From b15db52e4e67da8a133a67e8ffa415386de48b40 Mon Sep 17 00:00:00 2001
From: zhuyunfeng <10596244@qq.com>
Date: 星期二, 09 五月 2023 23:03:15 +0800
Subject: [PATCH] Add contributor
---
funasr/bin/vad_inference.py | 376 ++++++++++++++++++++++++++++++++++++----------------
1 files changed, 258 insertions(+), 118 deletions(-)
diff --git a/funasr/bin/vad_inference.py b/funasr/bin/vad_inference.py
index 679cc0b..5fbd844 100644
--- a/funasr/bin/vad_inference.py
+++ b/funasr/bin/vad_inference.py
@@ -1,6 +1,8 @@
import argparse
import logging
+import os
import sys
+import json
from pathlib import Path
from typing import Any
from typing import List
@@ -10,6 +12,7 @@
from typing import Union
from typing import Dict
+import math
import numpy as np
import torch
from typeguard import check_argument_types
@@ -27,7 +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'
@@ -80,11 +83,13 @@
self.device = device
self.dtype = dtype
self.frontend = frontend
+ self.batch_size = batch_size
@torch.no_grad()
def __call__(
- self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None
- ) -> List[List[int]]:
+ self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
+ in_cache: Dict[str, torch.Tensor] = dict()
+ ) -> Tuple[List[List[int]], Dict[str, torch.Tensor]]:
"""Inference
Args:
@@ -100,106 +105,101 @@
speech = torch.tensor(speech)
if self.frontend is not None:
- feats, feats_len = self.frontend.forward(speech, speech_lengths)
+ self.frontend.filter_length_max = math.inf
+ 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_len = feats_len.int()
else:
raise Exception("Need to extract feats first, please configure frontend configuration")
- batch = {"feats": feats, "feats_lengths": feats_len, "waveform": speech}
- # a. To device
- batch = to_device(batch, device=self.device)
+ # b. Forward Encoder streaming
+ t_offset = 0
+ step = min(feats_len.max(), 6000)
+ segments = [[]] * self.batch_size
+ for t_offset in range(0, feats_len, min(step, feats_len - t_offset)):
+ if t_offset + step >= feats_len - 1:
+ step = feats_len - t_offset
+ is_final = True
+ else:
+ is_final = False
+ batch = {
+ "feats": feats[:, t_offset:t_offset + step, :],
+ "waveform": speech[:, t_offset * 160:min(speech.shape[-1], (t_offset + step - 1) * 160 + 400)],
+ "is_final": is_final,
+ "in_cache": in_cache
+ }
+ # a. To device
+ #batch = to_device(batch, device=self.device)
+ segments_part, in_cache = self.vad_model(**batch)
+ if segments_part:
+ for batch_num in range(0, self.batch_size):
+ segments[batch_num] += segments_part[batch_num]
+ return fbanks, segments
- # b. Forward Encoder
- segments = self.vad_model(**batch)
+class Speech2VadSegmentOnline(Speech2VadSegment):
+ """Speech2VadSegmentOnline class
- return segments
+ 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)
-#def inference(
-# 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,
-# fs: Union[dict, int] = 16000,
-# **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 = Speech2VadSegment(**speech2vadsegment_kwargs)
-# # 3. Build data-iterator
-# 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
-# if output_dir is not None:
-# writer = DatadirWriter(output_dir)
-# else:
-# writer = None
-#
-# vad_results = []
-# 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 = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
-#
-# # do vad segment
-# results = speech2vadsegment(**batch)
-# for i, _ in enumerate(keys):
-# item = {'key': keys[i], 'value': results[i]}
-# vad_results.append(item)
-#
-# return vad_results
+ @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(
@@ -217,30 +217,48 @@
dtype: str = "float32",
seed: int = 0,
num_workers: int = 1,
+ online: bool = False,
**kwargs,
):
- inference_pipeline = inference_modelscope(
- 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,
- )
+ if not online:
+ inference_pipeline = inference_modelscope(
+ 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,
+ )
+ 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,
ngpu: int,
log_level: Union[int, str],
- #data_path_and_name_and_type,
+ # data_path_and_name_and_type,
vad_infer_config: Optional[str],
vad_model_file: Optional[str],
vad_cmvn_file: Optional[str] = None,
@@ -256,8 +274,7 @@
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,
@@ -268,7 +285,7 @@
device = "cuda"
else:
device = "cpu"
-
+ batch_size = 1
# 1. Set random-seed
set_all_random_seed(seed)
@@ -284,11 +301,17 @@
speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
def _forward(
- data_path_and_name_and_type,
- raw_inputs: Union[np.ndarray, torch.Tensor] = None,
- output_dir_v2: Optional[str] = None,
+ 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,
@@ -319,11 +342,12 @@
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 = {k: v[0] for k, v in batch.items() if not k.endswith("_lengths")}
# do vad segment
- results = speech2vadsegment(**batch)
+ _, results = speech2vadsegment(**batch)
for i, _ in enumerate(keys):
+ 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:
@@ -333,6 +357,116 @@
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()
+
+
+ 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"
+ batch_size = 1
+
+ # 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:
+ ibest_writer["text"][keys[i]] = "{}".format(results[i])
+
+ return vad_results
+
+ return _forward
def get_parser():
parser = config_argparse.ArgumentParser(
@@ -405,6 +539,11 @@
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(
@@ -428,3 +567,4 @@
if __name__ == "__main__":
main()
+
--
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