From f9fed09e96f43e7eab88378fc444c4987933badb Mon Sep 17 00:00:00 2001
From: zhifu gao <zhifu.gzf@alibaba-inc.com>
Date: 星期五, 09 十二月 2022 23:57:51 +0800
Subject: [PATCH] Merge pull request #10 from alibaba-damo-academy/dev
---
funasr/bin/asr_inference.py | 225 +++++++++++++++++++++++++++++++++++++-------------------
1 files changed, 149 insertions(+), 76 deletions(-)
diff --git a/funasr/bin/asr_inference.py b/funasr/bin/asr_inference.py
index 6ee0ffe..bd5d7f4 100755
--- a/funasr/bin/asr_inference.py
+++ b/funasr/bin/asr_inference.py
@@ -12,6 +12,7 @@
from typing import Sequence
from typing import Tuple
from typing import Union
+from typing import Dict
import numpy as np
import torch
@@ -38,7 +39,21 @@
from funasr.utils.types import str2bool
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 modelscope.utils.logger import get_logger
+
+logger = get_logger()
+
+header_colors = '\033[95m'
+end_colors = '\033[0m'
+
+global_asr_language: str = 'zh-cn'
+global_sample_rate: Union[int, Dict[Any, int]] = {
+ 'audio_fs': 16000,
+ 'model_fs': 16000
+}
class Speech2Text:
"""Speech2Text class
@@ -72,6 +87,7 @@
penalty: float = 0.0,
nbest: int = 1,
streaming: bool = False,
+ frontend_conf: dict = None,
**kwargs,
):
assert check_argument_types()
@@ -81,6 +97,9 @@
asr_model, asr_train_args = ASRTask.build_model_from_file(
asr_train_config, asr_model_file, device
)
+ if asr_model.frontend is None and frontend_conf is not None:
+ frontend = WavFrontend(**frontend_conf)
+ asr_model.frontend = frontend
logging.info("asr_model: {}".format(asr_model))
logging.info("asr_train_args: {}".format(asr_train_args))
asr_model.to(dtype=getattr(torch, dtype)).eval()
@@ -129,36 +148,6 @@
pre_beam_score_key=None if ctc_weight == 1.0 else "full",
)
- # TODO(karita): make all scorers batchfied
- if batch_size == 1:
- non_batch = [
- k
- for k, v in beam_search.full_scorers.items()
- if not isinstance(v, BatchScorerInterface)
- ]
- if len(non_batch) == 0:
- if streaming:
- beam_search.__class__ = BatchBeamSearchOnlineSim
- beam_search.set_streaming_config(asr_train_config)
- logging.info(
- "BatchBeamSearchOnlineSim implementation is selected."
- )
- else:
- beam_search.__class__ = BatchBeamSearch
- logging.info("BatchBeamSearch implementation is selected.")
- else:
- logging.warning(
- f"As non-batch scorers {non_batch} are found, "
- f"fall back to non-batch implementation."
- )
-
- beam_search.to(device=device, dtype=getattr(torch, dtype)).eval()
- for scorer in scorers.values():
- if isinstance(scorer, torch.nn.Module):
- scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
- logging.info(f"Beam_search: {beam_search}")
- logging.info(f"Decoding device={device}, dtype={dtype}")
-
# 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
if token_type is None:
token_type = asr_train_args.token_type
@@ -203,7 +192,7 @@
"""Inference
Args:
- data: Input speech data
+ speech: Input speech data
Returns:
text, token, token_int, hyp
@@ -216,6 +205,7 @@
# data: (Nsamples,) -> (1, Nsamples)
speech = speech.unsqueeze(0).to(getattr(torch, self.dtype))
+ lfr_factor = max(1, (speech.size()[-1] // 80) - 1)
# lengths: (1,)
lengths = speech.new_full([1], dtype=torch.long, fill_value=speech.size(1))
batch = {"speech": speech, "speech_lengths": lengths}
@@ -264,32 +254,36 @@
def inference(
- output_dir: str,
maxlenratio: float,
minlenratio: float,
batch_size: int,
- dtype: str,
beam_size: int,
ngpu: int,
- seed: int,
ctc_weight: float,
lm_weight: float,
- ngram_weight: float,
penalty: float,
- nbest: int,
- num_workers: int,
log_level: Union[int, str],
- data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
- key_file: Optional[str],
+ data_path_and_name_and_type,
asr_train_config: Optional[str],
asr_model_file: Optional[str],
- lm_train_config: Optional[str],
- lm_file: Optional[str],
- word_lm_train_config: Optional[str],
- token_type: Optional[str],
- bpemodel: Optional[str],
- allow_variable_data_keys: bool,
- streaming: bool,
+ audio_lists: Union[List[Any], bytes] = 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()
@@ -309,7 +303,46 @@
device = "cuda"
else:
device = "cpu"
+ hop_length: int = 160
+ sr: int = 16000
+ if isinstance(fs, int):
+ sr = fs
+ else:
+ if 'model_fs' in fs and fs['model_fs'] is not None:
+ sr = fs['model_fs']
+ # data_path_and_name_and_type for modelscope: (data from audio_lists)
+ # ['speech', 'sound', 'am.mvn']
+ # data_path_and_name_and_type for funasr:
+ # [('/mnt/data/jiangyu.xzy/exp/maas/mvn.1.scp', 'speech', 'kaldi_ark')]
+ if isinstance(data_path_and_name_and_type[0], Tuple):
+ features_type: str = data_path_and_name_and_type[0][1]
+ elif isinstance(data_path_and_name_and_type[0], str):
+ features_type: str = data_path_and_name_and_type[1]
+ else:
+ raise NotImplementedError("unknown features type:{0}".format(data_path_and_name_and_type))
+ if features_type != 'sound':
+ frontend_conf = None
+ flag_modelscope = False
+ else:
+ flag_modelscope = True
+ if frontend_conf is not None:
+ if 'hop_length' in frontend_conf:
+ hop_length = frontend_conf['hop_length']
+ finish_count = 0
+ file_count = 1
+ if flag_modelscope and not isinstance(data_path_and_name_and_type[0], Tuple):
+ data_path_and_name_and_type_new = [
+ audio_lists, data_path_and_name_and_type[0], data_path_and_name_and_type[1]
+ ]
+ if isinstance(audio_lists, bytes):
+ file_count = 1
+ else:
+ file_count = len(audio_lists)
+ if len(data_path_and_name_and_type) >= 3 and frontend_conf is not None:
+ mvn_file = data_path_and_name_and_type[2]
+ mvn_data = wav_utils.extract_CMVN_featrures(mvn_file)
+ frontend_conf['mvn_data'] = mvn_data
# 1. Set random-seed
set_all_random_seed(seed)
@@ -332,45 +365,66 @@
penalty=penalty,
nbest=nbest,
streaming=streaming,
+ frontend_conf=frontend_conf,
)
logging.info("speech2text_kwargs: {}".format(speech2text_kwargs))
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,
- )
+ if flag_modelscope:
+ loader = ASRTask.build_streaming_iterator_modelscope(
+ data_path_and_name_and_type_new,
+ 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,
+ sample_rate=fs
+ )
+ else:
+ 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,
+ )
# 7 .Start for-loop
# FIXME(kamo): The output format should be discussed about
- with DatadirWriter(output_dir) as writer:
- 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")}
+ asr_result_list = []
+ if output_dir is not None:
+ writer = DatadirWriter(output_dir)
+ else:
+ writer = None
- # N-best list of (text, token, token_int, hyp_object)
- try:
- results = speech2text(**batch)
- except TooShortUttError as e:
- logging.warning(f"Utterance {keys} {e}")
- hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
- results = [[" ", ["<space>"], [2], hyp]] * nbest
+ 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")}
- # Only supporting batch_size==1
- key = keys[0]
- for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results):
- # Create a directory: outdir/{n}best_recog
+ # N-best list of (text, token, token_int, hyp_object)
+ try:
+ results = speech2text(**batch)
+ except TooShortUttError as e:
+ logging.warning(f"Utterance {keys} {e}")
+ hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
+ results = [[" ", ["<space>"], [2], hyp]] * nbest
+
+ # Only supporting batch_size==1
+ key = keys[0]
+ for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results):
+ # 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
@@ -378,8 +432,25 @@
ibest_writer["token_int"][key] = " ".join(map(str, token_int))
ibest_writer["score"][key] = str(hyp.score)
- if text is not None:
+ 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
+ return asr_result_list
+
+
+def set_parameters(language: str = None,
+ sample_rate: Union[int, Dict[Any, int]] = None):
+ if language is not None:
+ global global_asr_language
+ global_asr_language = language
+ if sample_rate is not None:
+ global global_sample_rate
+ global_sample_rate = sample_rate
def get_parser():
@@ -432,6 +503,8 @@
required=True,
action="append",
)
+ group.add_argument("--audio_lists", type=list, default=None)
+ # example=[{'key':'EdevDEWdIYQ_0021','file':'/mnt/data/jiangyu.xzy/test_data/speech_io/SPEECHIO_ASR_ZH00007_zhibodaihuo/wav/EdevDEWdIYQ_0021.wav'}])
group.add_argument("--key_file", type=str_or_none)
group.add_argument("--allow_variable_data_keys", type=str2bool, default=False)
--
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