io.github.fasuizu-br/speech-ai

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Au dernier audit MCP Hub, le serveur MCP « io.github.fasuizu-br/speech-ai » est classé « Non encore testé ». Référencé dans l'annuaire. Seul le pentest comportemental fait foi, et il n'a pas encore été exécuté sur ce serveur. Le verdict est réévalué en continu ; consultez la fiche pour l'état à jour.

Méthode : MCP Hub analyse la description de chaque outil (couche 1) et, pour le verdict fort, exécute le serveur dans une micro-VM isolée avec leurres et réseau bloqué (couche 2). On publie des noms et des compteurs, jamais le contenu brut renvoyé par un outil.

Les 10 outils

• assess_pronunciation≈ 486 tokens

  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.)

  Paramètres, retour & exemple (3)

  Paramètres

  • text · string · requis
    The reference English text that the speaker was expected to read aloud.
  • audio_base64 · string · requis
    Base64-encoded audio data. Supports WAV, MP3, OGG, and WebM formats.
  • audio_format · string · optionnel
    Audio format hint — one of 'wav', 'mp3', 'ogg', 'webm'.

  Exemple d'appel

  {"name": "assess_pronunciation", "arguments": {"text": "texte", "audio_base64": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• check_pronunciation_service≈ 140 tokens

  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

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "check_pronunciation_service", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• check_stt_service≈ 128 tokens

  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

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "check_stt_service", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• check_tts_service≈ 128 tokens

  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

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "check_tts_service", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• check_whisper_service≈ 170 tokens

  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')

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "check_whisper_service", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• get_phoneme_inventory≈ 198 tokens

  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

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "get_phoneme_inventory", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• list_tts_voices≈ 122 tokens

  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

  Paramètres, retour & exemple (0)

  Exemple d'appel

  {"name": "list_tts_voices", "arguments": {}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• synthesize_speech≈ 406 tokens

  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

  Paramètres, retour & exemple (3)

  Paramètres

  • text · string · requis
    English text to convert to speech. Max 5000 characters.
  • speed · number · optionnel
    Speech speed multiplier (0.5 = half speed, 2.0 = double).
  • voice · ? · optionnel
    Voice ID (e.g. 'af_heart', 'am_adam'). Uses default if omitted.

  Exemple d'appel

  {"name": "synthesize_speech", "arguments": {"text": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• transcribe_audio≈ 438 tokens

  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.)

  Paramètres, retour & exemple (3)

  Paramètres

  • audio_base64 · string · requis
    Base64-encoded audio data. Supports WAV, MP3, OGG, FLAC, and WebM formats.
  • audio_format · ? · optionnel
    Audio format hint — 'wav', 'mp3', 'ogg', 'flac', 'webm'. Auto-detected if omitted.
  • include_timestamps · boolean · optionnel
    If true, include word-level start/end times and confidence.

  Exemple d'appel

  {"name": "transcribe_audio", "arguments": {"audio_base64": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

• transcribe_audio_pro≈ 482 tokens

  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.)

  Paramètres, retour & exemple (3)

  Paramètres

  • diarize · boolean · optionnel
    Enable speaker diarization to identify who spoke each word.
  • language · ? · optionnel
    Language code (e.g. 'en', 'es', 'zh'). Auto-detected when omitted.
  • audio_base64 · string · requis
    Base64-encoded audio data. Supports WAV, MP3, OGG, FLAC, and WebM formats.

  Exemple d'appel

  {"name": "transcribe_audio_pro", "arguments": {"audio_base64": "texte"}}

  Exemple de retour

  {"content": [{"type": "text", "text": "…"}]}

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Source

https://apim-ai-apis.azure-api.net/mcp/pronunciation/mcp