merge inference.py and memory optimization

凌匀

2023-04-21 49be65031b2510b6b94516174ea93019407a1aad

merge inference.py and memory optimization

 funasr/bin/vad_inference.py

@@ -30,7 +30,7 @@
from funasr.utils.types import str2triple_str
from funasr.utils.types import str_or_none
from funasr.utils import asr_utils, wav_utils, postprocess_utils
from funasr.models.frontend.wav_frontend import WavFrontend
from funasr.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline

header_colors = '\033[95m'
end_colors = '\033[0m'
@@ -109,7 +109,7 @@
            fbanks, fbanks_len = self.frontend.forward_fbank(speech, speech_lengths)
            feats, feats_len = self.frontend.forward_lfr_cmvn(fbanks, fbanks_len)
            fbanks = to_device(fbanks, device=self.device)
            feats = to_device(feats, device=self.device)
            # feats = to_device(feats, device=self.device)
            feats_len = feats_len.int()
        else:
            raise Exception("Need to extract feats first, please configure frontend configuration")
@@ -138,6 +138,69 @@
                    segments[batch_num] += segments_part[batch_num]
        return fbanks, segments

class Speech2VadSegmentOnline(Speech2VadSegment):
    """Speech2VadSegmentOnline class

    Examples:
        >>> import soundfile
        >>> speech2segment = Speech2VadSegmentOnline("vad_config.yml", "vad.pt")
        >>> audio, rate = soundfile.read("speech.wav")
        >>> speech2segment(audio)
        [[10, 230], [245, 450], ...]

    """
    def __init__(self, **kwargs):
        super(Speech2VadSegmentOnline, self).__init__(**kwargs)
        vad_cmvn_file = kwargs.get('vad_cmvn_file', None)
        self.frontend = None
        if self.vad_infer_args.frontend is not None:
            self.frontend = WavFrontendOnline(cmvn_file=vad_cmvn_file, **self.vad_infer_args.frontend_conf)


    @torch.no_grad()
    def __call__(
            self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
            in_cache: Dict[str, torch.Tensor] = dict(), is_final: bool = False, max_end_sil: int = 800
    ) -> Tuple[torch.Tensor, List[List[int]], torch.Tensor]:
        """Inference

        Args:
            speech: Input speech data
        Returns:
            text, token, token_int, hyp

        """
        assert check_argument_types()

        # Input as audio signal
        if isinstance(speech, np.ndarray):
            speech = torch.tensor(speech)
        batch_size = speech.shape[0]
        segments = [[]] * batch_size
        if self.frontend is not None:
            feats, feats_len = self.frontend.forward(speech, speech_lengths, is_final)
            fbanks, _ = self.frontend.get_fbank()
        else:
            raise Exception("Need to extract feats first, please configure frontend configuration")
        if feats.shape[0]:
            feats = to_device(feats, device=self.device)
            feats_len = feats_len.int()
            waveforms = self.frontend.get_waveforms()

            batch = {
                "feats": feats,
                "waveform": waveforms,
                "in_cache": in_cache,
                "is_final": is_final,
                "max_end_sil": max_end_sil
            }
            # a. To device
            batch = to_device(batch, device=self.device)
            segments, in_cache = self.vad_model.forward_online(**batch)
            # in_cache.update(batch['in_cache'])
            # in_cache = {key: value for key, value in batch['in_cache'].items()}
        return fbanks, segments, in_cache


def inference(
        batch_size: int,
@@ -154,8 +217,10 @@
        dtype: str = "float32",
        seed: int = 0,
        num_workers: int = 1,
        online: bool = False,
        **kwargs,
):
    if not online:
    inference_pipeline = inference_modelscope(
        batch_size=batch_size,
        ngpu=ngpu,
@@ -171,8 +236,23 @@
        num_workers=num_workers,
        **kwargs,
    )
    else:
        inference_pipeline = inference_modelscope_online(
            batch_size=batch_size,
            ngpu=ngpu,
            log_level=log_level,
            vad_infer_config=vad_infer_config,
            vad_model_file=vad_model_file,
            vad_cmvn_file=vad_cmvn_file,
            key_file=key_file,
            allow_variable_data_keys=allow_variable_data_keys,
            output_dir=output_dir,
            dtype=dtype,
            seed=seed,
            num_workers=num_workers,
            **kwargs,
        )
    return inference_pipeline(data_path_and_name_and_type, raw_inputs)


def inference_modelscope(
        batch_size: int,
@@ -192,9 +272,6 @@
        **kwargs,
):
    assert check_argument_types()
    ncpu = kwargs.get("ncpu", 1)
    torch.set_num_threads(ncpu)
    
    if batch_size > 1:
        raise NotImplementedError("batch decoding is not implemented")
    if ngpu > 1:
@@ -282,6 +359,119 @@

    return _forward

def inference_modelscope_online(
        batch_size: int,
        ngpu: int,
        log_level: Union[int, str],
        # data_path_and_name_and_type,
        vad_infer_config: Optional[str],
        vad_model_file: Optional[str],
        vad_cmvn_file: Optional[str] = None,
        # raw_inputs: Union[np.ndarray, torch.Tensor] = None,
        key_file: Optional[str] = None,
        allow_variable_data_keys: bool = False,
        output_dir: Optional[str] = None,
        dtype: str = "float32",
        seed: int = 0,
        num_workers: int = 1,
        **kwargs,
):
    assert check_argument_types()
    if batch_size > 1:
        raise NotImplementedError("batch decoding is not implemented")
    if ngpu > 1:
        raise NotImplementedError("only single GPU decoding is supported")

    logging.basicConfig(
        level=log_level,
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )

    if ngpu >= 1 and torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"

    # 1. Set random-seed
    set_all_random_seed(seed)

    # 2. Build speech2vadsegment
    speech2vadsegment_kwargs = dict(
        vad_infer_config=vad_infer_config,
        vad_model_file=vad_model_file,
        vad_cmvn_file=vad_cmvn_file,
        device=device,
        dtype=dtype,
    )
    logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
    speech2vadsegment = Speech2VadSegmentOnline(**speech2vadsegment_kwargs)

    def _forward(
            data_path_and_name_and_type,
            raw_inputs: Union[np.ndarray, torch.Tensor] = None,
            output_dir_v2: Optional[str] = None,
            fs: dict = None,
            param_dict: dict = None,
    ):
        # 3. Build data-iterator
        if data_path_and_name_and_type is None and raw_inputs is not None:
            if isinstance(raw_inputs, torch.Tensor):
                raw_inputs = raw_inputs.numpy()
            data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
        loader = VADTask.build_streaming_iterator(
            data_path_and_name_and_type,
            dtype=dtype,
            batch_size=batch_size,
            key_file=key_file,
            num_workers=num_workers,
            preprocess_fn=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
            collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_args, False),
            allow_variable_data_keys=allow_variable_data_keys,
            inference=True,
        )

        finish_count = 0
        file_count = 1
        # 7 .Start for-loop
        # FIXME(kamo): The output format should be discussed about
        output_path = output_dir_v2 if output_dir_v2 is not None else output_dir
        if output_path is not None:
            writer = DatadirWriter(output_path)
            ibest_writer = writer[f"1best_recog"]
        else:
            writer = None
            ibest_writer = None

        vad_results = []
        batch_in_cache = param_dict['in_cache'] if param_dict is not None else dict()
        is_final = param_dict.get('is_final', False) if param_dict is not None else False
        max_end_sil = param_dict.get('max_end_sil', 800) if param_dict is not None else 800
        for keys, batch in loader:
            assert isinstance(batch, dict), type(batch)
            assert all(isinstance(s, str) for s in keys), keys
            _bs = len(next(iter(batch.values())))
            assert len(keys) == _bs, f"{len(keys)} != {_bs}"
            batch['in_cache'] = batch_in_cache
            batch['is_final'] = is_final
            batch['max_end_sil'] = max_end_sil

            # do vad segment
            _, results, param_dict['in_cache'] = speech2vadsegment(**batch)
            # param_dict['in_cache'] = batch['in_cache']
            if results:
                for i, _ in enumerate(keys):
                    if results[i]:
                        if "MODELSCOPE_ENVIRONMENT" in os.environ and os.environ["MODELSCOPE_ENVIRONMENT"] == "eas":
                            results[i] = json.dumps(results[i])
                        item = {'key': keys[i], 'value': results[i]}
                        vad_results.append(item)
                        if writer is not None:
                            results[i] = json.loads(results[i])
                            ibest_writer["text"][keys[i]] = "{}".format(results[i])

        return vad_results

    return _forward

def get_parser():
    parser = config_argparse.ArgumentParser(
@@ -354,6 +544,11 @@
        type=str,
        help="Global cmvn file",
    )
    group.add_argument(
        "--online",
        type=str,
        help="decoding mode",
    )

    group = parser.add_argument_group("infer related")
    group.add_argument(
@@ -377,3 +572,4 @@

if __name__ == "__main__":
    main()


 funasr/bin/vad_inference_launch.py

@@ -1,4 +1,9 @@
#!/usr/bin/env python3
# Copyright ESPnet (https://github.com/espnet/espnet). All Rights Reserved.
#  Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)

import torch
torch.set_num_threads(1)

import argparse
import logging
@@ -109,8 +114,8 @@
        from funasr.bin.vad_inference import inference_modelscope
        return inference_modelscope(**kwargs)
    elif mode == "online":
        from funasr.bin.vad_inference_online import inference_modelscope
        return inference_modelscope(**kwargs)
        from funasr.bin.vad_inference import inference_modelscope_online
        return inference_modelscope_online(**kwargs)
    else:
        logging.info("Unknown decoding mode: {}".format(mode))
        return None

 funasr/models/e2e_vad.py

@@ -311,7 +311,7 @@
                                0.000001))

    def ComputeScores(self, feats: torch.Tensor, in_cache: Dict[str, torch.Tensor]) -> None:
        scores = self.encoder(feats, in_cache)  # return B * T * D
        scores = self.encoder(feats, in_cache).to('cpu')  # return B * T * D
        assert scores.shape[1] == feats.shape[1], "The shape between feats and scores does not match"
        self.vad_opts.nn_eval_block_size = scores.shape[1]
        self.frm_cnt += scores.shape[1]  # count total frames

 funasr/models/frontend/wav_frontend.py

@@ -34,7 +34,7 @@
    means = np.array(means_list).astype(np.float)
    vars = np.array(vars_list).astype(np.float)
    cmvn = np.array([means, vars])
    cmvn = torch.as_tensor(cmvn)
    cmvn = torch.as_tensor(cmvn, dype=torch.float32)
    return cmvn


@@ -47,10 +47,10 @@
    dtype = inputs.dtype
    frame, dim = inputs.shape

    means = np.tile(cmvn[0:1, :dim], (frame, 1))
    vars = np.tile(cmvn[1:2, :dim], (frame, 1))
    inputs += torch.from_numpy(means).type(dtype).to(device)
    inputs *= torch.from_numpy(vars).type(dtype).to(device)
    means = cmvn[0:1, :dim]
    vars = cmvn[1:2, :dim]
    inputs += means.to(device)
    inputs *= vars.to(device)

    return inputs.type(torch.float32)


 funasr/runtime/python/onnxruntime/funasr_onnx/utils/frontend.py

@@ -217,7 +217,7 @@
        frame_num = self.compute_frame_num(input.shape[-1], self.frame_sample_length, self.frame_shift_sample_length)
        # update self.in_cache
        self.input_cache = input[:, -(input.shape[-1] - frame_num * self.frame_shift_sample_length):]
        waveforms = np.empty(0, dtype=np.int16)
        waveforms = np.empty(0, dtype=np.float32)
        feats_pad = np.empty(0, dtype=np.float32)
        feats_lens = np.empty(0, dtype=np.int32)
        if frame_num:
@@ -237,7 +237,7 @@
                    mat[i, :] = self.fbank_fn.get_frame(i)
                feat = mat.astype(np.float32)
                feat_len = np.array(mat.shape[0]).astype(np.int32)
                feats.append(mat)
                feats.append(feat)
                feats_lens.append(feat_len)

            waveforms = np.stack(waveforms)

 tests/test_vad_inference_pipeline.py

@@ -20,6 +20,13 @@
        rec_result = inference_pipeline(
            audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example_8k.wav')
        logger.info("vad inference result: {0}".format(rec_result))
        assert rec_result[
                   "text"] == "[[0, 1960], [2870, 6730], [7960, 10180], [12140, 14830], [15740, 19400], " \
                              "[20220, 24230], [25540, 27290], [30070, 30970], [32070, 34280], [35990, 37050], " \
                              "[39400, 41020], [41810, 47320], [48120, 52150], [53560, 58310], [59290, 62210], " \
                              "[63110, 66420], [67300, 68280], [69670, 71770], [73100, 75550], [76850, 78500], " \
                              "[79380, 83280], [85000, 92320], [93560, 94110], [94990, 95620], [96940, 97590], " \
                              "[98400, 100530], [101600, 104890], [108780, 110900], [112020, 113460], [114210, 115030]]"

    def test_16k(self):
        inference_pipeline = pipeline(
@@ -29,6 +36,10 @@
        rec_result = inference_pipeline(
            audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav')
        logger.info("vad inference result: {0}".format(rec_result))
        assert rec_result[
                   "text"] == "[[70, 2340], [2620, 6200], [6480, 23670], [23950, 26250], [26780, 28990], " \
                              "[29950, 31430], [31750, 37600], [38210, 46900], [47310, 49630], [49910, 56460], " \
                              "[56740, 59540], [59820, 70450]"


if __name__ == '__main__':