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.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+encoder-model.onnx.data filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-4.0
+language:
+  - en
+  - es
+  - fr
+  - de
+  - bg
+  - hr
+  - cs
+  - da
+  - nl
+  - et
+  - fi
+  - el
+  - hu
+  - it
+  - lv
+  - lt
+  - mt
+  - pl
+  - pt
+  - ro
+  - sk
+  - sl
+  - sv
+  - ru
+  - uk
+base_model:
+- nvidia/parakeet-tdt-0.6b-v3
+pipeline_tag: automatic-speech-recognition
+tags:
+- automatic-speech-recognition
+- asr
+- onnx
+- onnx-asr
+---
+
+NVIDIA Parakeet TDT 0.6B V3 (Multilingual) [model](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3) converted to ONNX format for [onnx-asr](https://github.com/istupakov/onnx-asr).
+
+Install onnx-asr
+```shell
+pip install onnx-asr[cpu,hub]
+```
+
+Load Parakeet TDT model and recognize wav file
+```py
+import onnx_asr
+model = onnx_asr.load_model("nemo-parakeet-tdt-0.6b-v3")
+print(model.recognize("test.wav"))
+```
+
+Code for models export
+```py
+import nemo.collections.asr as nemo_asr
+from pathlib import Path
+
+model = nemo_asr.models.ASRModel.from_pretrained("nvidia/parakeet-tdt-0.6b-v3")
+
+onnx_dir = Path("nemo-onnx")
+onnx_dir.mkdir(exist_ok=True)
+model.export(str(Path(onnx_dir, "model.onnx")))
+
+with Path(onnx_dir, "vocab.txt").open("wt") as f:
+    for i, token in enumerate([*model.tokenizer.vocab, "<blk>"]):
+        f.write(f"{token} {i}\n")
+```

config.json

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+{
+    "model_type": "nemo-conformer-tdt",
+    "features_size": 128,
+    "subsampling_factor": 8
+}

decoder_joint-model.int8.onnx

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nemo128.onnx

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