mmVoiceMaker

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

AreaFeaturesTTSSync (HTTP/WebSocket), async (long text), streamingSegment-basedMulti-voice, multi-emotion synthesis from segments.json, auto mergeVoiceCloning (10s–5min), design (text prompt), managementAudioFormat 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:
• CharacterWrong VoiceCorrect 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:
• ScenarioDescriptionSegmentsVoice SelectionSingle VoiceUser needs one voice for the entire content. Segment only by length (≤1,000,000 chars per segment).Split by length onlyOne voice_id for all segmentsMulti-VoiceMultiple characters/speakers, each with different voice. Segment by speaker/role changes.Split by logical unit (speaker, dialogue, etc.)Different voice_id per rolePodcast/InterviewHost and guest speakers with distinct voices.Split by speakerVoice per host/guestAudiobook/FictionNarrator and character voices.Split by narration vs. dialogueVoice per narrator/characterDocumentaryMostly narration with occasional quotes.Keep as one segmentSingle narrator voiceReport/AnnouncementFormal content with consistent tone.Keep as one segmentProfessional 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 caseExampleSplit strategySingle VoiceLong article, news piece, announcementSplit by length (≤1,000,000 chars), same voice for allPodcast/Interview"Host: Welcome to the show. Guest: Thank you for having me."Split by speakerDocumentary 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 voiceReport"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: # Default: speech-2.8-hd (auto emotion matching) python mmvoice.py validate <cwd>/audio/segments.json # Specify model for context-specific validation python mmvoice.py validate <cwd>/audio/segments.json --model speech-2.6-hd # Validate voice_ids against available voices (slower, requires API call) python mmvoice.py validate <cwd>/audio/segments.json --validate-voices # Combined options (recommended) python mmvoice.py validate <cwd>/audio/segments.json --model speech-2.6-hd --validate-voices # Use `--verbose` to see segment details python mmvoice.py validate <cwd>/audio/segments.json --model speech-2.6-hd --validate-voices --verbose Emotion Validation checks: ModelEmotion Validationspeech-2.8-hd/turboEmotion can be empty (auto emotion matching)speech-2.6-hd/turboAll 9 emotions supportedOlder modelshappy, 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: # Default: speech-2.8-hd, output to <cwd>/audio/output.mp3 python mmvoice.py generate <cwd>/audio/segments.json # Specify output path python mmvoice.py generate <cwd>/audio/segments.json -o <cwd>/audio/<custom_audio_name>.mp3 # Specify model if needed python mmvoice.py generate <cwd>/audio/segments.json --model speech-2.6-hd Skip existing segments (for rate limit retries): # Only generate segments that don't exist yet - skips already-generated files 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)

Purpose: Create custom voices from audio (clone) or from a text description (design); list system and custom voices. CLI (entry point: mmvoice.py): python mmvoice.py clone AUDIO_FILE --voice-id VOICE_ID # Clone from 10s–5min audio python mmvoice.py design "DESCRIPTION" --voice-id ID # Design from text python mmvoice.py list-voices # List all voices Scripts: scripts/voice_clone.py (clone), scripts/voice_design.py (design), scripts/voice_management.py (list/manage). Documentation: reference/voice-guide.md — cloning (quick + high-quality + step-by-step), design workflow, management.

Text-to-speech (sync / async)

Purpose: Single-voice TTS: sync for short text (≤10k chars), async for long text (up to 1M chars); optional streaming. CLI: python mmvoice.py tts "TEXT" -o OUTPUT.mp3 [-v VOICE_ID] [--model MODEL] Scripts: scripts/sync_tts.py (HTTP/WebSocket sync), scripts/async_tts.py (async task + poll). Documentation: reference/tts-guide.md — sync TTS, async TTS, streaming, segment-based production.

Audio processing (merge / convert / normalize)

Purpose: Merge files (with optional crossfade), convert format, normalize loudness, trim. CLI: python mmvoice.py merge FILE1 [FILE2 ...] -o OUTPUT [--crossfade MS] python mmvoice.py convert INPUT -o OUTPUT [--format FORMAT] Script: scripts/audio_processing.py (merge, convert, normalize, trim). Documentation: reference/audio-guide.md — format conversion, merging (filter_complex + concat demuxer fallback), normalization, trimming, optimization.

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. DocumentContent for the Agentreference/cli-guide.mdAll 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.mdEnvironment setup (venv, pip install, FFmpeg), MINIMAX_VOICE_API_KEY, basic synthesis test. Use for first-time setup or “env not working”.reference/tts-guide.mdSync TTS (short text), async TTS (long text), streaming TTS, multi-segment production. Use for sync/async/streaming logic and parameters.reference/voice-guide.mdVoice cloning (quick, high-quality with prompt audio, step-by-step), voice design, voice management. Use for custom voice creation flows.reference/audio-guide.mdFormat conversion, merging (including crossfade and fallback), normalization, trimming, optimization. Use for merge/convert/normalize behavior and options.reference/script-examples.mdCopy-paste runnable examples for sync TTS, async TTS, segment-based TTS, audio processing, voice clone/design/management. Use for quick Python snippets.reference/troubleshooting.mdEnvironment (API key, FFmpeg), API errors, segment-based TTS, audio, voice. Use when an error message or unexpected behavior appears.reference/api_documentation.mdFull 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.mdSystem voices list (male/female/beta), selection guide, voice parameters, custom voices, voice IDs. Use to choose or look up voice_id.

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

Run python check_environment.py to diagnose setup issues See troubleshooting.md for common problems and solutions Check getting-started.md for detailed setup instructions