tts-audio-mcp

MCP server that analyzes TTS audio recordings — enabling Claude Code, OpenCode, and Qwen Code to debug voice call center audio the same way they debug code errors.

Feed it an audio file, get back a structured report with transcription, quality scores, pacing analysis, and mispronunciation detection.

What It Does

Audio File (.wav/.mp3/.m4a)
       |
       v
+-------------------------------+
|     tts-audio-mcp server      |
|                               |
|  transcribe   -> whisper.cpp  |
|  quality_score -> librosa     |
|  compare_tts  -> whisper+diff |
|  analyze_tts  -> all combined |
|                               |
|  Transport: stdio (MCP)       |
+-------------------------------+
       |
       v
  Structured report -> LLM reasons about fixes

Related MCP server: Talky Talky

Tools

transcribe

Transcribe audio to text with word-level timestamps.

Input: audio_path (string), language (string, default: "en")

Output: Full transcription text, per-word timestamps (start_ms, end_ms), segments, detected language, duration.

quality_score

Analyze speech quality — pitch variation, energy dynamics, silence ratio.

Input: audio_path (string)

Output:

• Pitch: mean/std/range Hz, monotone risk flag, interpretation

• Energy: RMS level, dynamic range dB, interpretation

• Silence ratio: percentage of audio that is silent

• Overall assessment with list of detected issues

compare_tts

Compare TTS output against expected text to find mispronunciations.

Input: audio_path (string), expected_text (string), language (string, default: "en")

Output: Word Error Rate (WER), substitutions, insertions, deletions with positions.

analyze_tts

Full composite analysis — runs all of the above and returns a single structured report.

Input: audio_path (string), expected_text (string, optional), language (string, default: "en")

Output: Combined report with transcription, quality scores, pacing analysis (WPM, rushed words, long pauses), and pronunciation diff.

Example Output

TTS Analysis Report
File: /tmp/tts-test-speech.wav
Duration: 4.15s | Words: 13 | Rate: 195 WPM

--- Transcription ---
Hello. Thank you for calling Acme Support. How can I help you today?

--- Quality Scores ---
Pitch: mean 258.5Hz, std 61.1Hz, range 264.3Hz
  Good variation — expressive
Energy: RMS 0.0912, dynamic range 80dB
  Wide dynamic range
Silence ratio: 37.3%
Overall: Minor issues detected (1)
  ! Very wide dynamic range — may clip

--- Pacing Analysis ---
Speaking rate: 195 WPM (natural: 120-180)
Minor pacing issues
Rushed words:
  "How" spoken in 40ms
  ! Speaking rate too fast: 195 WPM (natural: 120-180)
  ! 1 rushed word(s) detected (<80ms)

--- Pronunciation Check ---
Expected: Hello. Thank you for calling Acme Support. How can I help you today?
Got:      Hello. Thank you for calling Acme Support. How can I help you today?
WER: 0.0%
Perfect match — no mispronunciations detected

--- Issues Summary ---
1. Very wide dynamic range — may clip
2. Speaking rate too fast: 195 WPM (natural: 120-180)
3. 1 rushed word(s) detected (<80ms)

Prerequisites

• whisper.cpp with Metal acceleration: brew install whisper-cpp

• Whisper model: ggml-large-v3-turbo.bin (~1.5 GB) in models/

• Python 3.12 with librosa: .venv/bin/python3 with pip install librosa

• ffmpeg for audio format conversion: brew install ffmpeg

• Node.js 18+

Installation

git clone https://github.com/reactiongears/tts-audio-mcp.git
cd tts-audio-mcp

# Node dependencies
npm install

# Python venv for audio analysis
python3.12 -m venv .venv
.venv/bin/pip install librosa 'setuptools<82'

# Download whisper model
mkdir -p models
curl -L -o models/ggml-large-v3-turbo.bin \
  https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v3-turbo.bin

# Build
npm run build

Integration

Claude Code

Add to ~/.claude/.mcp.json:

{
  "mcpServers": {
    "tts-audio": {
      "command": "node",
      "args": ["/path/to/tts-audio-mcp/dist/index.js"]
    }
  }
}

OpenCode

Add to ~/.config/opencode/opencode.json under "mcp":

"tts-audio": {
  "type": "local",
  "command": ["node", "/path/to/tts-audio-mcp/dist/index.js"],
  "enabled": true
}

Qwen Code

Add to ~/.qwen/settings.json under "mcpServers":

"tts-audio": {
  "command": "node",
  "args": ["/path/to/tts-audio-mcp/dist/index.js"]
}

Environment Variables

Usage

Once the MCP server is configured in your coding assistant, the tools are available automatically. You talk to your assistant in natural language — it decides when to call the tools and interprets the results for you.

Quick Start

Generate a test audio file to try it out:

# macOS — use the built-in TTS engine
say -o /tmp/test-greeting.wav --data-format=LEI16@16000 \
  "Hello. Thank you for calling Acme Support. How can I help you today?"

Then in Claude Code, OpenCode, or Qwen Code:

> Analyze the audio at /tmp/test-greeting.wav

The assistant calls analyze_tts behind the scenes and returns a full report with transcription, quality scores, pacing, and issues.

Debugging TTS Problems

"It sounds robotic" — Check pitch variation and monotone risk:

> Run quality_score on /recordings/agent-greeting.wav — customers say it sounds robotic

The report shows pitch std < 20 Hz = monotone risk. You know to increase prosody variation in your TTS config.

"Words are getting swallowed" — Compare against expected script:

> Compare /recordings/transfer-prompt.wav against the expected text:
> "Thank you for your patience. I'll transfer you to a specialist now."

The tool transcribes the audio, diffs it against your script, and reports substitutions ("specialist" → "specialist's"), deletions, and WER. You know exactly which words the TTS is mangling.

"It's talking too fast / has weird pauses" — Check pacing:

> Analyze /recordings/ivr-menu.wav — callers are complaining it's too fast

The report flags speaking rate (natural range: 120-180 WPM), individual rushed words (< 80ms), and unnatural pauses (> 500ms). You know where to add SSML breaks or adjust rate.

"Something is off but I'm not sure what" — Full analysis:

> Run a full analysis on /recordings/hold-message.wav
> The expected text is: "Your call is important to us. Please hold and an agent will be with you shortly."

Returns everything: transcription, quality metrics, pacing analysis, pronunciation diff, and a prioritized issues summary.

Batch Debugging

You can analyze multiple recordings in a conversation:

> Compare these three recordings against their scripts and tell me which one has the most issues:
> 1. /recordings/greeting.wav — "Welcome to Acme Support"
> 2. /recordings/hold.wav — "Please hold while I look that up"
> 3. /recordings/goodbye.wav — "Thank you for calling. Have a great day!"

The assistant calls compare_tts for each file and summarizes which recordings need attention.

Using Individual Tools

You can also ask for specific analysis:

Supported Audio Formats

• .wav — processed directly (best performance)

• .mp3 — auto-converted to WAV via ffmpeg

• .m4a — auto-converted to WAV via ffmpeg

Interpreting Results

Quality Scores:

Pacing:

Pronunciation (WER):

Real-World Workflow

A typical voice call center debugging session:

1. Customer reports: "The bot sounds weird when it says the account number"

2. You pull the call recording: /recordings/call-1234-segment.wav

3. You know the expected script: "Your account number is 7 8 4 2 0 1 3"

4. In Claude Code:

   > Compare /recordings/call-1234-segment.wav against "Your account number is 7 8 4 2 0 1 3"
   > What's wrong and how should I fix the TTS config?

5. Claude calls compare_tts, sees the TTS is running digits together ("seven eight four" → "seventy-eight four"), and suggests adding SSML <say-as interpret-as="digits"> tags or inter-digit pauses to your TTS configuration

The LLM doesn't just report the numbers — it reasons about the root cause and suggests specific fixes to your TTS code or configuration.

License

MIT