mlx-tada 0.1.0

TADA speech synthesis on Apple Silicon via MLX

MLX-TADA

TADA speech synthesis on Apple Silicon via MLX.

Installation

pip install mlx-tada

For auto-transcription of reference audio (optional):

pip install mlx-whisper

To convert weights yourself (requires gated Llama access):

pip install "mlx-tada[convert]"

Quick Start

Pre-converted weights from Hugging Face (recommended)

No gated model access required. Weights are downloaded and cached automatically:

from mlx_tada import TadaForCausalLM, save_wav

model = TadaForCausalLM.from_pretrained("HumeAI/mlx-tada-3b", quantize=4)
ref = model.load_reference("speaker.wav")
out = model.generate("Hello, this is a test of TADA speech synthesis.", ref)
save_wav(out.audio, "output.wav")

Available models:

• HumeAI/mlx-tada-1b — 1B English-only (~4.3 GB)
• HumeAI/mlx-tada-3b — 3B multilingual (~8.9 GB)

Convert weights yourself (alternative)

Requires a Hugging Face account with access to the gated Llama 3.2 models.

pip install "mlx-tada[convert]"
huggingface-cli login

# 3B model
python -m mlx_tada.convert_3b ./weights/3b

# 1B model
python -m mlx_tada.convert_1b ./weights/1b

Then load from the local path:

from mlx_tada import TadaForCausalLM, save_wav

model = TadaForCausalLM.from_weights("./weights/3b", quantize=4)

Generate Speech

CLI

python -m mlx_tada.generate \
  --weights ./weights/3b \
  --audio speaker.wav \
  --text "The history of artificial intelligence is a fascinating journey that spans decades of research and innovation. It all began in the 1950s when pioneers like Alan Turing first posed the question of whether machines could think." \
  --output output.wav

With 4-bit quantization (10x faster, 60% less memory):

python -m mlx_tada.generate \
  --weights ./weights/3b \
  --audio speaker.wav \
  --text "The history of artificial intelligence is a fascinating journey that spans decades of research and innovation. It all began in the 1950s when pioneers like Alan Turing first posed the question of whether machines could think." \
  --quantize 4 \
  --output output.wav

Python

from mlx_tada import TadaForCausalLM, save_wav

model = TadaForCausalLM.from_pretrained("HumeAI/mlx-tada-3b", quantize=4)
ref = model.load_reference("speaker.wav")
out = model.generate("The history of artificial intelligence is a fascinating journey that spans decades of research and innovation. It all began in the 1950s when pioneers like Alan Turing first posed the question of whether machines could think.", ref)
save_wav(out.audio, "output.wav")

# out.audio     - numpy float32 array (24kHz)
# out.duration  - audio duration in seconds
# out.rtf       - real-time factor
# out.num_tokens

Inference Options

Control generation behavior with InferenceOptions:

from mlx_tada import TadaForCausalLM, InferenceOptions, save_wav

model = TadaForCausalLM.from_weights("./weights/3b", quantize=4)
ref = model.load_reference("speaker.wav")

opts = InferenceOptions(
    acoustic_cfg_scale=1.6,
    duration_cfg_scale=1.0,
    num_flow_matching_steps=10,
    time_schedule="logsnr",
    cfg_schedule="cosine",
)

out = model.generate(text="Hello world, today is a nice day.", reference=ref, inference_options=opts)
save_wav(out.audio, "output.wav")

The following inference options from the PyTorch version are not currently supported in MLX:

• speed_up_factor
• num_acoustic_candidates
• scorer
• negative_condition_source
• text_only_logit_scale
• spkr_verification_weight

Speech Continuation

Use num_extra_steps to let the model generate speech beyond the provided text. The model continues speaking naturally and stops when it produces an end-of-sequence token:

from mlx_tada import TadaForCausalLM, InferenceOptions, save_wav

model = TadaForCausalLM.from_weights("./weights/3b", quantize=4)
ref = model.load_reference("speaker.wav")

opts = InferenceOptions(
    acoustic_cfg_scale=1.6,
    num_flow_matching_steps=10,
    time_schedule="logsnr",
)

out = model.generate(
    text="The history of artificial intelligence is a fascinating journey that spans decades of research and innovation.",
    reference=ref,
    inference_options=opts,
    num_extra_steps=50,
)
save_wav(out.audio, "output.wav")

Save and Reuse References

from mlx_tada import Reference

ref = model.load_reference("speaker.wav")
ref.save("speaker.npz")

ref = Reference.load("speaker.npz")
out = model.generate("Reusing the same voice.", ref)

Save Audio

from mlx_tada import save_wav
save_wav(out.audio, "output.wav")

Debug Logging

DEBUG=1 python -m mlx_tada.generate \
  --weights ./weights/3b \
  --audio speaker.wav \
  --text "Hello"

from mlx_tada import setup_logging

setup_logging()

Running Tests

MLX_WEIGHTS=./weights/1b pytest tests/ -v