Qwen3-TTS-12Hz-0.6B Base - Mixed Precision and Mixed Frameworks

This repository packages a mixed-precision, mixed-framework deployment bundle derived from Qwen/Qwen3-TTS-12Hz-0.6B-Base for multilingual, reference-audio-conditioned text-to-speech. The bundle uses GGUF, ONNX, LiteRT, and ExecuTorch artifacts and supports streaming synthesis at 24 kHz.

✨ Key Highlights

• Reference-audio-conditioned synthesis — uses a reference recording and transcript to condition the generated voice.
• No intelligibility regression — no intelligibility WER regression was observed against the FP32 Qwen3-TTS Base implementation.
• Reduced model size — 1.52× smaller than the FP32 Qwen3-TTS Base implementation.
• Performance uplift — compared with the FP32 Qwen3-TTS Base implementation:
  • RTF is improved by more than 2× on the MacBook Pro® computer with M4 Pro
  • RTF is improved by more than 2× on the Android™ Vivo X300 smartphone.
• Real-time factor — using one Arm® CPU core with SME2 (An RTF lower than 1 is faster than real time):
  • RTF is 0.396 on a MacBook Pro® computer with an M4 Pro chip
  • RTF is 1.394 on an Android™ Vivo X300 smartphone.
• Time to first chunk — using one Arm® CPU core with SME2
  • latency is 0.284 s on a MacBook Pro® computer with an M4 Pro chip
  • latency is 0.862 s on an Android™ Vivo X300 smartphone.
• Arm® CPU deployment — optimized for efficient execution on Arm® CPUs.
• Streaming output — returns synthesized speech at 24 kHz in streaming mode.

📦 Model Details

Model Description

Qwen3-TTS-12Hz-0.6B-Base is a multilingual text-to-speech model that can synthesize speech conditioned on a reference speaker.

• Developed by: Qwen team, Alibaba Cloud
• Model type: Multilingual text-to-speech with reference-speaker conditioning
• License: Apache-2.0, inherited from the upstream model and source project
• Base model: Qwen/Qwen3-TTS-12Hz-0.6B-Base
• Packaged variant: qwen3_tts_12hz_0.6b_base
• Package form: GGUF, ONNX, TFLite, and ExecuTorch artifacts

Model Sources

• Base model: https://huggingface.co/Qwen/Qwen3-TTS-12Hz-0.6B-Base
• Upstream repository: https://github.com/QwenLM/Qwen3-TTS

🚀 Get Started with the Model

Run with Compute Flow

Follow these steps to run Qwen3-TTS 0.6B Base on macOS® with Apple Silicon or on Linux® with an Arm®-based CPU, such as a Raspberry Pi® 5.

Step 1: Clone Compute Flow and set up the virtual environment

git clone https://github.com/ARM-software/ComputeFlow.git && cd ComputeFlow

uv sync && source .venv/bin/activate

Step 2: Install the brick and model

compute-flow update
compute-flow model install tts:qwen3-tts qwen3-tts-0-6b-base
compute-flow build native

Step 3: Run the Python CLI example

python -m cli_examples.tts_cli \
  -m @manifest/qwen3-tts/qwen3-tts-0-6b-base/qwen3_tts_base_en_manifest.json \
  -p "Hello everyone. I am Jack and I am your personal assistant." \
  -t 1 \
  -o qwen3_tts_base_output.wav

The English manifest uses the bundled reference recording and transcript in assets/.

📊 Quality evaluation

Quality was assessed using WER on a subset of the Seed-TTS corpus. No intelligibility regression was observed against the FP32 Qwen3-TTS Base implementation.

Settings: English, temperature 0.9, top-k 50, and seed 12345. Audio was converted to mono 16 kHz before ASR scoring.

🎯 Performance evaluation

Performance was measured under the following conditions:

• One Arm® CPU core.
• 5 warmups.
• 30 measured runs with a 60-second pause between runs.
• The Android™ Vivo X300 smartphone screen was kept on.

The following methodology and definitions were used:

• The input prompt was: "Hello everyone. I am Jack and I am your personal assistant."
• End-to-end latency is the summed brick execution time for the complete utterance and excludes model setup.
• Average memory is the mean process RSS sampled throughout setup and inference.
• Peak memory is the maximum sampled RSS or high-water mark.

Compared with the FP32 Qwen3-TTS Base implementation, the optimized model provides the following uplift.

🛠️ Technical Specifications

Objective

Generate streaming 24 kHz speech from text while conditioning speaker identity from reference audio.

Runtime Architecture

Precision and Quantization

• talker_q8_0.gguf and code_predictor_q8_0.gguf use Q8_0 quantization.
• speech_decoder_4_post_lc2_conv1x1_as_fc_fc_f32_dw_fp16_no_tconv.tflite is accelerated with FP16 compute.

Input Specification

Output Specification

The model returns synthesized 24 kHz audio in streaming mode.

Manifest Configuration

At runtime, users can select the synthesis language and set the number of active code-prediction groups. The packaged defaults are language-specific and use 16 active code-prediction groups.

Supported languages are Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian. Automatic language selection is also available.

Repository Contents

• qwen3_tts_base_<language>_manifest.json — language-specific configuration.
• tokenizer.json — tokenizer model.
• *.onnx, *.gguf, *.tflite, and spk_encoder.pte — model components.
• benchmarks/ — structured baseline and Arm®-optimized benchmark reports.
• assets/ — language and variant maps, reference-speaker material, and speaker embeddings.
• SHA256SUMS — model-bundle checksums for reproducibility.

🗂️ Model and Asset Origin

• Qwen3-TTS models and assets
• assets/fast_speaker.wav — source of the default reference-speaker audio.

🔐 Checksums

SHA256SUMS was generated by recursively hashing every regular file in the model bundle, including files in subdirectories, except the generated root SHA256SUMS and paths with a dotfile component.

From the model bundle root, verify the checked-out files with:

shasum -a 256 -c SHA256SUMS