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https://github.com/modelscope/FunASR
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update repo
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嘉渊
parent
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@ -1,42 +1,23 @@
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# -*- encoding: utf-8 -*-
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import argparse
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import logging
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import os
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import sys
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import json
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import math
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from pathlib import Path
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from typing import Any
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from typing import Dict
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from typing import List
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from typing import Optional
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from typing import Sequence
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from typing import Tuple
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from typing import Union
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from typing import Dict
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import math
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import numpy as np
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import torch
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from typeguard import check_argument_types
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from typeguard import check_return_type
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from funasr.fileio.datadir_writer import DatadirWriter
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from funasr.modules.scorers.scorer_interface import BatchScorerInterface
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from funasr.modules.subsampling import TooShortUttError
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from funasr.tasks.vad import VADTask
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from funasr.torch_utils.device_funcs import to_device
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.utils import config_argparse
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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from funasr.utils import asr_utils, wav_utils, postprocess_utils
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from funasr.build_utils.build_model_from_file import build_model_from_file
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from funasr.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline
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from funasr.torch_utils.device_funcs import to_device
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class Speech2VadSegment:
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@ -64,8 +45,8 @@ class Speech2VadSegment:
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assert check_argument_types()
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# 1. Build vad model
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vad_model, vad_infer_args = VADTask.build_model_from_file(
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vad_infer_config, vad_model_file, device
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vad_model, vad_infer_args = build_model_from_file(
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vad_infer_config, vad_model_file, "None", device, task_name="vad"
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)
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frontend = None
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if vad_infer_args.frontend is not None:
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@ -128,13 +109,14 @@ class Speech2VadSegment:
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"in_cache": in_cache
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}
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# a. To device
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#batch = to_device(batch, device=self.device)
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# batch = to_device(batch, device=self.device)
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segments_part, in_cache = self.vad_model(**batch)
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if segments_part:
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for batch_num in range(0, self.batch_size):
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segments[batch_num] += segments_part[batch_num]
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return fbanks, segments
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class Speech2VadSegmentOnline(Speech2VadSegment):
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"""Speech2VadSegmentOnline class
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@ -146,6 +128,7 @@ class Speech2VadSegmentOnline(Speech2VadSegment):
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[[10, 230], [245, 450], ...]
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"""
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def __init__(self, **kwargs):
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super(Speech2VadSegmentOnline, self).__init__(**kwargs)
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vad_cmvn_file = kwargs.get('vad_cmvn_file', None)
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@ -153,7 +136,6 @@ class Speech2VadSegmentOnline(Speech2VadSegment):
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if self.vad_infer_args.frontend is not None:
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self.frontend = WavFrontendOnline(cmvn_file=vad_cmvn_file, **self.vad_infer_args.frontend_conf)
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@torch.no_grad()
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def __call__(
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self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
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@ -198,5 +180,3 @@ class Speech2VadSegmentOnline(Speech2VadSegment):
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# in_cache.update(batch['in_cache'])
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# in_cache = {key: value for key, value in batch['in_cache'].items()}
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return fbanks, segments, in_cache
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@ -1,59 +1,35 @@
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# -*- encoding: utf-8 -*-
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#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import torch
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torch.set_num_threads(1)
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import argparse
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import logging
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import os
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import sys
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from typing import Union, Dict, Any
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from funasr.utils import config_argparse
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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import argparse
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import logging
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import os
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import sys
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import json
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from pathlib import Path
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from typing import Any
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from typing import List
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from typing import Optional
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from typing import Sequence
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from typing import Tuple
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from typing import Union
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from typing import Dict
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import math
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import numpy as np
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import torch
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from typeguard import check_argument_types
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from typeguard import check_return_type
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from funasr.build_utils.build_streaming_iterator import build_streaming_iterator
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from funasr.fileio.datadir_writer import DatadirWriter
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from funasr.modules.scorers.scorer_interface import BatchScorerInterface
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from funasr.modules.subsampling import TooShortUttError
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from funasr.tasks.vad import VADTask
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from funasr.torch_utils.device_funcs import to_device
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from funasr.torch_utils.set_all_random_seed import set_all_random_seed
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from funasr.utils import config_argparse
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from funasr.utils.cli_utils import get_commandline_args
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from funasr.utils.types import str2bool
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from funasr.utils.types import str2triple_str
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from funasr.utils.types import str_or_none
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from funasr.utils import asr_utils, wav_utils, postprocess_utils
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from funasr.models.frontend.wav_frontend import WavFrontend, WavFrontendOnline
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from funasr.bin.vad_infer import Speech2VadSegment, Speech2VadSegmentOnline
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def inference_vad(
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batch_size: int,
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ngpu: int,
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@ -75,7 +51,6 @@ def inference_vad(
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if batch_size > 1:
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raise NotImplementedError("batch decoding is not implemented")
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logging.basicConfig(
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level=log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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@ -112,16 +87,14 @@ def inference_vad(
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if isinstance(raw_inputs, torch.Tensor):
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raw_inputs = raw_inputs.numpy()
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data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
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loader = VADTask.build_streaming_iterator(
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data_path_and_name_and_type,
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loader = build_streaming_iterator(
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task_name="vad",
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preprocess_args=None,
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data_path_and_name_and_type=data_path_and_name_and_type,
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dtype=dtype,
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batch_size=batch_size,
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key_file=key_file,
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num_workers=num_workers,
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preprocess_fn=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
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collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_args, False),
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allow_variable_data_keys=allow_variable_data_keys,
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inference=True,
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)
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finish_count = 0
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@ -157,6 +130,7 @@ def inference_vad(
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return _forward
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def inference_vad_online(
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batch_size: int,
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ngpu: int,
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@ -176,7 +150,6 @@ def inference_vad_online(
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):
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assert check_argument_types()
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logging.basicConfig(
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level=log_level,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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@ -214,16 +187,14 @@ def inference_vad_online(
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if isinstance(raw_inputs, torch.Tensor):
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raw_inputs = raw_inputs.numpy()
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data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
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loader = VADTask.build_streaming_iterator(
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data_path_and_name_and_type,
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loader = build_streaming_iterator(
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task_name="vad",
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preprocess_args=None,
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data_path_and_name_and_type=data_path_and_name_and_type,
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dtype=dtype,
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batch_size=batch_size,
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key_file=key_file,
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num_workers=num_workers,
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preprocess_fn=VADTask.build_preprocess_fn(speech2vadsegment.vad_infer_args, False),
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collate_fn=VADTask.build_collate_fn(speech2vadsegment.vad_infer_args, False),
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allow_variable_data_keys=allow_variable_data_keys,
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inference=True,
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)
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finish_count = 0
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@ -273,8 +244,6 @@ def inference_vad_online(
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return _forward
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def inference_launch(mode, **kwargs):
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if mode == "offline":
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return inference_vad(**kwargs)
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@ -284,6 +253,7 @@ def inference_launch(mode, **kwargs):
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logging.info("Unknown decoding mode: {}".format(mode))
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return None
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def get_parser():
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parser = config_argparse.ArgumentParser(
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description="VAD Decoding",
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@ -405,5 +375,6 @@ def main(cmd=None):
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inference_pipeline = inference_launch(**kwargs)
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return inference_pipeline(kwargs["data_path_and_name_and_type"])
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if __name__ == "__main__":
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main()
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@ -5,7 +5,7 @@ from typeguard import check_argument_types
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from funasr.datasets.iterable_dataset import IterableESPnetDataset
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from funasr.datasets.small_datasets.collate_fn import CommonCollateFn
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from funasr.datasets.small_datasets.preprocessor import build_preprocess
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from funasr.build_utils.build_model_from_file import build_model_from_file
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def build_streaming_iterator(
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task_name,
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@ -20,7 +20,7 @@ def build_streaming_iterator(
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use_collate_fn: bool = True,
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preprocess_fn=None,
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ngpu: int = 0,
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train: bool=False,
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train: bool = False,
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) -> DataLoader:
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"""Build DataLoader using iterable dataset"""
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assert check_argument_types()
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