Nemo Arabic STT Diacritized
An Arabic speech-to-text pipeline that produces fully diacritized (tashkeel) transcripts. It combines two independently developed models in sequence; it is a packaged inference pipeline, not a single jointly trained...
Model source
- Kind
- Other
- File size
- 84.3 MB
- Version
- vRevision ec48631
- Updated
- Sep 13, 2026
- Downloads
- 10
- Source
- Hugging Face
Source description
Nemo-Arabic-STT-Diacritized
An Arabic speech-to-text pipeline that produces fully diacritized (tashkeel) transcripts. It combines two independently developed models in sequence; it is a packaged inference pipeline, not a single jointly trained architecture.
Pipeline
- Speech recognition: NVIDIA NeMo FastConformer Hybrid
(
stt_ar_fastconformer_hybrid_large_pcd_v1.0) transcribes Arabic speech to plain, undiacritized text. - Diacritization: CATT (Character-based Arabic Tashkeel Transformer) adds tashkeel to the transcript.
Audio in, diacritized Arabic text out. Neither checkpoint was retrained or fine-tuned for this repository.
Files
| File | Description | Source |
|---|---|---|
stt_ar_fastconformer_hybrid_large_pcd_v1.0.nemo | ASR checkpoint, unmodified | NVIDIA, CC-BY-4.0 |
best_ed_mlm_ns_epoch_178.pt | Diacritizer checkpoint, unmodified | abjadai/CATT, Apache-2.0 |
diacritize.py, catt/ | Diacritizer inference code (vendored from CATT) | abjadai/CATT, Apache-2.0 |
pipeline.py | DiacritizedASR — loads both models once, audio in / diacritized text out in a single call | This repository |
server.py | Reference FastAPI server implementing the full pipeline | This repository |
Usage
from pipeline import DiacritizedASR
model = DiacritizedASR(
nemo_path="stt_ar_fastconformer_hybrid_large_pcd_v1.0.nemo",
catt_ckpt="best_ed_mlm_ns_epoch_178.pt",
)
diacritized = model.transcribe("audio.wav")
Both models load once at construction; each .transcribe() call runs ASR followed immediately
by diacritization in the same process. For the two steps individually:
import nemo.collections.asr as nemo_asr
from diacritize import Diacritizer
asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.restore_from(
"stt_ar_fastconformer_hybrid_large_pcd_v1.0.nemo"
)
diacritizer = Diacritizer(ckpt="best_ed_mlm_ns_epoch_178.pt")
text = asr_model.transcribe(["audio.wav"])[0].text
diacritized = diacritizer.diacritize_texts([text])[0]
Or run server.py directly for an HTTP API (POST /transcribe, GET /health).
Example
Input audio (Arabic speech) transcribed and diacritized:
plain: السلام عليكم ورحمة الله وبركاته كيف يمكنني مساعدتك اليوم؟
diacritized: السَّلَامُ عَلَيْكُمْ وَرَحْمَةُ اللَّهِ وَبَرَكَاتُهُ كَيْفَ يُمْكِنُنِي مُسَاعَدَتُكَ الْيَوْمَ؟
Limitations
- Diacritization quality depends on ASR transcript quality; transcription errors propagate to diacritization.
- CATT diacritizes using full-sentence context; very short or ambiguous transcripts may diacritize imperfectly.
- Developed and tested on general Modern Standard Arabic conversational speech, not evaluated on Quranic recitation.
Attribution and License
- ASR model: NVIDIA, stt_ar_fastconformer_hybrid_large_pcd_v1.0, CC-BY-4.0.
- Diacritizer: abjadai/CATT, Apache-2.0.
- This repository (packaging and inference code) is released under CC-BY-4.0, consistent with the ASR model's license and compatible with CATT's Apache-2.0 terms.
Built for the Muslim Arabic voice AI companion project.
Sources
1 source
Sources
1 sourceVerified Sep 13
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
2 excerpts
Source excerpts
2 excerpts--- license: cc-by-4.0 language: - ar tags: - automatic-speech-recognition - arabic - diacritization - tashkeel - speech pipeline_tag: automatic-speech-recognition --- # Nemo-Arabic-STT-Diacritized An Arabic speech-to-text pipeline that produces fully diacritized (tashkeel) transcripts. It combines two independently developed models in sequence; it is a packaged inference pipeline, not a single jointly trained architecture. ## Pipeline 1. **Speech recognition**: [NVIDIA NeMo FastConformer Hybrid](https://huggingface.co/nvidia/stt_ar_fastconformer_hybrid_large_pcd_v1.0) (`stt_ar_fastconformer_hybrid_large_pcd_v1.0`) transcribes Arabic speech to plain, undiacritized text. 2. **Diacritization**: [CATT](https://github.com/abjadai/catt) (Character-based Arabic Tashkeel Transformer) adds tashkeel to the transcript. Audio in, diacritized Arabic text out. Neither checkpoint was retrained or fine-tuned for this repository. ## Files | File | Description | Source | |---|---|---| | `stt_ar_fastconformer_hybrid_large_pcd_v1.0.nemo` | ASR checkpoint, unmodified | NVIDIA, CC-BY-4.0 | | `best_ed_mlm_ns_epoch_178.pt` | Diacritizer checkpoint, unmodified | abjadai/CATT, Apache-2.0 | | `diacritize.py`, `catt/` | Diacritizer inference code (vendored from CATT) | abjadai/CATT, Apache-2.0 | | `pipeline.py` | `DiacritizedASR` — loads both models once, audio in / diacritized text out in a single call | This repository | | `server.py` | Reference FastAPI server implementing the full pipeline | This repository | ## Usage ```python from pipeline import DiacritizedASR model = DiacritizedASR( nemo_path="stt_ar_fastconformer_hybrid_large_pcd_v1.0.nemo", catt_ckpt="best_ed_mlm_ns_epoch_178.pt", ) diacritized = model.transcribe("audio.wav") ``` Both models load once at construction; each `.transcribe()` call runs ASR followed immediately by diacritization in the same process. For the two steps individually: ```python import nemo.collections.asr as nemo_asr from diacritize import Diacritizer asr_model = nemo_asr.models.EncDecHybridRNNTCTCBPEModel.restore_from( "stt_ar_fastconformer_hybrid_large_pcd_v1.0.nemo" ) diacritizer = Diacritizer(ckpt="best_ed_mlm_ns_epoch_178.pt") text = asr_model.transcribe(["audio.wav"])[0].text diacritized = diacritizer.diacritize_texts([text])[0] ``` Or run `server.py` directly for an HTTP API (`POST /transcribe`, `GET /health`). ## Exam...
Source context: 10 downloads · 1 likes · Pipeline automatic-speech-recognition · Library nemo · Repo NightPrince/Nemo-Arabic-STT-Diacritized
