Speech AI Pronunciation, STT & TTS vs Whisper MCP
A side-by-side look at the Speech AI Pronunciation, STT & TTS and Whisper MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Speech AI Pronunciation, STT & TTS for speech-to-text work over a hosted endpoint; choose Whisper MCP for speech-to-text as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Speech AI Pronunciation, STT & TTS when
- You need dedicated capabilities in the Speech-to-Text domain.
- You prefer a remote HTTP/SSE transport.
- You want tools like
assess_pronunciation, check_pronunciation_service, get_phoneme_inventory.
Choose Whisper MCP when
- You need dedicated capabilities in the Speech-to-Text domain.
- You prefer a local stdio subprocess transport.
- You want tools like
transcribe_audio, list_whisper_models, download_whisper_model.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Speech-to-Text | Speech-to-Text |
| Quality signal | 52/100 · Good | 61/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | Local subprocess (stdio) |
| Auth | Not declared | Not declared |
| Pricing | Not declared | Not declared |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 3 |
| Directory activity | 0 views 0 installs 0 upvotes | 1 views 0 installs 0 upvotes |
| Tools | 10 | 3 |
| Full listing | View Speech AI Pronunciation, STT & TTS | View Whisper MCP |
Tools & capabilities
Speech AI Pronunciation, STT & TTS tools 10
assess_pronunciationAssess 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_serviceCheck 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 versionget_phoneme_inventoryGet 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 diphthongtranscribe_audioTranscribe 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_serviceCheck 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 versionsynthesize_speechGenerate 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 millisecondslist_tts_voicesList 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 IDcheck_tts_serviceCheck 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 versiontranscribe_audio_proTranscribe 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_serviceCheck 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')
Whisper MCP tools 3
transcribe_audioTranscribe an audio file using Whisper. Supports various audio formats (wav, mp3, m4a, etc.). Returns the transcribed text.list_whisper_modelsList available Whisper models and their download status. Shows which models are downloaded locally.download_whisper_modelDownload a Whisper model for local transcription. Models are stored in ~/.whisper/
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Speech AI Pronunciation, STT & TTS
{
"mcpServers": {
"speech-ai-pronunciation-stt-tts": {
"url": "https://pronunciation-mcp.thankfulfield-a7857897.eastus.azurecontainerapps.io/mcp"
}
}
}
Whisper MCP
{
"mcpServers": {
"whisper-mcp": {
"command": "npx",
"args": [
"-y",
"whisper-mcp"
]
}
}
}
Frequently asked questions
Speech AI Pronunciation, STT & TTS is categorized under Speech-to-Text and uses a remote HTTP/SSE transport. Whisper MCP belongs to Speech-to-Text and uses a local stdio subprocess. Pick Speech AI Pronunciation, STT & TTS for speech-to-text work and Whisper MCP for speech-to-text.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Speech AI Pronunciation, STT & TTS has no declared pricing — check the project's own site. Whisper MCP has no declared pricing — check the project's own site. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add speech-ai-pronunciation-stt-tts and whisper-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Speech AI Pronunciation, STT & TTS has a quality signal of 52/100 (Good) with 0 views, 0 installs and 0 GitHub stars. Whisper MCP has 61/100 (Good) with 1 views, 0 installs and 3 GitHub stars.