Example #1
0
def main():
    parser = ArgumentParser()
    parser.add_argument("--vad_model",
                        type=str,
                        default="MatchboxNet-VAD-3x2",
                        required=False,
                        help="Pass: '******'")
    parser.add_argument(
        "--dataset",
        type=str,
        required=True,
        help=
        "Path of json file of evaluation data. Audio files should have unique names.",
    )
    parser.add_argument("--out_dir",
                        type=str,
                        default="vad_frame",
                        help="Dir of your vad outputs")
    parser.add_argument("--time_length", type=float, default=0.63)
    parser.add_argument("--shift_length", type=float, default=0.01)
    parser.add_argument("--normalize_audio", type=bool, default=False)
    parser.add_argument("--num_workers", type=float, default=20)
    parser.add_argument("--split_duration", type=float, default=400)
    parser.add_argument(
        "--dont_auto_split",
        default=False,
        action='store_true',
        help=
        "Whether to automatically split manifest entry by split_duration to avoid potential CUDA out of memory issue.",
    )

    args = parser.parse_args()

    torch.set_grad_enabled(False)

    if args.vad_model.endswith('.nemo'):
        logging.info(f"Using local VAD model from {args.vad_model}")
        vad_model = EncDecClassificationModel.restore_from(
            restore_path=args.vad_model)
    else:
        logging.info(f"Using NGC cloud VAD model {args.vad_model}")
        vad_model = EncDecClassificationModel.from_pretrained(
            model_name=args.vad_model)

    if not os.path.exists(args.out_dir):
        os.mkdir(args.out_dir)

    # Prepare manifest for streaming VAD
    manifest_vad_input = args.dataset
    if not args.dont_auto_split:
        logging.info("Split long audio file to avoid CUDA memory issue")
        logging.debug(
            "Try smaller split_duration if you still have CUDA memory issue")
        config = {
            'manifest_filepath': manifest_vad_input,
            'time_length': args.time_length,
            'split_duration': args.split_duration,
            'num_workers': args.num_workers,
        }
        manifest_vad_input = prepare_manifest(config)
    else:
        logging.warning(
            "If you encounter CUDA memory issue, try splitting manifest entry by split_duration to avoid it."
        )

    # setup_test_data
    vad_model.setup_test_data(
        test_data_config={
            'vad_stream': True,
            'sample_rate': 16000,
            'manifest_filepath': manifest_vad_input,
            'labels': [
                'infer',
            ],
            'num_workers': args.num_workers,
            'shuffle': False,
            'time_length': args.time_length,
            'shift_length': args.shift_length,
            'trim_silence': False,
            'normalize_audio': args.normalize_audio,
        })

    vad_model = vad_model.to(device)
    vad_model.eval()

    time_unit = int(args.time_length / args.shift_length)
    trunc = int(time_unit / 2)
    trunc_l = time_unit - trunc
    all_len = 0

    data = []
    for line in open(args.dataset, 'r'):
        file = json.loads(line)['audio_filepath'].split("/")[-1]
        data.append(file.split(".wav")[0])
    logging.info(f"Inference on {len(data)} audio files/json lines!")

    status = get_vad_stream_status(data)
    for i, test_batch in enumerate(vad_model.test_dataloader()):
        test_batch = [x.to(device) for x in test_batch]
        with autocast():
            log_probs = vad_model(input_signal=test_batch[0],
                                  input_signal_length=test_batch[1])
            probs = torch.softmax(log_probs, dim=-1)
            pred = probs[:, 1]

            if status[i] == 'start':
                to_save = pred[:-trunc]
            elif status[i] == 'next':
                to_save = pred[trunc:-trunc_l]
            elif status[i] == 'end':
                to_save = pred[trunc_l:]
            else:
                to_save = pred

            all_len += len(to_save)
            outpath = os.path.join(args.out_dir, data[i] + ".frame")
            with open(outpath, "a") as fout:
                for f in range(len(to_save)):
                    fout.write('{0:0.4f}\n'.format(to_save[f]))
        del test_batch
        if status[i] == 'end' or status[i] == 'single':
            logging.debug(
                f"Overall length of prediction of {data[i]} is {all_len}!")
            all_len = 0
Example #2
0
    def diarize(self, paths2audio_files: List[str] = None, batch_size: int = 1):
        """
        """

        if paths2audio_files:
            self.paths2audio_files = paths2audio_files
        else:
            if self._cfg.diarizer.paths2audio_files is None:
                raise ValueError("Pass path2audio files either through config or to diarize method")
            else:
                self.paths2audio_files = self._cfg.diarizer.paths2audio_files

        if type(self.paths2audio_files) is str and os.path.isfile(self.paths2audio_files):
            paths2audio_files = []
            with open(self.paths2audio_files, 'r') as path2file:
                for audiofile in path2file.readlines():
                    audiofile = audiofile.strip()
                    paths2audio_files.append(audiofile)

        elif type(self.paths2audio_files) in [list, ListConfig]:
            paths2audio_files = list(self.paths2audio_files)

        else:
            raise ValueError("paths2audio_files must be of type list or path to file containing audio files")

        self.AUDIO_RTTM_MAP = audio_rttm_map(paths2audio_files, self._cfg.diarizer.path2groundtruth_rttm_files)

        if self.has_vad_model:
            logging.info("Performing VAD")
            mfst_file = self.path2audio_files_to_manifest(paths2audio_files)
            self._dont_auto_split = False
            self._split_duration = 50
            manifest_vad_input = mfst_file

            if not self._dont_auto_split:
                logging.info("Split long audio file to avoid CUDA memory issue")
                logging.debug("Try smaller split_duration if you still have CUDA memory issue")
                config = {
                    'manifest_filepath': mfst_file,
                    'time_length': self._vad_window_length_in_sec,
                    'split_duration': self._split_duration,
                    'num_workers': self._cfg.num_workers,
                }
                manifest_vad_input = prepare_manifest(config)
            else:
                logging.warning(
                    "If you encounter CUDA memory issue, try splitting manifest entry by split_duration to avoid it."
                )

            self._setup_vad_test_data(manifest_vad_input)
            self._run_vad(manifest_vad_input)
        else:
            if not os.path.exists(self._speaker_manifest_path):
                raise NotFoundError("Oracle VAD based manifest file not found")

        self._extract_embeddings(self._speaker_manifest_path)
        out_rttm_dir = os.path.join(self._out_dir, 'pred_rttms')
        os.makedirs(out_rttm_dir, exist_ok=True)

        perform_diarization(
            embeddings_file=self._embeddings_file,
            reco2num=self._num_speakers,
            manifest_path=self._speaker_manifest_path,
            sample_rate=self._cfg.sample_rate,
            window=self._cfg.diarizer.speaker_embeddings.window_length_in_sec,
            shift=self._cfg.diarizer.speaker_embeddings.shift_length_in_sec,
            audio_rttm_map=self.AUDIO_RTTM_MAP,
            out_rttm_dir=out_rttm_dir,
        )