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import argparse |
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import torch |
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import torchaudio |
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from nemo.collections.asr.models import EncDecCTCModel |
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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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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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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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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( |
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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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def _parse_args(): |
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parser = argparse.ArgumentParser( |
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description="Run inference using GigaAM-CTC checkpoint" |
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) |
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parser.add_argument("--model_config", help="Path to GigaAM-CTC config file (.yaml)") |
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parser.add_argument( |
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"--model_weights", help="Path to GigaAM-CTC checkpoint file (.ckpt)" |
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) |
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parser.add_argument("--audio_path", help="Path to audio signal") |
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parser.add_argument("--device", help="Device: cpu / cuda") |
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return parser.parse_args() |
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def main(model_config: str, model_weights: str, device: str, audio_path: str): |
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model = EncDecCTCModel.from_config_file(model_config) |
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ckpt = torch.load(model_weights, map_location="cpu") |
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model.load_state_dict(ckpt, strict=False) |
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model = model.to(device) |
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model.eval() |
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transcription = model.transcribe([audio_path])[0] |
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print(f"transcription: {transcription}") |
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if __name__ == "__main__": |
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args = _parse_args() |
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main( |
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model_config=args.model_config, |
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model_weights=args.model_weights, |
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device=args.device, |
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audio_path=args.audio_path, |
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) |
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