Parseh aligner ar v1: ONNX int8 from jonatasgrosman/wav2vec2-large-xlsr-53-arabic@af46c2d8531b8dcbb5e23b952f739b372c2e5d2d

LICENSE

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NOTICE

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+Parseh ONNX aligner — Arabic
+
+Derived from jonatasgrosman/wav2vec2-large-xlsr-53-arabic at immutable revision af46c2d8531b8dcbb5e23b952f739b372c2e5d2d.
+Base model: facebook/wav2vec2-large-xlsr-53.
+
+Changes made: converted the CTC checkpoint to ONNX (opset 17) and dynamically quantized MatMul weights to int8. No retraining was performed.
+
+Training material named by the source card:
+- Mozilla Common Voice (CC0)
+- Arabic Speech Corpus (CC-BY-4.0)
+
+The original source model licence is apache-2.0. This derivative carries the applicable licence text and required attribution; no endorsement is implied.

README.md

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+---
+license: apache-2.0
+language: ar
+library_name: onnx
+tags:
+- onnx
+- wav2vec2
+- ctc
+- forced-alignment
+- quantized
+- parseh
+base_model: jonatasgrosman/wav2vec2-large-xlsr-53-arabic
+base_model_relation: quantized
+datasets:
+- common_voice
+- arabic_speech_corpus
+---
+
+# Parseh Arabic forced-alignment network
+
+This CTC network aligns known Arabic caption text to 16 kHz mono audio in Parseh. It is not a general-purpose speech-recognition model. Parseh runs it locally with NumPy and ONNX Runtime, then uses CTC Viterbi alignment for word spans or character spans.
+
+## Provenance and conversion
+
+Derived from [jonatasgrosman/wav2vec2-large-xlsr-53-arabic](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-arabic) at `af46c2d8531b8dcbb5e23b952f739b372c2e5d2d`, based on `facebook/wav2vec2-large-xlsr-53`. It was converted to ONNX opset 17 and dynamically quantized to int8 (`QUInt8`, per-channel `MatMul` weights); no retraining was performed. Exact source and output hashes are in `meta.json` and `SHA256SUMS`.
+
+## Verification
+
+- ONNX Runtime CPU versions: 1.23.2, 1.30.0; dynamic 0.5 s and 60 s inputs passed.
+- fp32 ONNX/PyTorch log-softmax max absolute difference: `0.00032806396484375`; frame-argmax agreement: `1.0`.
+- int8/fp32 frame-argmax agreement: `1.0`; generated-speech span-start delta median/p95/max: `0.0` / `0.0` / `0.0` ms.
+- Generated 30-second CPU alignment cost: `4.811386` s; peak RSS: `2886012928` bytes.
+
+`espeak-ng` generated audio was used only for pipeline monotonicity, not a real-speech accuracy claim. Unknown characters use a wildcard score to preserve a monotonic path.
+
+## Limitations
+
+Input must be 16 kHz mono audio and known caption text. Noise, accents, text errors, vocabulary gaps, and quantization can affect timestamps. No accuracy guarantee is made. 
+
+## Licence and attribution
+
+The source tag is `apache-2.0`. `LICENSE` and `NOTICE` retain source, base-model, dataset attribution, and changes. No endorsement by original authors, Meta, Mozilla, or Parseh is implied.
+
+## Citation from the source card
+
+```bibtex
+@misc{grosman2021xlsr53-large-arabic,
+  title={Fine-tuned {XLSR}-53 large model for speech recognition in {A}rabic},
+  author={Grosman, Jonatas},
+  howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-arabic}},
+  year={2021}
+}
+```
+
+## Contact
+
+Contact: [development@parseh.io](mailto:development@parseh.io).

SHA256SUMS

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+5e1341f75bd7eb203f1434dc866a7b9ee514d11ba237d56bdb7a9e3ed654674f  NOTICE
+c796a7cd240d86f72b868cdb508ac148117e4af8282a304ef9ff221e8314bebe  README.md
+11db53fe59ba173625d65f04a80f457806cbf7c39882d0736a14bb2484ad5e76  meta.json
+b9829586625dca46c28eaf6654c7e4d6956738eb3b128c77fcb3ef9df31e5b75  model.int8.onnx
+c403ce09975b90dff0dd8302c42d422e9de1f166cd7772df23490069893cb0cf  preprocessor_config.json
+96a92ff9bd106f825c878f502fe341f9f0488ace92d3c76dc40a6b30296efedd  vocab.json

meta.json

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+{
+  "format": 1,
+  "language": "ar",
+  "source": {
+    "repo": "jonatasgrosman/wav2vec2-large-xlsr-53-arabic",
+    "revision": "af46c2d8531b8dcbb5e23b952f739b372c2e5d2d",
+    "license": "apache-2.0",
+    "base_model": "facebook/wav2vec2-large-xlsr-53",
+    "datasets": [
+      "common_voice",
+      "arabic_speech_corpus"
+    ],
+    "files": {
+      "README.md": {
+        "sha256": "6545ef1c22f8053e9f257a3368a19642c434451f5a611f9c662976a1e0dd4586",
+        "size": 8306
+      },
+      "config.json": {
+        "sha256": "9283014cd08d1fbcb13f7965c4dda8ef54a988e40945ff2616e15afcd0f1fac7",
+        "size": 1565
+      },
+      "preprocessor_config.json": {
+        "sha256": "c403ce09975b90dff0dd8302c42d422e9de1f166cd7772df23490069893cb0cf",
+        "size": 158
+      },
+      "pytorch_model.bin": {
+        "sha256": "a0b26f6d9d3edfde1784aef863c192a8cc1e438a23b45910ab648531ebe1857b",
+        "size": 1262142936
+      },
+      "special_tokens_map.json": {
+        "sha256": "bb7068de1150661a10b55f9e4b12a0e77af8bf91f5e45e1b58afaf1d0e17f675",
+        "size": 85
+      },
+      "vocab.json": {
+        "sha256": "96a92ff9bd106f825c878f502fe341f9f0488ace92d3c76dc40a6b30296efedd",
+        "size": 507
+      }
+    }
+  },
+  "model": {
+    "file": "model.int8.onnx",
+    "precision": "int8-dynamic",
+    "input": "input_values",
+    "output": "logits",
+    "sample_rate": 16000,
+    "frame_samples": 320,
+    "min_samples": 400,
+    "normalize": "zero-mean-unit-variance",
+    "blank_id": 0,
+    "vocab_size": 51,
+    "word_delimiter": "|",
+    "text_case": "preserve",
+    "unit": "char"
+  },
+  "text": {
+    "spaces_separate_words": true,
+    "unknown_characters": "wildcard"
+  },
+  "verify": {
+    "tools": {
+      "onnxruntime": [
+        "1.30.0",
+        "1.23.2"
+      ]
+    },
+    "logit_maxabs_fp32_vs_torch": 0.00032806396484375,
+    "frame_argmax_agreement_fp32_vs_torch": 1.0,
+    "frame_argmax_agreement_int8": 1.0,
+    "fp32_size": 1262398239,
+    "int8_size": 356303343,
+    "frames": 49,
+    "runtime": {
+      "1.30.0": {
+        "provider": "CPUExecutionProvider",
+        "runs": {
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+            "samples": 8000,
+            "shape": [
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+            "seconds": 0.065743
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+          "60.0": {
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+            "shape": [
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+            "seconds": 9.251672
+          }
+        }
+      },
+      "1.23.2": {
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+            "seconds": 8.617029
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+    },
+    "word_start_delta_ms_int8_vs_fp32": {
+      "median": 0.0,
+      "p95": 0.0,
+      "max": 0.0,
+      "spans": 2,
+      "test_audio": "espeak-ng generated sentence"
+    },
+    "sanity": {
+      "monotonic_sentence_spans": 2,
+      "unknown_wildcard_spans": 2
+    },
+    "cost": {
+      "audio_seconds": 30,
+      "alignment_seconds": 4.811386,
+      "peak_rss_bytes": 2886012928
+    },
+    "synthetic_sentence": {
+      "voice": "ar",
+      "caption": "\u0645\u0631\u062d\u0628\u0627 \u0628\u0627\u0644\u0639\u0627\u0644\u0645",
+      "samples": 26254,
+      "note": "pipeline/monotonicity check only; not an accuracy claim"
+    }
+  },
+  "files": {
+    "model.int8.onnx": {
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+      "size": 356303343
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+  },
+  "elapsed_seconds": 39.699
+}

model.int8.onnx

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preprocessor_config.json

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+  "do_normalize": true,
+  "feature_size": 1,
+  "padding_side": "right",
+  "padding_value": 0.0,
+  "return_attention_mask": true,
+  "sampling_rate": 16000
+}

vocab.json

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+{"<pad>": 0, "<s>": 1, "</s>": 2, "<unk>": 3, "|": 4, "-": 5, "ء": 6, "آ": 7, "أ": 8, "ؤ": 9, "إ": 10, "ئ": 11, "ا": 12, "ب": 13, "ة": 14, "ت": 15, "ث": 16, "ج": 17, "ح": 18, "خ": 19, "د": 20, "ذ": 21, "ر": 22, "ز": 23, "س": 24, "ش": 25, "ص": 26, "ض": 27, "ط": 28, "ظ": 29, "ع": 30, "غ": 31, "ـ": 32, "ف": 33, "ق": 34, "ك": 35, "ل": 36, "م": 37, "ن": 38, "ه": 39, "و": 40, "ى": 41, "ي": 42, "ً": 43, "ٌ": 44, "ٍ": 45, "َ": 46, "ُ": 47, "ِ": 48, "ّ": 49, "ْ": 50}