| name | mm-voice-maker |
| description | Enables voice synthesis, voice cloning, voice design, and audio post-processing using MiniMax Voice API and FFmpeg. Use when converting text to speech, creating custom voices, or processing/merging audio. |
MiniMax Voice Maker
Professional text-to-speech skill with emotion detection, voice cloning, and audio processing capabilities powered by MiniMax Voice API and FFmpeg.
Capabilities
| Area | Features |
|---|
| TTS | Sync (HTTP/WebSocket), async (long text), streaming |
| Segment-based | Multi-voice, multi-emotion synthesis from segments.json, auto merge |
| Voice | Cloning (10s–5min), design (text prompt), management |
| Audio | Format conversion, merge, normalize, trim, remove silence (FFmpeg) |
File structure:
mmVoice_Maker/
├── SKILL.md # This overview
├── mmvoice.py # CLI tool (recommended for Agents)
├── check_environment.py # Environment verification
├── requirements.txt
├── scripts/ # Entry: scripts/__init__.py
│ ├── utils.py # Config, data classes
│ ├── sync_tts.py # HTTP/WebSocket TTS
│ ├── async_tts.py # Long text TTS
│ ├── segment_tts.py # Segment-based TTS (multi-voice, multi-emotion)
│ ├── voice_clone.py # Voice cloning
│ ├── voice_design.py # Voice design
│ ├── voice_management.py # List/delete voices
│ └── audio_processing.py # FFmpeg audio tools
└── reference/ # Load as needed
├── cli-guide.md # CLI usage guide
├── getting-started.md # Setup and quick test
├── tts-guide.md # Sync/async TTS workflows
├── voice-guide.md # Clone/design/manage
├── audio-guide.md # Audio processing
├── script-examples.md # Runnable code snippets
├── troubleshooting.md # Common issues
├── api_documentation.md # Complete API reference
└── voice_catalog.md # Voice selection guide
Main Workflow Guideline (Text to Speech)
6-step workflow:
[step1]. Verify environment
[step2-preparation]⚠️NOTE: Before processing the text, you must read voice-catalog.md for voice selection.
[step2]. Process text into script → <cwd>/audio/segments.json. Note: [Step2.4] is really important, you must check it twice before sending the script to the user.
[step2.5]. ⚠️ Generate preview for user confirmation (highly recommended for multi-voice content)
[step3]. Present plan to user for confirmation
[step4]. Validate segments.json
[step5]. Generate and merge audio → intermediate files in <cwd>/audio/tmp/, final output in <cwd>/audio/output.mp3
[step6]. ⚠️ CRITICAL: User confirms audio quality FIRST → THEN cleanup temp files (only after user is satisfied)
<cwd> is Claude's current working directory (not the skill directory). Audio files are saved relative to where Claude is running commands.
Step 1: Verify environment
python check_environment.py
Checks:
- Python 3.8+
- Required packages (requests, websockets)
- FFmpeg installation
- MINIMAX_VOICE_API_KEY environment variable
If API key is not set, ask user for keys and set it:
export MINIMAX_VOICE_API_KEY="your-api-key-here"
Step 2: Decision and Pre-processing
⚠️ MOST IMPORTANT PRINCIPLE: Gender Matching First
Before selecting voices, you MUST always match gender first. This is non-negotiable.
Golden Rule:
If a character is male → use male voice
If a character is female → use female voice
If a character is neutral/other → choose appropriate neutral voice
Why this matters:
- Violating gender matching (e.g., male character with female voice) breaks immersion
- Even if personality traits match, gender comes first
- This is especially critical for classic literature, historical content, and professional narration
Examples:
| Character | Wrong Voice | Correct Voice |
|---|
| 唐三藏 (male monk) | female-yujie ❌ | Chinese (Mandarin)_Gentleman ✅ |
| 林黛玉 (female) | male-qn-badao ❌ | female-shaonv ✅ |
| 曹操 (male warlord) | female-chengshu ❌ | Chinese (Mandarin)_Unrestrained_Young_Man ✅ |
Decision guide:
Evaluate based on:
- Does the user specify a model? → Use that model, or use the default one "speech-2.8"
- Is multi-voice needed? → Different voice_id per speaker/character
- For speech-2.8: emotion is auto-matched (leave
emotion empty)
- For older models: manually specify emotion tags
Use case scenarios:
| Scenario | Description | Segments | Voice Selection |
|---|
| Single Voice | User needs one voice for the entire content. Segment only by length (≤1,000,000 chars per segment). | Split by length only | One voice_id for all segments |
| Multi-Voice | Multiple characters/speakers, each with different voice. Segment by speaker/role changes. | Split by logical unit (speaker, dialogue, etc.) | Different voice_id per role |
| Podcast/Interview | Host and guest speakers with distinct voices. | Split by speaker | Voice per host/guest |
| Audiobook/Fiction | Narrator and character voices. | Split by narration vs. dialogue | Voice per narrator/character |
| Documentary | Mostly narration with occasional quotes. | Keep as one segment | Single narrator voice |
| Report/Announcement | Formal content with consistent tone. | Keep as one segment | Professional voice |
Processing Workflow (4 sub-steps):
Step 2.1: Text Segmentation and Role Analysis
First, segment your text into logical units and identify the role/character for each segment.
Key principle (Important!): Split by logical unit, NOT simply by sentence
When to split (Important!):
- Different speakers clearly marked
- Narrator vs. character dialogue (in fiction/audiobooks/interview etc.)
- In some scenarios (like audiobooks, multi-voice fiction etc.), where speaker's identity is important, split when narration and dialogue mix in the same sentence.
When NOT to split (Important!):
- Third-person narration like "John said..." or "The reporter noted..."
- Quoted speech in narration (in documentary/podcast/report etc.) should keep in narrator's voice
- Keep in narrator's voice unless specific characterization is needed
Decision depends on use case:
| Use case | Example | Split strategy |
|---|
| Single Voice | Long article, news piece, announcement | Split by length (≤1,000,000 chars), same voice for all |
| Podcast/Interview | "Host: Welcome to the show. Guest: Thank you for having me." | Split by speaker |
| Documentary narration | "The scientist explained, 'The results are promising.'" | Keep as one segment (narrator voice) |
| Audiobook/Fiction | "'Who's there?' she whispered." | Split: "'Who's there?'" should be in character voice, while "she whispered." should be in narrator's voice |
| Report | "According to the report, the economy is growing." | Keep as one segment |
Example1: Single Voice (speech-2.8)
For single-voice content (e.g., news, announcements, articles), segment only by length while maintaining the same voice:
[
{"text": "First part of the article (under 1,000,000 chars)...", "role": "narrator", "voice_id": "female-shaonv", "emotion": ""},
{"text": "Second part of the article (under 1,000,000 chars)...", "role": "narrator", "voice_id": "female-shaonv", "emotion": ""},
{"text": "Third part of the article (under 1,000,000 chars)...", "role": "narrator", "voice_id": "female-shaonv", "emotion": ""}
]
Example2: Audiobook with characters (speech-2.8)
In audiobooks (multi-voice fiction), split when narration and dialogue mix in the same sentence:
[
{"text": "The detective entered the room.", "role": "narrator", "voice_id": "", "emotion": ""},
{"text": "\"Who's there?\"", "role": "female_character", "voice_id": "", "emotion": ""},
{"text": "she whispered.", "role": "narrator", "voice_id": "", "emotion": ""},
{"text": "\"It's me,\"", "role": "male_character", "voice_id": "", "emotion": ""},
{"text": "he replied calmly.", "role": "narrator", "voice_id": "", "emotion": ""}
]
Example3: Documentary/podcast narration (speech-2.8)
Quoted speech in narration stays in narrator's voice (no need to split):
[
{
"text": "The scientist explained, \"The results show significant improvement in all test groups.\"",
"role": "narrator",
"voice_id": "",
"emotion": ""
},
{
"text": "According to the latest report, the economy has grown by 3% this quarter.",
"role": "narrator",
"voice_id": "",
"emotion": ""
}
]
**Note:** In the preliminary `segments.json`:
- Fill in the `text` field with segment content
- Fill in the `role` field to identify the character (narrator, male_character, female_character, host, guest, etc.)
- Leave `voice_id` empty (to be filled in Step 2.2)
- Leave `emotion` empty for speech-2.8 models
**Step 2.2: Voice Selection**
After segmenting and labeling roles, analyze all detected characters in your text. Consult [voice_catalog.md](reference/voice_catalog.md) **Section 1 "How to Choose a Voice"** to match voices to characters.
**⚠️ CRITICAL: Follow the two-step selection process below**
**Path A — Professional domains (Story/Narration, News/Announcements, Documentary):**
If the content belongs to one of these three professional domains, prioritize selecting from the recommended voices in **voice_catalog.md Section 2.1** (filter by scenario + gender). These voices are specifically optimized for their professional use cases.
**Path B — All other scenarios:**
Select from **voice_catalog.md Section 2.2**, following this strict priority hierarchy:
1. **First: Match Gender** (non-negotiable) — Male characters MUST use male voices, female characters MUST use female voices
2. **Second: Match Language** — The voice MUST match the content language (Chinese content → Chinese voice, Korean content → Korean voice, English content → English voice, etc.). Never assign a voice from the wrong language.
3. **Third: Match Age** — Determine the age group (Children / Youth / Adult / Elderly / Professional) and select from the corresponding subsection in Section 2.2
4. **Fourth: Match Personality & Role** — Choose the best fit based on personality traits, tone, and character role
**Voice Selection Decision Tree:**
Is this a professional domain (Story/News/Documentary)?
├── YES → Select from voice_catalog Section 2.1 (filter by scenario + gender)
└── NO → Select from voice_catalog Section 2.2:
Step 1: Match Gender
├── Male character → Male voices only
└── Female character → Female voices only
Step 2: Match Age Group
└── Children / Youth / Adult / Elderly / Professional
Step 3: Match Language
└── Filter to voices matching the content language
Step 4: Match Personality & Role
└── Choose best fit by tone, personality, character role
**Step 2.3: Emotions Segmentation** *(For non-2.8 series models only)*
For models other than speech-2.8 series, analyze emotions in your segments:
- For **long segments**, split further based on **emotional transitions**
- Add appropriate **emotion tags** to each segment
- Refer to Section 3 in [text-processing.md](reference/text-processing.md) for emotion tags and examples
- Skip this step for speech-2.8 models (emotion is auto-matched)
**Emotion Tags:**
- For speech-2.6 series (speech-2.6-hd and speech-2.6-turbo): happy, sad, angry, fearful, disgusted, surprised, calm, fluent, whisper
- For older models: happy, sad, angry, fearful, disgusted, surprised, calm (7 emotions)
**Step 2.4: Check and Post-processing**
Finally, review and optimize your script:
- Verify segment length limits (async TTS ≤1,000,000 characters)
- Clean up conversational text (remove speaker names if needed)
- Ensure consistency in voice and emotion tags
- **Critical check for multi-voice content**: For audiobooks, multi-voice fiction, or content where dialogue is presented from a first-person perspective, verify that narration and dialogue mixed in the same sentence are properly split.
**When splitting IS needed (first-person dialogue in fiction/audiobooks):**
Example: `"John asked, 'Where are you going?'"` should be split into:
- Segment 1: `"John asked, "` - uses narrator voice (describes who is speaking)
- Segment 2: `"Where are you going?"` - uses the character's voice (actual dialogue in first-person)
This ensures proper voice differentiation: descriptive narration uses the narrator's voice, while the character's spoken words use the character's designated voice.
**When splitting is NOT needed (third-person quotes in podcast/documentary/news):**
In podcasts, documentaries, or news reports, quoted speech is typically presented in third-person narrative style - the speaker's words are being reported, not performed. Keep these as one segment with the narrator's voice and remove the speaker's name at the beginning:
- `"Welcome to our show." → narrator voice, remove the speaker's name (like "The host said:") at the beginning
- `"According to experts, 'This technology represents a significant breakthrough.'" → keep as one segment (narrator voice)
- `"Scientists noted, 'The experimental results exceeded our expectations.'" → keep as one segment (narrator voice)
- **If the split is missing**: Go back to Step 2.1 and ensure dialogue portions are separated from narration with appropriate role labels.
**Create segments.json:**
After completing all 4 sub-steps, save the final `segments.json` to `<cwd>/audio/segments.json`.
### Step 2.5: Generate Preview for User Confirmation (Highly Recommended)
**For multi-voice content (audiobooks, dramas, etc.), always generate a preview first.**
This saves time and prevents waste when voice selections need adjustment.
**How to generate a preview:**
1. Create a smaller segments file with 10-20 representative segments (include all characters)
2. Generate the preview audio
3. Ask user to listen and confirm voice choices
**Preview segments.json example:**
```json
[
{"text": "Narration opening...", "role": "narrator", "voice_id": "...", "emotion": ""},
{"text": "Male character speaks...", "role": "male_character", "voice_id": "...", "emotion": ""},
{"text": "Female character speaks...", "role": "female_character", "voice_id": "...", "emotion": ""},
{"text": "More dialogue...", "role": "...", "voice_id": "...", "emotion": ""}
]
Preview command:
python mmvoice.py generate segments_preview.json -o preview.mp3
When user confirms preview:
- Use the same voice selections for the full segments.json
- No need to re-select voices
Step 3: Present plan to user for confirmation
Before proceeding to validation and generation, present the segmentation plan to the user and wait for confirmation:
Present to the user:
- Roles identified: List all characters/speakers in the text
- Voice assignments: Show which voice_id is assigned to each role (include voice characteristics from voice_catalog.md)
- Model being used: Explain why this model was selected
- Language: Confirm the primary language of the content
- Emotion approach: Auto-matched (speech-2.8) or manual tags (older models)
Example confirmation message:
I've analyzed the text and created a segmentation plan:
**Roles and Voices:**
- Narrator: male-qn-jingying (deep, authoritative, suitable for storytelling)
- Protagonist: female-shaonv (bright, energetic, youthful)
- Antagonist: male-qn-qingse (cool, menacing)
**Model:** speech-2.8-hd (recommended - automatic emotion matching)
**Language:** Chinese
**Segments:** 8 segments total
Please review and confirm:
1. ⚠️ **Gender Verification**: Do the voice genders match the character genders?
- [Narrator: Male ✓] [Protagonist: Female ✓] [Antagonist: Male ✓]
2. ⚠️ **Language Verification**: Do the voice languages match the content language?
- [All voices: Chinese ✓]
3. Are the voice assignments appropriate for each character (age, personality)?
4. Should any segments be combined or split differently?
5. Any other changes you'd like to make?
**After generation:**
- I'll generate a preview first for you to review
- Only after you confirm the audio quality will I clean up temporary files
- If not satisfied, I'll re-generate and we iterate until you're happy
Reply "confirm" to proceed, or let me know what to adjust.
Wait for user response:
- If user confirms → Proceed to Step 4 (validate)
- If user suggests changes → Update
segments.json and present the plan again for confirmation
Step 4: Validate segments.json (model, emotion, voice_id validation)
Before generating audio, validate the segments file:
python mmvoice.py validate <cwd>/audio/segments.json
python mmvoice.py validate <cwd>/audio/segments.json --model speech-2.6-hd
python mmvoice.py validate <cwd>/audio/segments.json --validate-voices
python mmvoice.py validate <cwd>/audio/segments.json --model speech-2.6-hd --validate-voices
python mmvoice.py validate <cwd>/audio/segments.json --model speech-2.6-hd --validate-voices --verbose
Emotion Validation checks:
| Model | Emotion Validation |
|---|
| speech-2.8-hd/turbo | Emotion can be empty (auto emotion matching) |
| speech-2.6-hd/turbo | All 9 emotions supported |
| Older models | happy, sad, angry, fearful, disgusted, surprised, calm (7 emotions) |
Voice ID validation:
With --validate-voices:
- Calls API once to get all available voices
- Validates each voice_id against the list
- Shows errors for invalid voice_ids (blocks validation)
Step 5: Generate and merge audio
Generate audio for all segments and merge into final output.
File placement (default behavior if user doesn't specify):
<cwd>/ # Claude's current working directory
└── audio/ # Created automatically
├── tmp/ # Intermediate segment files
│ ├── segment_0000.mp3
│ ├── segment_0001.mp3
│ └── ...
└── <custom_audio_name>.mp3 # Final merged audio, name can be customized
Where <cwd> is Claude's current working directory (where commands are executed).
- If
-o is not specified, output goes to <cwd>/audio/output.mp3
- Intermediate files go to
<cwd>/audio/tmp/
- After user confirms the final audio, ask whether to delete
<cwd>/audio/tmp/
Basic usage:
python mmvoice.py generate <cwd>/audio/segments.json
python mmvoice.py generate <cwd>/audio/segments.json -o <cwd>/audio/<custom_audio_name>.mp3
python mmvoice.py generate <cwd>/audio/segments.json --model speech-2.6-hd
Skip existing segments (for rate limit retries):
python mmvoice.py generate <cwd>/audio/segments.json --skip-existing
Error handling:
- If a segment fails, the script reports which segment and why
- Use
--continue-on-error to generate remaining segments despite failures
- Use
--skip-existing to skip already successfully generated segments (recommended for retries after rate limit)
- The script automatically uses fallback merging if FFmpeg filter_complex fails
Step 6: Confirm and cleanup
⚠️ CRITICAL: Never delete temp files until user confirms!
After generation completes, you MUST follow this exact sequence:
Step 6.1: Report generation result to user
✓ Audio saved to: <output_path>
Generated: X/Y segments
Intermediate files in: <cwd>/audio/tmp/
Step 6.2: Ask user to confirm audio quality
Ask the user to listen to the audio and confirm:
- Is the audio quality satisfactory?
- Are all voices appropriate?
- Any adjustments needed?
Step 6.3: Wait for user response
Step 6.4: Only after user confirms, offer cleanup
After confirming audio quality, temporary files can be deleted with:
rm -rf <cwd>/audio/tmp/
NEVER execute rm -rf on temp files without explicit user confirmation!
If user is NOT satisfied:
- Do NOT delete temp files
- Discuss what needs to be adjusted
- Re-generate affected segments if needed
- Ask for confirmation again
Other Usage
Use the following when the task involves voice creation, single-voice TTS (sync/async), or audio processing instead of the main segment-based workflow. Each subsection gives CLI commands, script paths, and the reference doc to open for details.
Voice creation (clone / design / list)
Text-to-speech (sync / async)
Audio processing (merge / convert / normalize)
Segment-based TTS (main workflow)
- CLI:
validate and generate as in Steps 4–5 above.
- Script:
scripts/segment_tts.py.
- Documentation: reference/cli-guide.md, reference/api_documentation.md.
Reference documents (on-demand)
Open these when you need concrete usage, parameters, or troubleshooting. Paths are relative to the skill root.
| Document | Content for the Agent |
|---|
| reference/cli-guide.md | All CLI commands (validate, generate, tts, clone, design, list-voices, merge, convert, check-env) with options and examples. Use for correct CLI invocation. |
| reference/getting-started.md | Environment setup (venv, pip install, FFmpeg), MINIMAX_VOICE_API_KEY, basic synthesis test. Use for first-time setup or “env not working”. |
| reference/tts-guide.md | Sync TTS (short text), async TTS (long text), streaming TTS, multi-segment production. Use for sync/async/streaming logic and parameters. |
| reference/voice-guide.md | Voice cloning (quick, high-quality with prompt audio, step-by-step), voice design, voice management. Use for custom voice creation flows. |
| reference/audio-guide.md | Format conversion, merging (including crossfade and fallback), normalization, trimming, optimization. Use for merge/convert/normalize behavior and options. |
| reference/script-examples.md | Copy-paste runnable examples for sync TTS, async TTS, segment-based TTS, audio processing, voice clone/design/management. Use for quick Python snippets. |
| reference/troubleshooting.md | Environment (API key, FFmpeg), API errors, segment-based TTS, audio, voice. Use when an error message or unexpected behavior appears. |
| reference/api_documentation.md | Full API reference: config, sync/async TTS, emotion parameter, segment-based TTS, voice clone/design/management, audio processing, common parameters, error handling. Use for exact function signatures and parameter details. |
| reference/voice_catalog.md | System voices list (male/female/beta), selection guide, voice parameters, custom voices, voice IDs. Use to choose or look up voice_id. |
Important notes
Requirements
- Python: 3.8 or higher
- API Key:
MINIMAX_VOICE_API_KEY environment variable must be set
- FFmpeg: Required for audio processing (merge, convert, normalize)
- Install:
brew install ffmpeg (macOS) or sudo apt install ffmpeg (Ubuntu)
Limits and constraints
- Text length: Sync TTS ≤10,000 chars; async TTS ≤1,000,000 chars
- Voice cloning: Audio must be 10s–5min duration, ≤20MB, formats: mp3/wav/m4a
- Voice expiration: Custom voices (cloned/designed) expire after 7 days if not used with TTS
Special features
- Pause insertion: Use
<#x#> in text where x = pause duration in seconds (0.01–99.99)
- Example:
"Hello<#1.5#>world" creates 1.5s pause between words
- Supported emotions: happy, sad, angry, fearful, disgusted, surprised, calm, fluent, whisper
- speech-2.8: automatic matching; speech-2.6: all 9; older models: first 7
Troubleshooting
