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ONNX export of Qwen/Qwen3-TTS-12Hz-1.7B-Base, a multilingual text-to-speech model from Alibaba Cloud's Qwen team with an ECAPA-TDNN speaker encoder for voice cloning from short reference audio. It is not a newly...
Source du modèle
Description de la source
ONNX export of Qwen/Qwen3-TTS-12Hz-1.7B-Base, a multilingual text-to-speech model from Alibaba Cloud's Qwen team with an ECAPA-TDNN speaker encoder for voice cloning from short reference audio. It is not a newly trained model. This is an unofficial community conversion.
The 1.7B Base variant is larger than the 0.6B Base and targets higher-quality synthesis where compute budget permits. The smaller variant is in .
Sources
1 sourceVérifié 9 août
Artefacts du modèle
6 artefactsExtraits de sources
3 extraits
tonythethompson/Qwen3-TTS-12Hz-0.6B-Base-ONNX| Field | Value |
|---|---|
| Upstream model | Qwen/Qwen3-TTS-12Hz-1.7B-Base |
| Upstream source revision | fd4b254389122332181a7c3db7f27e918eec64e3 |
| Packaging source revision | ab09194b7e07e645f2165fda2e95ac97c2446b38 |
| Export tool/script | ONNX export from upstream Qwen3-TTS PyTorch weights (community packaging) |
| Quantization recipe | See onnx/ filenames for FP32/FP16/quant variants shipped in this repo |
| File | Size | Description |
|---|---|---|
speaker_encoder.onnx + .data | ~46 MB | ECAPA-TDNN speaker encoder |
talker_prefill.onnx + .data | ~5 GB | Talker LM prefill |
talker_decode.onnx + .data | ~5 GB | Talker LM single-step decode |
code_predictor.onnx + .data | ~420 MB | Code Predictor |
tokenizer12hz_encode.onnx + .data | ~182 MB | 12 Hz codec tokenizer (encode) |
vocoder.onnx + .data | ~437 MB | Vocoder decoder (24 kHz output) |
Multilingual text-to-speech for local inference via ONNX Runtime. Synthesizes speech using
a ~3-second voice reference clip, enabling voice-consistent output. The 1.7B Base is the
higher-quality option; use the 0.6B Base for lower-latency or lower-VRAM deployments. The
CustomVoice variant is in
tonythethompson/Qwen3-TTS-12Hz-1.7B-CustomVoice-ONNX.
Export tooling, precision, and quantization are recorded in the Source table above. This packaging mirror does not publish independent parity benchmarks; validate on your target execution provider before production use.
Qwen3-TTS is a voice synthesis and voice cloning model capable of producing realistic speech closely matching a target speaker from a short audio reference.
Apache 2.0 — inherited from
Qwen/Qwen3-TTS-12Hz-1.7B-Base. This packaging repo adds no new license terms.
talker_decode.onnx
onnx · 4,69 MB · SHA-256 6ac51727cdf5…32f8 · Hugging Face
--- license: apache-2.0 base_model: Qwen/Qwen3-TTS-12Hz-1.7B-Base base_model_relation: quantized pipeline_tag: text-to-speech library_name: onnxruntime language: - multilingual tags: - onnx - onnxruntime - tts - qwen3-tts - text-to-speech - voice-cloning - ecapa-tdnn --- # Qwen3-TTS 12Hz 1.7B Base — ONNX ONNX export of [Qwen/Qwen3-TTS-12Hz-1.7B-Base](https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base), a multilingual text-to-speech model from Alibaba Cloud's Qwen team with an ECAPA-TDNN speaker encoder for voice cloning from short reference audio. It is not a newly trained model. This is an unofficial community conversion. The 1.7B Base variant is larger than the 0.6B Base and targets higher-quality synthesis where compute budget permits. The smaller variant is in [`tonythethompson/Qwen3-TTS-12Hz-0.6B-Base-ONNX`](https://huggingface.co/tonythethompson/Qwen3-TTS-12Hz-0.6B-Base-ONNX). ## Source | Field | Value | |---|---| | Upstream model | [Qwen/Qwen3-TTS-12Hz-1.7B-Base](https://huggingface.co/Qwen/Qwen3-TTS-12Hz-1.7B-Base) | | Upstream source revision | `fd4b254389122332181a7c3db7f27e918eec64e3` | | Packaging source revision | `ab09194b7e07e645f2165fda2e95ac97c2446b38` | | Export tool/script | ONNX export from upstream Qwen3-TTS PyTorch weights (community packaging) | | Quantization recipe | See `onnx/` filenames for FP32/FP16/quant variants shipped in this repo | ## Files | File | Size | Description | |---|---|---| | `speaker_encoder.onnx` + `.data` | ~46 MB | ECAPA-TDNN speaker encoder | | `talker_prefill.onnx` + `.data` | ~5 GB | Talker LM prefill | | `talker_decode.onnx` + `.data` | ~5 GB | Talker LM single-step decode | | `code_predictor.onnx` + `.data` | ~420 MB | Code Predictor | | `tokenizer12hz_encode.onnx` + `.data` | ~182 MB | 12 Hz codec tokenizer (encode) | | `vocoder.onnx` + `.data` | ~437 MB | Vocoder decoder (24 kHz output) | | `embeddings/` | ~1.4 GB | Text/codec embedding tables (.npy) + config | | `tokenizer/` | ~5 MB | BPE tokenizer (vocab.json, merges.txt) | ## Architecture - **Speaker Encoder**: ECAPA-TDNN, encodes ~3 seconds of reference audio into a speaker embedding - **Talker LM**: Autoregressive transformer (larger than 0.6B), generates 12Hz codec tokens - **Code Predictor**: Predicts remaining RVQ codebook groups from the Talker output - **Vocoder**: RVQ dequantize -> decoder ->...
Source context: 0 downloads · 0 likes · Pipeline text-to-speech · Library onnxruntime · Repo tonythethompson/Qwen3-TTS-12Hz-1.7B-Base-ONNX
Source context: 0 downloads · 0 likes · Pipeline text-to-speech · Library onnxruntime · Repo tonythethompson/Qwen3-TTS-12Hz-1.7B-Base-ONNX
embeddings/| ~1.4 GB |
| Text/codec embedding tables (.npy) + config |
tokenizer/ | ~5 MB | BPE tokenizer (vocab.json, merges.txt) |