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nvidia/parakeet-tdt-0.6b-v3
packaged for sherpa-onnx. The FastConformer
encoder is exported in fp16 (near-fp32 accuracy) and the decoder/joiner are int8,
giving a compact on-device transducer suitable for mobile.| File | ~Size | Notes |
|---|---|---|
encoder.fp16.onnx | 1.18 GB | FastConformer encoder, fp16 (float32 IO) |
decoder.int8.onnx | 11 MB | transducer decoder, int8 |
joiner.int8.onnx | 6 MB | transducer joiner, int8 |
tokens.txt | 92 KB | token table |
1import sherpa_onnx
2
3recognizer = sherpa_onnx.OfflineRecognizer.from_transducer(
4 tokens="tokens.txt",
5 encoder="encoder.fp16.onnx",
6 decoder="decoder.int8.onnx",
7 joiner="joiner.int8.onnx",
8 model_type="nemo_transducer",
9 feature_dim=128,
10 decoding_method="greedy_search",
11 num_threads=4,
12)
13
14import soundfile as sf
15samples, sr = sf.read("audio.wav", dtype="float32")
16s = recognizer.create_stream()
17s.accept_waveform(sr, samples)
18recognizer.decode_stream(s)
19print(s.result.text)model_type="nemo_transducer" and feature_dim=128
are required for this model.parakeet-tdt-0.6b-v3, released under
CC-BY-4.0. This export is redistributed
under the same license. Please credit NVIDIA NeMo and the original model.