price ($/MTok in/out) |
|---|---|---|
| Claude Opus 4.6 | claude-opus | $5 / $25 |
| Claude Sonnet 4.6 | claude-sonnet | $3 / $15 |
| Claude Haiku 4.5 | claude-haiku | $1 / $5 |
| DeepSeek R1 | deepseek-r1 | $1.35 / $5.40 |
| DeepSeek V3 | deepseek-v3 | $0.27 / $1.10 |
| Llama 3.3 70B | llama-3.3-70b | $0.72 / $0.72 |
| Amazon Nova Pro | nova-pro | $0.80 / $3.20 |
| Amazon Nova Micro | nova-micro | $0.035 / $0.14 |
| Mistral Large 3 | mistral-large-3 | $2 / $6 |
| Qwen3 32B | qwen3-32b | $0.35 / $0.35 |
Full list: GET /v1/models (113+ models from 17 providers).
Supports: streaming SSE, tool calling, structured output (json_object/json_schema), extended thinking.
Works with: OpenAI SDK, LiteLLM, LangChain, Cline, Cursor, Aider, Continue, SillyTavern, Open WebUI.
3 MCP servers with 20 tools total. Streamable HTTP transport.
| Server | URL | Tools |
|---|---|---|
| Speech AI | https://apim-ai-apis.azure-api.net/mcp/pronunciation/mcp | 10 tools + 8 resources + 3 prompts |
| NLP Tools | https://apim-ai-apis.azure-api.net/mcp/nlp/mcp | 6 tools + 3 resources + 3 prompts |
| Image Tools | https://apim-ai-apis.azure-api.net/mcp/image/mcp | 4 tools + 3 resources + 2 prompts |
{
"mcpServers": {
"brainiall-speech": {
"url": "https://apim-ai-apis.azure-api.net/mcp/pronunciation/mcp",
"headers": { "Ocp-Apim-Subscription-Key": "YOUR_KEY" }
},
"brainiall-nlp": {
"url": "https://apim-ai-apis.azure-api.net/mcp/nlp/mcp",
"headers": { "Ocp-Apim-Subscription-Key": "YOUR_KEY" }
},
"brainiall-image": {
"url": "https://apim-ai-apis.azure-api.net/mcp/image/mcp",
"headers": { "Ocp-Apim-Subscription-Key": "YOUR_KEY" }
}
}
}Also available on: Smithery (score 95/100) | MCPize | Apify ($0.02/call) | MCP Registry
| File | Description |
|---|---|
python/basic_usage.py | Speech APIs — assess, transcribe, synthesize |
python/pronunciation_tutor.py | Interactive pronunciation tutor |
javascript/basic_usage.js | Node.js examples for speech APIs |
curl/examples.sh | curl commands for every endpoint |
mcp/claude-desktop-config.json | MCP config for Claude Desktop |
mcp/cursor-config.json | MCP config for Cursor IDE |
llms-full.txt | Complete API reference for LLM consumption |
| Product | Price | Unit |
|---|---|---|
| Pronunciation | $0.02 | per call |
| TTS | $0.01-0.03 | per 1K chars |
| STT (compact) | $0.01 | per request |
| Whisper Pro | $0.02 | per minute |
| NLP (any) | $0.001-0.002 | per call |
| Image (any) | $0.003-0.005 | per image |
| LLM Gateway | competitive pricing | per MTok |
Credit packages: $5, $10, $25, $50, $100. Portal | Azure Marketplace (search "Brainiall").
MIT — Brainiall
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.