120 lines
3.8 KiB
Python
120 lines
3.8 KiB
Python
#!/usr/bin/env python3
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# Copyright 2024 Xiaomi Corp. (authors: Fangjun Kuang)
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from typing import Dict
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import onnx
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import torch
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import torchaudio
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from nemo.collections.asr.models import EncDecRNNTBPEModel
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from nemo.collections.asr.modules.audio_preprocessing import (
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AudioToMelSpectrogramPreprocessor as NeMoAudioToMelSpectrogramPreprocessor,
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)
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from nemo.collections.asr.parts.preprocessing.features import (
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FilterbankFeaturesTA as NeMoFilterbankFeaturesTA,
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)
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from onnxruntime.quantization import QuantType, quantize_dynamic
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def add_meta_data(filename: str, meta_data: Dict[str, str]):
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"""Add meta data to an ONNX model. It is changed in-place.
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Args:
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filename:
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Filename of the ONNX model to be changed.
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meta_data:
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Key-value pairs.
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"""
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model = onnx.load(filename)
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while len(model.metadata_props):
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model.metadata_props.pop()
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for key, value in meta_data.items():
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meta = model.metadata_props.add()
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meta.key = key
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meta.value = str(value)
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onnx.save(model, filename)
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class FilterbankFeaturesTA(NeMoFilterbankFeaturesTA):
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def __init__(self, mel_scale: str = "htk", wkwargs=None, **kwargs):
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if "window_size" in kwargs:
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del kwargs["window_size"]
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if "window_stride" in kwargs:
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del kwargs["window_stride"]
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super().__init__(**kwargs)
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self._mel_spec_extractor: torchaudio.transforms.MelSpectrogram = (
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torchaudio.transforms.MelSpectrogram(
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sample_rate=self._sample_rate,
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win_length=self.win_length,
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hop_length=self.hop_length,
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n_mels=kwargs["nfilt"],
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window_fn=self.torch_windows[kwargs["window"]],
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mel_scale=mel_scale,
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norm=kwargs["mel_norm"],
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n_fft=kwargs["n_fft"],
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f_max=kwargs.get("highfreq", None),
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f_min=kwargs.get("lowfreq", 0),
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wkwargs=wkwargs,
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)
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)
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class AudioToMelSpectrogramPreprocessor(NeMoAudioToMelSpectrogramPreprocessor):
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def __init__(self, mel_scale: str = "htk", **kwargs):
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super().__init__(**kwargs)
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kwargs["nfilt"] = kwargs["features"]
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del kwargs["features"]
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self.featurizer = (
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FilterbankFeaturesTA( # Deprecated arguments; kept for config compatibility
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mel_scale=mel_scale,
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**kwargs,
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)
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)
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@torch.no_grad()
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def main():
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model = EncDecRNNTBPEModel.from_config_file("./rnnt_model_config.yaml")
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ckpt = torch.load("./rnnt_model_weights.ckpt", map_location="cpu")
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model.load_state_dict(ckpt, strict=False)
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model.eval()
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with open("./tokens.txt", "w", encoding="utf-8") as f:
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for i, s in enumerate(model.joint.vocabulary):
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f.write(f"{s} {i}\n")
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f.write(f"<blk> {i+1}\n")
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print("Saved to tokens.txt")
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model.encoder.export("encoder.onnx")
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model.decoder.export("decoder.onnx")
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model.joint.export("joiner.onnx")
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meta_data = {
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"vocab_size": model.decoder.vocab_size, # not including the blank
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"pred_rnn_layers": model.decoder.pred_rnn_layers,
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"pred_hidden": model.decoder.pred_hidden,
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"normalize_type": "",
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"subsampling_factor": 4,
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"model_type": "EncDecRNNTBPEModel",
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"version": "1",
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"model_author": "https://github.com/salute-developers/GigaAM",
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"license": "https://github.com/salute-developers/GigaAM/blob/main/GigaAM%20License_NC.pdf",
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"language": "Russian",
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"is_giga_am": 1,
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}
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add_meta_data("encoder.onnx", meta_data)
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quantize_dynamic(
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model_input="encoder.onnx",
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model_output="encoder.int8.onnx",
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weight_type=QuantType.QUInt8,
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)
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if __name__ == "__main__":
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main()
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