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F5-TTS-German-ONNX
nibor1896/F5-TTS-German-ONNX
F5-TTS-German-ONNX is a text-to-speech model from nibor1896. Use it when you need text read aloud. It is set up for f5-tts. The card lists the license as cc-by-nc-4.0.
Pure-ONNX export of the German F5-TTS voice-cloning model hvoss-techfak/F5-TTS-German, runnable with plain ONNX Runtime — no PyTorch, no Python required at inference time. This makes it easy to embed German text-to-sp…
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Updated Jul 13, 2026
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At a glance
- Task
- Text-to-Speech
- Library
- f5-tts
- License
- cc-by-nc-4.0
- Access
- Public
- Created
- Jul 13, 2026
- Updated
- Jul 13, 2026
- SHA
- 6d46e3a9
Base models
Model card
From the Hugging Face model README
F5-TTS-German — ONNX
Pure-ONNX export of the German F5-TTS voice-cloning model
hvoss-techfak/F5-TTS-German, runnable with
plain ONNX Runtime — no PyTorch, no Python required at inference
time. This makes it easy to embed German text-to-speech in native applications (C#, C++, …) with
only the ONNX Runtime dependency.
Exported with DakeQQ/F5-TTS-ONNX.
Why this export exists
hvoss-techfak/F5-TTS-German is a fine-tune of the F5TTS_Base (v0) architecture
(text_mask_padding=False, pe_attn_head=1). DakeQQ's exporter was hard-wired for F5TTS_v1_Base,
so exporting a v0 checkpoint produced fluent-but-wrong "word-salad" in the target language.
The root cause and fix were contributed upstream in DakeQQ/F5-TTS-ONNX#74 (merged):
AttnProcessornow honorspe_attn_head— for v0 checkpoints RoPE is applied to the first head only, not all heads.TextEmbeddingnow honorsmask_padding— v0 checkpoints must not zero out padding positions.
These files were exported from the fixed exporter with --omegacfg_path .../F5TTS_Base.yaml.
Files
| File | Size | Role |
|---|---|---|
F5_Preprocess.onnx | ~65 MB | Reference audio (int16) + text ids → mel/text conditioning, RoPE tables, initial noise |
F5_Transformer.onnx | ~1.26 GB | One denoising step of the DiT (run in an NFE loop, default 32 steps) |
F5_Decode.onnx | ~60 MB | Denoised mel → 24 kHz int16 waveform (vocoder + ISTFT) |
vocab.txt | — | Character/token vocabulary (unchanged from the source checkpoint) |
Inference (pipeline order)
- Tokenize the reference text + target text to
text_ids(int32). For German, a plain character → vocab-index mapping is sufficient (jieba/pinyin segmentation is not required). - Load the reference voice clip as 24 kHz mono int16.
F5_Preprocess→noise, rope_cos_q, rope_sin_q, rope_cos_k, rope_sin_k, cat_mel_text, cat_mel_text_drop, ref_signal_len.- Loop
F5_Transformerfor N steps (default 32), feeding the step index intotime_step.1and the runningnoise/denoisedback in. F5_Decode(denoised, ref_signal_len)→ int16 audio @ 24 kHz.
A reference implementation is F5-TTS-ONNX-Inference.py in the
DakeQQ/F5-TTS-ONNX repo.
Verification
The exported models were verified end-to-end (pure ONNX Runtime) on an umlaut-heavy German
sentence and transcribed with faster-whisper large-v3: language de, confidence 1.00,
exact target transcript.
License & attribution
- Model weights derive from
hvoss-techfak/F5-TTS-Germanand inherit its CC-BY-NC-4.0 (non-commercial) license. - Base architecture: F5-TTS (SWivid et al.).
- ONNX export tooling: DakeQQ/F5-TTS-ONNX.
If you use this, please credit the original hvoss-techfak/F5-TTS-German checkpoint.
- Task
- Text-to-Speech
- Library
- f5-tts
- License
- cc-by-nc-4.0
- Languages
- de
- Created
- Jul 13, 2026
- Updated
- Jul 13, 2026