dvcrn 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
CLI Commands
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.
Python API
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
.safetensorsfor instant loading
Output Format
All commands output WAV files:
- Sample rate: 24 kHz
- Channels: Mono
- Bit depth: 16-bit PCM