From 0a4e3b7e64e9e095cfdcd4b3c28bde7aa58839e7 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期六, 11 二月 2023 17:40:00 +0800
Subject: [PATCH] readme
---
funasr/bin/asr_inference_paraformer.py | 219 ++++++++++++++----------------------------------------
1 files changed, 58 insertions(+), 161 deletions(-)
diff --git a/funasr/bin/asr_inference_paraformer.py b/funasr/bin/asr_inference_paraformer.py
index 1455517..6c5acfc 100644
--- a/funasr/bin/asr_inference_paraformer.py
+++ b/funasr/bin/asr_inference_paraformer.py
@@ -3,6 +3,9 @@
import logging
import sys
import time
+import copy
+import os
+import codecs
from pathlib import Path
from typing import Optional
from typing import Sequence
@@ -35,6 +38,8 @@
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.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
+
header_colors = '\033[95m'
end_colors = '\033[0m'
@@ -78,6 +83,7 @@
penalty: float = 0.0,
nbest: int = 1,
frontend_conf: dict = None,
+ hotword_list_or_file: str = None,
**kwargs,
):
assert check_argument_types()
@@ -168,6 +174,34 @@
self.asr_train_args = asr_train_args
self.converter = converter
self.tokenizer = tokenizer
+
+ # 6. [Optional] Build hotword list from file or str
+ if hotword_list_or_file is None:
+ self.hotword_list = None
+ elif os.path.exists(hotword_list_or_file):
+ self.hotword_list = []
+ hotword_str_list = []
+ with codecs.open(hotword_list_or_file, 'r') as fin:
+ for line in fin.readlines():
+ hw = line.strip()
+ hotword_str_list.append(hw)
+ self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
+ self.hotword_list.append([1])
+ hotword_str_list.append('<s>')
+ logging.info("Initialized hotword list from file: {}, hotword list: {}."
+ .format(hotword_list_or_file, hotword_str_list))
+ else:
+ logging.info("Attempting to parse hotwords as str...")
+ self.hotword_list = []
+ hotword_str_list = []
+ for hw in hotword_list_or_file.strip().split():
+ hotword_str_list.append(hw)
+ self.hotword_list.append(self.converter.tokens2ids([i for i in hw]))
+ self.hotword_list.append([1])
+ hotword_str_list.append('<s>')
+ logging.info("Hotword list: {}.".format(hotword_str_list))
+
+
is_use_lm = lm_weight != 0.0 and lm_file is not None
if (ctc_weight == 0.0 or asr_model.ctc == None) and not is_use_lm:
beam_search = None
@@ -181,7 +215,7 @@
self.nbest = nbest
self.frontend = frontend
self.encoder_downsampling_factor = 1
- if asr_train_args.encoder_conf["input_layer"] == "conv2d":
+ if asr_train_args.encoder == "data2vec_encoder" or asr_train_args.encoder_conf["input_layer"] == "conv2d":
self.encoder_downsampling_factor = 4
@torch.no_grad()
@@ -229,8 +263,14 @@
pre_token_length = pre_token_length.round().long()
if torch.max(pre_token_length) < 1:
return []
- decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
- decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
+ if not isinstance(self.asr_model, ContextualParaformer):
+ if self.hotword_list:
+ logging.warning("Hotword is given but asr model is not a ContextualParaformer.")
+ decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length)
+ decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
+ else:
+ decoder_outs = self.asr_model.cal_decoder_with_predictor(enc, enc_len, pre_acoustic_embeds, pre_token_length, hw_list=self.hotword_list)
+ decoder_out, ys_pad_lens = decoder_outs[0], decoder_outs[1]
results = []
b, n, d = decoder_out.size()
@@ -279,162 +319,6 @@
# assert check_return_type(results)
return results
-
-# def inference(
-# maxlenratio: float,
-# minlenratio: float,
-# batch_size: int,
-# beam_size: int,
-# ngpu: int,
-# ctc_weight: float,
-# lm_weight: float,
-# penalty: float,
-# log_level: Union[int, str],
-# data_path_and_name_and_type,
-# asr_train_config: Optional[str],
-# asr_model_file: Optional[str],
-# cmvn_file: Optional[str] = None,
-# raw_inputs: Union[np.ndarray, torch.Tensor] = None,
-# lm_train_config: Optional[str] = None,
-# lm_file: Optional[str] = None,
-# token_type: Optional[str] = None,
-# key_file: Optional[str] = None,
-# word_lm_train_config: Optional[str] = None,
-# bpemodel: Optional[str] = None,
-# allow_variable_data_keys: bool = False,
-# streaming: bool = False,
-# output_dir: Optional[str] = None,
-# dtype: str = "float32",
-# seed: int = 0,
-# ngram_weight: float = 0.9,
-# nbest: int = 1,
-# num_workers: int = 1,
-# frontend_conf: dict = None,
-# fs: Union[dict, int] = 16000,
-# lang: Optional[str] = None,
-# **kwargs,
-# ):
-# assert check_argument_types()
-#
-# if word_lm_train_config is not None:
-# raise NotImplementedError("Word LM 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 speech2text
-# speech2text_kwargs = dict(
-# asr_train_config=asr_train_config,
-# asr_model_file=asr_model_file,
-# cmvn_file=cmvn_file,
-# lm_train_config=lm_train_config,
-# lm_file=lm_file,
-# token_type=token_type,
-# bpemodel=bpemodel,
-# device=device,
-# maxlenratio=maxlenratio,
-# minlenratio=minlenratio,
-# dtype=dtype,
-# beam_size=beam_size,
-# ctc_weight=ctc_weight,
-# lm_weight=lm_weight,
-# ngram_weight=ngram_weight,
-# penalty=penalty,
-# nbest=nbest,
-# frontend_conf=frontend_conf,
-# )
-# speech2text = Speech2Text(**speech2text_kwargs)
-#
-# # 3. Build data-iterator
-# loader = ASRTask.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=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False),
-# collate_fn=ASRTask.build_collate_fn(speech2text.asr_train_args, False),
-# allow_variable_data_keys=allow_variable_data_keys,
-# inference=True,
-# )
-#
-# forward_time_total = 0.0
-# length_total = 0.0
-# finish_count = 0
-# file_count = 1
-# # 7 .Start for-loop
-# # FIXME(kamo): The output format should be discussed about
-# asr_result_list = []
-# if output_dir is not None:
-# writer = DatadirWriter(output_dir)
-# else:
-# writer = None
-#
-# 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 for k, v in batch.items() if not k.endswith("_lengths")}
-#
-# logging.info("decoding, utt_id: {}".format(keys))
-# # N-best list of (text, token, token_int, hyp_object)
-#
-# time_beg = time.time()
-# results = speech2text(**batch)
-# if len(results) < 1:
-# hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
-# results = [[" ", ["sil"], [2], hyp, 10, 6]] * nbest
-# time_end = time.time()
-# forward_time = time_end - time_beg
-# lfr_factor = results[0][-1]
-# length = results[0][-2]
-# forward_time_total += forward_time
-# length_total += length
-# logging.info(
-# "decoding, feature length: {}, forward_time: {:.4f}, rtf: {:.4f}".
-# format(length, forward_time, 100 * forward_time / (length*lfr_factor)))
-#
-# for batch_id in range(_bs):
-# result = [results[batch_id][:-2]]
-#
-# key = keys[batch_id]
-# for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), result):
-# # Create a directory: outdir/{n}best_recog
-# if writer is not None:
-# ibest_writer = writer[f"{n}best_recog"]
-#
-# # Write the result to each file
-# ibest_writer["token"][key] = " ".join(token)
-# ibest_writer["token_int"][key] = " ".join(map(str, token_int))
-# ibest_writer["score"][key] = str(hyp.score)
-#
-# if text is not None:
-# text_postprocessed = postprocess_utils.sentence_postprocess(token)
-# item = {'key': key, 'value': text_postprocessed}
-# asr_result_list.append(item)
-# finish_count += 1
-# # asr_utils.print_progress(finish_count / file_count)
-# if writer is not None:
-# ibest_writer["text"][key] = text
-#
-# logging.info("decoding, utt: {}, predictions: {}".format(key, text))
-#
-# logging.info("decoding, feature length total: {}, forward_time total: {:.4f}, rtf avg: {:.4f}".
-# format(length_total, forward_time_total, 100 * forward_time_total / (length_total*lfr_factor)))
-# return asr_result_list
def inference(
maxlenratio: float,
@@ -544,6 +428,11 @@
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
+ if param_dict is not None:
+ hotword_list_or_file = param_dict.get('hotword')
+ else:
+ hotword_list_or_file = None
+
if ngpu >= 1 and torch.cuda.is_available():
device = "cuda"
else:
@@ -572,6 +461,7 @@
ngram_weight=ngram_weight,
penalty=penalty,
nbest=nbest,
+ hotword_list_or_file=hotword_list_or_file,
)
speech2text = Speech2Text(**speech2text_kwargs)
@@ -653,7 +543,7 @@
ibest_writer["rtf"][key] = rtf_cur
if text is not None:
- text_postprocessed = postprocess_utils.sentence_postprocess(token)
+ text_postprocessed, _ = postprocess_utils.sentence_postprocess(token)
item = {'key': key, 'value': text_postprocessed}
asr_result_list.append(item)
finish_count += 1
@@ -707,7 +597,12 @@
default=1,
help="The number of workers used for DataLoader",
)
-
+ parser.add_argument(
+ "--hotword",
+ type=str_or_none,
+ default=None,
+ help="hotword file path or hotwords seperated by space"
+ )
group = parser.add_argument_group("Input data related")
group.add_argument(
"--data_path_and_name_and_type",
@@ -835,8 +730,10 @@
print(get_commandline_args(), file=sys.stderr)
parser = get_parser()
args = parser.parse_args(cmd)
+ param_dict = {'hotword': args.hotword}
kwargs = vars(args)
kwargs.pop("config", None)
+ kwargs['param_dict'] = param_dict
inference(**kwargs)
--
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