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BytesAgain β€Ί Skills β€Ί Pocket Tts
πŸ¦€ ClawHub

Pocket Tts

by @sherajdev

Generate high-quality English speech offline on CPU using 8 built-in voices or custom voice cloning with Kyutai's Pocket TTS model.

Versionv1.0.1
Downloads2,650
Stars⭐ 3
#clawhub
TERMINAL
clawhub install pocket-tts

πŸ“– About This Skill

Pocket TTS Skill

Fully local, offline text-to-speech using Kyutai's Pocket TTS model. Generate high-quality audio from text without any API calls or internet connection. Features 8 built-in voices, voice cloning support, and runs entirely on CPU.

Features

  • 🎯 Fully local - No API calls, runs completely offline
  • πŸš€ CPU-only - No GPU required, works on any computer
  • ⚑ Fast generation - ~2-6x real-time on CPU
  • 🎀 8 built-in voices - alba, marius, javert, jean, fantine, cosette, eponine, azelma
  • 🎭 Voice cloning - Clone any voice from a WAV sample
  • πŸ”Š Low latency - ~200ms first audio chunk
  • πŸ“š Simple Python API - Easy integration into any project

    • Installation

    # 1. Accept the model license on Hugging Face

    https://huggingface.co/kyutai/pocket-tts
    2. Install the package
    
pip install pocket-tts
    Or use uv for automatic dependency management
    
uvx pocket-tts generate "Hello world"

    Usage

    CLI

    # Basic usage
pocket-tts "Hello, I am your AI assistant"
    With specific voice
    
pocket-tts "Hello" --voice alba --output hello.wav
    With custom voice file (voice cloning)
    
pocket-tts "Hello" --voice-file myvoice.wav --output output.wav
    Adjust speed
    
pocket-tts "Hello" --speed 1.2
    Start local server
    
pocket-tts --serve
    List available voices
    
pocket-tts --list-voices

    Python API

    from pocket_tts import TTSModel
import scipy.io.wavfile
    Load model
    
tts_model = TTSModel.load_model()
    Get voice state
    
voice_state = tts_model.get_state_for_audio_prompt(
    "hf://kyutai/tts-voices/alba-mackenna/casual.wav"
)
    Generate audio
    
audio = tts_model.generate_audio(voice_state, "Hello world!")
    Save to WAV
    
scipy.io.wavfile.write("output.wav", tts_model.sample_rate, audio.numpy())
    Check sample rate
    
print(f"Sample rate: {tts_model.sample_rate} Hz")

    Available Voices

    | Voice | Description | |-------|-------------| | alba | Casual female voice | | marius | Male voice | | javert | Clear male voice | | jean | Natural male voice | | fantine | Female voice | | cosette | Female voice | | eponine | Female voice | | azelma | Female voice |

    Or use --voice-file /path/to/wav.wav for custom voice cloning.

    Options

    | Option | Description | Default | |--------|-------------|---------| | text | Text to convert | Required | | -o, --output | Output WAV file | output.wav | | -v, --voice | Voice preset | alba | | -s, --speed | Speech speed (0.5-2.0) | 1.0 | | --voice-file | Custom WAV for cloning | None | | --serve | Start HTTP server | False | | --list-voices | List all voices | False |

    Requirements

  • Python 3.10-3.14
  • PyTorch 2.5+ (CPU version works)
  • Works on 2 CPU cores

    • Notes

  • ⚠️ Model is gated - accept license on Hugging Face first
  • 🌍 English language only (v1)
  • πŸ’Ύ First run downloads model (~100M parameters)
  • πŸ”Š Audio is returned as 1D torch tensor (PCM data)

    • Links

  • Demo
  • GitHub
  • Hugging Face
  • Paper

    • πŸ’‘ Examples

    CLI

    # Basic usage
pocket-tts "Hello, I am your AI assistant"
    With specific voice
    
pocket-tts "Hello" --voice alba --output hello.wav
    With custom voice file (voice cloning)
    
pocket-tts "Hello" --voice-file myvoice.wav --output output.wav
    Adjust speed
    
pocket-tts "Hello" --speed 1.2
    Start local server
    
pocket-tts --serve
    List available voices
    
pocket-tts --list-voices

    Python API

    from pocket_tts import TTSModel
import scipy.io.wavfile
    Load model
    
tts_model = TTSModel.load_model()
    Get voice state
    
voice_state = tts_model.get_state_for_audio_prompt(
    "hf://kyutai/tts-voices/alba-mackenna/casual.wav"
)
    Generate audio
    
audio = tts_model.generate_audio(voice_state, "Hello world!")
    Save to WAV
    
scipy.io.wavfile.write("output.wav", tts_model.sample_rate, audio.numpy())
    Check sample rate
    
print(f"Sample rate: {tts_model.sample_rate} Hz")

    βš™οΈ Configuration

    | Option | Description | Default | |--------|-------------|---------| | text | Text to convert | Required | | -o, --output | Output WAV file | output.wav | | -v, --voice | Voice preset | alba | | -s, --speed | Speech speed (0.5-2.0) | 1.0 | | --voice-file | Custom WAV for cloning | None | | --serve | Start HTTP server | False | | --list-voices | List all voices | False |

    πŸ“‹ Tips & Best Practices

  • ⚠️ Model is gated - accept license on Hugging Face first
  • 🌍 English language only (v1)
  • πŸ’Ύ First run downloads model (~100M parameters)
  • πŸ”Š Audio is returned as 1D torch tensor (PCM data)