Speech AI (Pronunciation + TTS + STT)
About
Production-ready examples for Brainiall Speech AI APIs — Pronunciation Assessment, STT, TTS. Python, JavaScript, curl, and MCP configs.
Details
- Author
- fasuizu-br
- GitHub stars
- 2
- Downloads
- 257
- Categories
- Other, AI
Jump to
- Pronunciation assessment with per‑phoneme scoring (39 ARPAbet)
- Text‑to‑speech with 12 American/British voices, 24 kHz WAV
- Speech‑to‑text with compact 17 MB model, word timestamps
- Pricing from $0.01 per STT request to $0.03 per 1K TTS chars
- Streamable HTTP transport for AI agents (no WebSocket)
- Available on Smithery (score 95/100), MCPize, Apify, MCP Registry
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Speech AI (Pronunciation + TTS + STT)Command (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
An API key is required; obtain it from the Brainiall portal (GitHub sign‑in, purchase credits, create key). Add the key as Ocp-Apim-Subscription-Key (or Authorization/api-key) in the header of every request. Configure the MCP server URL (https://apim-ai-apis.azure-api.net/mcp/pronunciation/mcp) with that header in Claude Desktop, Cursor, or Cline (example JSON config is provided). Use the server’s 10 tools, 8 resources, and 3 prompts to perform speech tasks.
assess_pronunciation
Assess English pronunciation quality from audio. Scores pronunciation at four levels: overall, sentence, word, and phoneme. Each score is 0-100. Phonemes are returned in both IPA and ARPAbet notation. Sub-300ms inference latency. Args: audio_base64: Base64-encoded audio data. Supports WAV, MP3, OGG, and WebM formats. text: The reference English text that the speaker was expected to read aloud. audio_format: Audio format hint — one of 'wav', 'mp3', 'ogg', 'webm'. Defaults to 'wav'. Returns: dict with keys: - overallScore (int 0-100): Overall pronunciation quality - sentenceScore (int 0-100): Sentence-level fluency and accuracy - words (list): Per-word scores, each containing: - word (str): The word - score (int 0-100): Word pronunciation score - phonemes (list): Per-phoneme scores with IPA/ARPAbet notation - decodedTranscript (str): What the model heard (ASR transcript) - transcript (str): Reference text - confidence (float 0-1): Scoring confidence - warnings (list[str]): Quality warnings if any - audioQuality (dict): Audio metrics (SNR, peak/RMS dB, etc.)
check_pronunciation_service
Check if the pronunciation assessment service is healthy and ready. Returns: dict with keys: - status (str): 'healthy' or error state - modelLoaded (bool): Whether the scoring model is loaded - version (str): API version
get_phoneme_inventory
Get the full phoneme inventory supported by the pronunciation scorer. Returns a list of all English phonemes the engine can assess, including ARPAbet symbol, IPA equivalent, example word, and phoneme category (vowel, consonant, diphthong). Returns: list of dicts, each with keys: - arpabet (str): ARPAbet symbol (e.g. 'AA', 'TH') - ipa (str): IPA notation - example (str): Example word containing the phoneme - category (str): vowel, consonant, or diphthong
transcribe_audio
Transcribe audio to text with word-level timestamps. Converts spoken English audio into text with optional word-level timestamps and per-word confidence scores. Args: audio_base64: Base64-encoded audio data (WAV, MP3, OGG, FLAC, WebM). audio_format: Audio format hint. Auto-detected from magic bytes if omitted. include_timestamps: Whether to include word-level timing (default: true). Returns: dict with keys: - text (str): Full decoded transcript - words (list): Per-word results with timestamps, each containing: - word (str): The transcribed word - start (float): Start time in seconds - end (float): End time in seconds - confidence (float 0-1): Word-level confidence - audioDurationMs (int): Audio duration in milliseconds - metadata (dict): Processing time, audio length, model version - audioQuality (dict): Audio metrics (SNR, peak/RMS dB, etc.)
check_stt_service
Check if the speech-to-text service is healthy and ready. Returns: dict with keys: - status (str): 'healthy' or error state - modelLoaded (bool): Whether the STT model is loaded - version (str): API version
synthesize_speech
Generate natural speech audio from English text. Produces high-quality speech with 12 English voices. Returns base64-encoded WAV audio (16-bit PCM, 24kHz mono) along with metadata. Available voices: - af_heart (default), af_bella, af_nicole, af_sarah, af_sky (American female) - am_adam, am_michael (American male) - bf_emma, bf_isabella (British female) - bm_george, bm_lewis, bm_daniel (British male) Args: text: English text to synthesize (1-5000 characters). voice: Voice ID. See list above. Defaults to 'af_heart'. speed: Speed multiplier from 0.5 to 2.0 (default: 1.0). Returns: dict with keys: - audio_base64 (str): Base64-encoded WAV audio (16-bit PCM, 24kHz) - duration_ms (str): Audio duration in milliseconds - voice (str): Voice ID used - text_length (str): Input text character count - processing_ms (str): Synthesis time in milliseconds
list_tts_voices
List all available text-to-speech voices with metadata. Returns: dict with keys: - voices (list): Available voices, each with id, name, gender, accent, grade - defaultVoice (str): Default voice ID
check_tts_service
Check if the text-to-speech service is healthy and ready. Returns: dict with keys: - status (str): 'healthy' or error state - modelLoaded (bool): Whether the TTS model is loaded - version (str): API version
transcribe_audio_pro
Transcribe audio with Whisper Large V3 Turbo — multilingual STT. Supports 99 languages with automatic language detection, word-level timestamps, per-word confidence scores, and optional speaker diarization (identifies who spoke each word). Best-in-class WER (~2%). Args: audio_base64: Base64-encoded audio (WAV, MP3, OGG, FLAC, WebM). language: Language code. Auto-detected if omitted. Supports 99 languages. diarize: Enable speaker diarization (default: false). When true, each word includes a speaker label (e.g. SPEAKER_00, SPEAKER_01). Returns: dict with keys: - text (str): Full decoded transcript - words (list): Per-word results with timestamps, each containing: - word (str), start (float), end (float), confidence (float 0-1) - speaker (str|null): Speaker label when diarize=true - speakers (dict|null): Speaker info with count and labels - audioDurationMs (int): Audio duration in milliseconds - metadata (dict): Processing time, language, languageProbability - audioQuality (dict): Audio metrics (SNR, peak/RMS dB, etc.)
check_whisper_service
Check if the Whisper STT Pro service is healthy and ready. Returns: dict with keys: - status (str): 'healthy' or error state - modelLoaded (bool): Whether the Whisper model is loaded - diarizeLoaded (bool): Whether the diarization pipeline is loaded - version (str): API version - modelName (str): Whisper model name (e.g. 'large-v3-turbo')
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"speech ai (pronunciation + tts + stt)": {
"speech-ai": {
"url": "https://pronunciation-mcp.thankfulfield-a7857897.eastus.azurecontainerapps.io/mcp",
"headers": {
"x-api-key": "<YOUR_API_KEY>"
}
}
}
}
}McpServers
{
"speech-ai": {
"url": "https://pronunciation-mcp.thankfulfield-a7857897.eastus.azurecontainerapps.io/mcp",
"headers": {
"x-api-key": "<YOUR_API_KEY>"
}
}
}Examples
| 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 |
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