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Tencent SkillHub Β· AI
Pocket TTS Complete Documentation
Generate speech from text using Kyutai Pocket TTS - lightweight, CPU-friendly, streaming TTS with voice cloning. English only. ~6x real-time on M4 MacBook Air.
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Generate speech from text using Kyutai Pocket TTS - lightweight, CPU-friendly, streaming TTS with voice cloning. English only. ~6x real-time on M4 MacBook Air.
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Documentation
Pocket TTS
Lightweight CPU-friendly text-to-speech with voice cloning. No GPU required.
When to Use
Generating speech from text on CPU without GPU Voice cloning from audio samples Streaming audio generation (low latency) Local TTS without API dependencies Real-time speech synthesis (~6x faster than real-time)
Key Features
100M parameters - Small, efficient model CPU-optimized - No GPU needed, uses only 2 cores ~6x real-time - Fast generation on modern CPUs ~200ms latency - To first audio chunk (streaming) Voice cloning - From 3-10s audio samples 24kHz mono WAV - High-quality output English only - More languages planned
Installation
pip install pocket-tts # or uv add pocket-tts
Generate Speech
# Basic generation (default voice) pocket-tts generate --text "Hello world" # Custom voice (local file, URL, or safetensors) pocket-tts generate --voice ./my_voice.wav pocket-tts generate --voice "hf://kyutai/tts-voices/alba-mackenna/casual.wav" pocket-tts generate --voice ./voice.safetensors # Quality tuning pocket-tts generate --temperature 0.7 --lsd-decode-steps 3 See docs/generate.md for full CLI reference.
Start Web Server
# Start FastAPI server with web UI pocket-tts serve # Custom host/port pocket-tts serve --host localhost --port 8080 See docs/serve.md for server options.
Export Voice Embeddings
Convert audio files to .safetensors for faster loading: # Single file pocket-tts export-voice voice.mp3 voice.safetensors # Batch conversion pocket-tts export-voice voices/ embeddings/ --truncate See docs/export_voice.md for export options.
Basic Usage
from pocket_tts import TTSModel import scipy.io.wavfile # Load model model = TTSModel.load_model() # Get voice state voice = model.get_state_for_audio_prompt( "hf://kyutai/tts-voices/alba-mackenna/casual.wav" ) # Generate audio audio = model.generate_audio(voice, "Hello world!") # Save scipy.io.wavfile.write("output.wav", model.sample_rate, audio.numpy())
Load Model
model = TTSModel.load_model( config="b6369a24", # Model variant temp=0.7, # Temperature (0.5-1.0) lsd_decode_steps=1, # Generation steps (1-5) eos_threshold=-4.0 # End-of-sequence threshold )
Voice State
# From audio file/URL voice = model.get_state_for_audio_prompt("./voice.wav") voice = model.get_state_for_audio_prompt("hf://kyutai/tts-voices/alba-mackenna/casual.wav") # From safetensors (fast loading) voice = model.get_state_for_audio_prompt("./voice.safetensors")
Streaming Generation
# Stream audio chunks for chunk in model.generate_audio_stream(voice, "Long text..."): # Process/save/play each chunk as generated print(f"Chunk: {chunk.shape[0]} samples")
Multi-Voice Management
# Preload multiple voices voices = { "casual": model.get_state_for_audio_prompt("hf://kyutai/tts-voices/alba-mackenna/casual.wav"), "announcer": model.get_state_for_audio_prompt("./announcer.safetensors"), } # Use different voices audio1 = model.generate_audio(voices["casual"], "Hey there!") audio2 = model.generate_audio(voices["announcer"], "Breaking news!") See docs/python-api.md for complete API reference.
Available Voices
Pre-made voices from hf://kyutai/tts-voices/: alba-mackenna/casual.wav (default, female) jessica-jian/casual.wav (female) voice-donations/Selfie.wav (male, marius) voice-donations/Butter.wav (male, javert) ears/p010/freeform_speech_01.wav (male, jean) vctk/p244_023.wav (female, fantine) vctk/p262_023.wav (female, eponine) vctk/p303_023.wav (female, azelma) Or clone any voice from your own audio samples.
Voice Cloning Tips
Clean audio - Remove background noise (use Adobe Podcast Enhance) Length - 3-10 seconds of speech is ideal Quality - Input quality affects output quality Format - WAV, MP3, or any common audio format supported
Performance Tips
CPU-only - GPU provides no speedup (model too small, batch size 1) 2 cores - Uses only 2 CPU cores efficiently Streaming - Low latency (<200ms to first chunk) Safetensors - Pre-process voices to .safetensors for instant loading
Output Format
All commands output WAV files: Sample rate: 24 kHz Channels: Mono Bit depth: 16-bit PCM
Links
GitHub Tech Report Paper (arXiv) HuggingFace Model Voice Repository Live Demo
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- docs/export_voice.md
- docs/generate.md
- docs/python-api.md
- docs/serve.md
- README.md