Awesome-omni-skill text-to-voice

Convert text to speech using Kyutai's Pocket TTS. Use when the user asks to "generate speech", "text to speech", "TTS", "convert text to audio", "voice synthesis", "generate voice", "read aloud", or "create audio from text". Supports voice cloning from audio samples and multiple pre-made voices (alba, marius, javert, jean, fantine, cosette, eponine, azelma).

install

source · Clone the upstream repo

git clone https://github.com/diegosouzapw/awesome-omni-skill

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data-ai/text-to-voice" ~/.claude/skills/diegosouzapw-awesome-omni-skill-text-to-voice-81080e && rm -rf "$T"

manifest: skills/data-ai/text-to-voice/SKILL.md

source content

Text-to-Voice with Kyutai Pocket TTS

Convert text to natural speech using Kyutai's Pocket TTS - a lightweight 100M parameter model that runs efficiently on CPU.

Installation

pip install pocket-tts
# or use uvx to run without installing:
uvx pocket-tts generate

Requires Python 3.10+ and PyTorch 2.5+. GPU not required.

CLI Usage

Basic Generation

# Generate with defaults (saves to ./tts_output.wav)
uvx pocket-tts generate

# Specify text
pocket-tts generate --text "Hello, this is my message."

# Specify output file location
pocket-tts generate --text "Hello" --output-path ./audio/greeting.wav

# Full example with all common options
pocket-tts generate \
  --text "Welcome to the demo." \
  --voice alba \
  --output-path ./output/welcome.wav

CLI Options

Option	Default	Description
`--text`	"Hello world..."	Text to convert to speech
`--voice`	alba	Voice name, local file path, or HuggingFace URL
`--output-path`	`./tts_output.wav`	Where to save the generated audio file
`--temperature`	0.7	Generation temperature (higher = more expressive)
`--lsd-decode-steps`	1	Quality steps (higher = better quality, slower)
`--eos-threshold`	-4.0	End detection threshold (lower = finish earlier)
`--frames-after-eos`	auto	Extra frames after end (each frame = 80ms)
`--device`	cpu	Device to use (cpu/cuda)
`-q, --quiet`	false	Disable logging output

Voice Selection (CLI)

# Use a pre-made voice by name
pocket-tts generate --voice alba --text "Hello"
pocket-tts generate --voice javert --text "Hello"

# Use a local audio file for voice cloning
pocket-tts generate --voice ./my_voice.wav --text "Hello"

# Use a voice from HuggingFace
pocket-tts generate --voice "hf://kyutai/tts-voices/alba-mackenna/merchant.wav" --text "Hello"

Quality Tuning (CLI)

# Higher quality (more generation steps)
pocket-tts generate --lsd-decode-steps 5 --temperature 0.5 --output-path high_quality.wav

# More expressive/varied output
pocket-tts generate --temperature 1.0 --output-path expressive.wav

# Shorter output (finishes speaking earlier)
pocket-tts generate --eos-threshold -3.0 --output-path shorter.wav

Local Web Server

For quick iteration with multiple voices/texts:

uvx pocket-tts serve
# Open http://localhost:8000

Available Voices

Pre-made voices (use name directly with

--voice

Voice	Gender	License	Description
`alba`	Female	CC BY 4.0	Casual voice
`marius`	Male	CC0	Voice donation
`javert`	Male	CC0	Voice donation
`jean`	Male	CC-NC	EARS dataset
`fantine`	Female	CC BY 4.0	VCTK dataset
`cosette`	Female	CC-NC	Expresso dataset
`eponine`	Female	CC BY 4.0	VCTK dataset
`azelma`	Female	CC BY 4.0	VCTK dataset

Full voice catalog: https://huggingface.co/kyutai/tts-voices

For detailed voice information, see references/voices.md.

Voice Cloning

Clone any voice from an audio sample. For best results:

Use clean audio (minimal background noise)
10+ seconds recommended
Consider Adobe Podcast Enhance to clean samples

pocket-tts generate --voice ./my_recording.wav --text "Hello" --output-path cloned.wav

Output Format

Sample Rate: 24kHz
Channels: Mono
Format: 16-bit PCM WAV
Default location:
```
./tts_output.wav
```

Python API

For programmatic use:

from pocket_tts import TTSModel
import scipy.io.wavfile

tts_model = TTSModel.load_model()
voice_state = tts_model.get_state_for_audio_prompt("alba")
audio = tts_model.generate_audio(voice_state, "Hello world!")

# Save to specific location
scipy.io.wavfile.write("./audio/output.wav", tts_model.sample_rate, audio.numpy())

TTSModel.load_model()

model = TTSModel.load_model(
    variant="b6369a24",      # Model variant
    temp=0.7,                # Temperature (0.0-1.0)
    lsd_decode_steps=1,      # Generation steps
    noise_clamp=None,        # Max noise value
    eos_threshold=-4.0       # End-of-sequence threshold
)

Voice State

# Pre-made voice
voice_state = model.get_state_for_audio_prompt("alba")

# Local file
voice_state = model.get_state_for_audio_prompt("./my_voice.wav")

# HuggingFace
voice_state = model.get_state_for_audio_prompt("hf://kyutai/tts-voices/alba-mackenna/casual.wav")

Generate Audio

audio = model.generate_audio(voice_state, "Text to speak")
# Returns: torch.Tensor (1D)

Streaming

for chunk in model.generate_audio_stream(voice_state, "Long text..."):
    # Process each chunk as it's generated
    pass

Properties

```
model.sample_rate
```
- 24000 Hz
```
model.device
```
- "cpu" or "cuda"

Performance

~200ms latency to first audio chunk
~6x real-time on MacBook Air M4 CPU
Uses only 2 CPU cores

Limitations

English only
No built-in pause/silence control