Add Nabra-82M Arabic TTS conversion scripts for Kokoro runtime - #3898
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Nabra-82M is a Kokoro/StyleTTS2 model fine-tuned for Modern Standard Arabic (base: oddadmix/Nabra-82M-v0.1). Its ONNX export shares the Kokoro v1.0 input signature, so it runs on the existing kokoro runtime without code changes. These scripts stamp sherpa metadata (model_type=kokoro, voice=ar, style_dim=510,1,256), generate tokens.txt from the model vocab, pack voices.bin from the [510,1,256] style tensor, and bake an FIR notch at 4800/9600 Hz into the graph to remove iSTFT image tones. Pre-packaged models: https://huggingface.co/marwanelamami/nabra-82m-sherpa-onnx
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Included review availability: Your plan provides up to 10 included reviews per hour; 7 remain after this review. 📝 WalkthroughWalkthroughThe pull request adds a script that converts Nabra-82M ONNX exports for the Kokoro runtime. It updates metadata, adds an FIR notch filter, rewires and sorts the graph, saves the model, and documents packaging and runtime testing. ChangesNabra conversion workflow
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: ⚪ Minimal · up to This PR adds localized Arabic TTS conversion scripts and documentation; no actionable merge-blocking risk remains beyond normal checks and review. 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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Actionable comments posted: 2
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In `@scripts/kokoro/nabra/add_meta_data.py`:
- Line 10: Remove the unused TensorProto symbol from the onnx import in the
module, while preserving the helper and numpy_helper imports.
- Around line 68-71: Insert conv at the original squeeze node index rather than
appending it to g.node, so the producer precedes the Squeeze consumer after its
input is changed to audio_notched.
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scripts/kokoro/nabra/README.mdscripts/kokoro/nabra/add_meta_data.py
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The Conv node was appended after the Squeeze that consumes its output, which violates ONNX topological ordering and fails onnx.checker. Insert the Conv at the Squeeze's index and re-sort the node list so every producer precedes its consumers. Also drop the unused TensorProto import flagged by flake8.
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Both fixed in 4757529: the Conv is now inserted at the Squeeze's index (plus a defensive topological re-sort, since the same ordering issue can arise when appending nodes), and the unused TensorProto import is removed. Verified with onnx.checker.check_model on a regenerated model - passes. |
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Hey @csukuangfj , I would love your thoughts on this, thank you |
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Thank you for your contribution! Could you please upload a few sample generated audio files along with their corresponding transcripts/text so we can easily test and verify the output quality? |
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Hey @csukuangfj, you can find all of them here: https://huggingface.co/marwanelamami/nabra-82m-sherpa-onnx/blob/main/samples/README.md Edit: Note that Nabra-int4-QAT has some noise in its sound, that's a limitation due to its architecture, QAT didn't fix this issue with 3 experiments of increasing the steps or changing configs |
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Any updates @csukuangfj? |
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Follows issue #3897.
oddadmix trained Nabra-82M (oddadmix/Nabra-82M-v0.1), a Kokoro/StyleTTS2 model fine-tuned for Modern Standard Arabic. I quantized and packaged it. Its ONNX export shares the Kokoro v1.0 input signature (
tokensint64[1,T],stylefloat32[1,256],speedfloat32[1]), so it runs on the existing kokoro runtime with no code changes. I verified sherpa's espeak-ng token output matches the training pipeline's symbol-for-symbol on several sentences.This PR adds conversion scripts under
scripts/kokoro/nabra/:add_meta_data.py: stamps sherpa metadata (model_type=kokoro,voice=ar,style_dim=510,1,256, etc.) and bakes an FIR notch (Conv1d, 129 taps) at 4800/9600 Hz into the graph tail to remove iSTFT image tonesrun.sh: end-to-end conversion (metadata + tokens.txt + voices.bin)README.md: usage example withOfflineTtsKokoroModelConfigPre-packaged models (int4/int8/fp16) are at https://huggingface.co/marwanelamami/nabra-82m-sherpa-onnx if you'd rather link than host.
Tested with sherpa-onnx 1.13.6 Python API; RTF ~0.66-0.68 @ 4 threads on Snapdragon 865/870.
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