Merge pull request #153 from alibaba-damo-academy/dev_gzf

zhifu gao

2023-02-25 dae8f7472dee1f630d45828c186f028c05cb298f

Merge pull request #153 from alibaba-damo-academy/dev_gzf

Dev gzf

 funasr/export/export_model.py

@@ -58,7 +58,7 @@
        if enc_size:
            dummy_input = model.get_dummy_inputs(enc_size)
        else:
            dummy_input = model.get_dummy_inputs_txt()
            dummy_input = model.get_dummy_inputs()

        # model_script = torch.jit.script(model)
        model_script = torch.jit.trace(model, dummy_input)
@@ -111,12 +111,117 @@
            dummy_input,
            os.path.join(path, f'{model.model_name}.onnx'),
            verbose=verbose,
            opset_version=14,
            input_names=model.get_input_names(),
            output_names=model.get_output_names(),
            dynamic_axes=model.get_dynamic_axes()
        )


class ASRModelExport:
    def __init__(self, cache_dir: Union[Path, str] = None, onnx: bool = True):
        assert check_argument_types()
        self.set_all_random_seed(0)
        if cache_dir is None:
            cache_dir = Path.home() / ".cache" / "export"
        
        self.cache_dir = Path(cache_dir)
        self.export_config = dict(
            feats_dim=560,
            onnx=False,
        )
        print("output dir: {}".format(self.cache_dir))
        self.onnx = onnx
    
    def _export(
        self,
        model: Speech2Text,
        tag_name: str = None,
        verbose: bool = False,
    ):
        
        export_dir = self.cache_dir / tag_name.replace(' ', '-')
        os.makedirs(export_dir, exist_ok=True)
        
        # export encoder1
        self.export_config["model_name"] = "model"
        model = get_model(
            model,
            self.export_config,
        )
        model.eval()
        # self._export_onnx(model, verbose, export_dir)
        if self.onnx:
            self._export_onnx(model, verbose, export_dir)
        else:
            self._export_torchscripts(model, verbose, export_dir)
        
        print("output dir: {}".format(export_dir))
    
    def _export_torchscripts(self, model, verbose, path, enc_size=None):
        if enc_size:
            dummy_input = model.get_dummy_inputs(enc_size)
        else:
            dummy_input = model.get_dummy_inputs_txt()
        
        # model_script = torch.jit.script(model)
        model_script = torch.jit.trace(model, dummy_input)
        model_script.save(os.path.join(path, f'{model.model_name}.torchscripts'))
    
    def set_all_random_seed(self, seed: int):
        random.seed(seed)
        np.random.seed(seed)
        torch.random.manual_seed(seed)
    
    def export(self,
               tag_name: str = 'damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
               mode: str = 'paraformer',
               ):
        
        model_dir = tag_name
        if model_dir.startswith('damo/'):
            from modelscope.hub.snapshot_download import snapshot_download
            model_dir = snapshot_download(model_dir, cache_dir=self.cache_dir)
        asr_train_config = os.path.join(model_dir, 'config.yaml')
        asr_model_file = os.path.join(model_dir, 'model.pb')
        cmvn_file = os.path.join(model_dir, 'am.mvn')
        json_file = os.path.join(model_dir, 'configuration.json')
        if mode is None:
            import json
            with open(json_file, 'r') as f:
                config_data = json.load(f)
                mode = config_data['model']['model_config']['mode']
        if mode.startswith('paraformer'):
            from funasr.tasks.asr import ASRTaskParaformer as ASRTask
        elif mode.startswith('uniasr'):
            from funasr.tasks.asr import ASRTaskUniASR as ASRTask
        
        model, asr_train_args = ASRTask.build_model_from_file(
            asr_train_config, asr_model_file, cmvn_file, 'cpu'
        )
        self._export(model, tag_name)
    
    def _export_onnx(self, model, verbose, path, enc_size=None):
        if enc_size:
            dummy_input = model.get_dummy_inputs(enc_size)
        else:
            dummy_input = model.get_dummy_inputs()
        
        # model_script = torch.jit.script(model)
        model_script = model  # torch.jit.trace(model)
        
        torch.onnx.export(
            model_script,
            dummy_input,
            os.path.join(path, f'{model.model_name}.onnx'),
            verbose=verbose,
            opset_version=12,
            input_names=model.get_input_names(),
            output_names=model.get_output_names(),
            dynamic_axes=model.get_dynamic_axes()
        )


if __name__ == '__main__':
    import sys
    

 funasr/export/models/__init__.py

@@ -1,5 +1,6 @@
from funasr.models.e2e_asr_paraformer import Paraformer
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
from funasr.models.e2e_uni_asr import UniASR

def get_model(model, export_config=None):


 funasr/export/models/e2e_asr_paraformer.py

@@ -59,7 +59,7 @@
        enc, enc_len = self.encoder(**batch)
        mask = self.make_pad_mask(enc_len)[:, None, :]
        pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = self.predictor(enc, mask)
        pre_token_length = pre_token_length.round().type(torch.int32)
        pre_token_length = pre_token_length.floor().type(torch.int32)

        decoder_out, _ = self.decoder(enc, enc_len, pre_acoustic_embeds, pre_token_length)
        decoder_out = torch.log_softmax(decoder_out, dim=-1)

 funasr/export/models/predictor/cif.py

@@ -16,6 +16,11 @@
    

    return mask.type(dtype).to(device) if device is not None else mask.type(dtype)



def sequence_mask_scripts(lengths, maxlen:int):

    row_vector = torch.arange(0, maxlen, 1).type(lengths.dtype).to(lengths.device)

    matrix = torch.unsqueeze(lengths, dim=-1)

    mask = row_vector < matrix

    return mask.type(torch.float32).to(lengths.device)



class CifPredictorV2(nn.Module):

    def __init__(self, model):

@@ -71,28 +76,29 @@
        

        return hidden, alphas, token_num_floor





@torch.jit.script

def cif(hidden, alphas, threshold: float):

    batch_size, len_time, hidden_size = hidden.size()

    threshold = torch.tensor([threshold], dtype=alphas.dtype).to(alphas.device)

    

    # loop varss

    integrate = torch.zeros([batch_size], device=hidden.device)

    frame = torch.zeros([batch_size, hidden_size], device=hidden.device)

    integrate = torch.zeros([batch_size], dtype=alphas.dtype, device=hidden.device)

    frame = torch.zeros([batch_size, hidden_size], dtype=hidden.dtype, device=hidden.device)

    # intermediate vars along time

    list_fires = []

    list_frames = []

    

    for t in range(len_time):

        alpha = alphas[:, t]

        distribution_completion = torch.ones([batch_size], device=hidden.device) - integrate

        distribution_completion = torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device) - integrate

        

        integrate += alpha

        list_fires.append(integrate)

        

        fire_place = integrate >= threshold

        integrate = torch.where(fire_place,

                                integrate - torch.ones([batch_size], device=hidden.device),

                                integrate - torch.ones([batch_size], dtype=alphas.dtype, device=hidden.device),

                                integrate)

        cur = torch.where(fire_place,

                          distribution_completion,

@@ -107,12 +113,20 @@
    

    fires = torch.stack(list_fires, 1)

    frames = torch.stack(list_frames, 1)

    list_ls = []

    len_labels = torch.round(alphas.sum(-1)).int()

    max_label_len = len_labels.max()

    # list_ls = []

    len_labels = torch.round(alphas.sum(-1)).type(torch.int32)

    # max_label_len = int(torch.max(len_labels).item())

    # print("type: {}".format(type(max_label_len)))

    fire_idxs = fires >= threshold

    frame_fires = torch.zeros_like(hidden)

    max_label_len = frames[0, fire_idxs[0]].size(0)

    for b in range(batch_size):

        fire = fires[b, :]

        l = torch.index_select(frames[b, :, :], 0, torch.nonzero(fire >= threshold).squeeze())

        pad_l = torch.zeros([int(max_label_len - l.size(0)), int(hidden_size)], device=hidden.device)

        list_ls.append(torch.cat([l, pad_l], 0))

    return torch.stack(list_ls, 0), fires

        # fire = fires[b, :]

        frame_fire = frames[b, fire_idxs[b]]

        frame_len = frame_fire.size(0)

        frame_fires[b, :frame_len, :] = frame_fire

	
        if frame_len >= max_label_len:

            max_label_len = frame_len

    frame_fires = frame_fires[:, :max_label_len, :]

    return frame_fires, fires


 scan.py