From c5274e728aa3350a778889b77dac288234dbb9a0 Mon Sep 17 00:00:00 2001
From: aky15 <ankeyuthu@gmail.com>
Date: 星期一, 10 七月 2023 12:48:50 +0800
Subject: [PATCH] Update asr_inference_launch.py (#719)
---
funasr/bin/asr_inference_launch.py | 118 +++++++++++++++++++++++++++++++++--------------------------
1 files changed, 66 insertions(+), 52 deletions(-)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index de18894..10f8e50 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -1272,27 +1272,27 @@
nbest: int,
num_workers: int,
log_level: Union[int, str],
- data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
+ # data_path_and_name_and_type: Sequence[Tuple[str, str, str]],
asr_train_config: Optional[str],
asr_model_file: Optional[str],
- cmvn_file: Optional[str],
- beam_search_config: Optional[dict],
- lm_train_config: Optional[str],
- lm_file: Optional[str],
- model_tag: Optional[str],
- token_type: Optional[str],
- bpemodel: Optional[str],
- key_file: Optional[str],
- allow_variable_data_keys: bool,
- quantize_asr_model: Optional[bool],
- quantize_modules: Optional[List[str]],
- quantize_dtype: Optional[str],
- streaming: Optional[bool],
- simu_streaming: Optional[bool],
- chunk_size: Optional[int],
- left_context: Optional[int],
- right_context: Optional[int],
- display_partial_hypotheses: bool,
+ cmvn_file: Optional[str] = None,
+ beam_search_config: Optional[dict] = None,
+ lm_train_config: Optional[str] = None,
+ lm_file: Optional[str] = None,
+ model_tag: Optional[str] = None,
+ token_type: Optional[str] = None,
+ bpemodel: Optional[str] = None,
+ key_file: Optional[str] = None,
+ allow_variable_data_keys: bool = False,
+ quantize_asr_model: Optional[bool] = False,
+ quantize_modules: Optional[List[str]] = None,
+ quantize_dtype: Optional[str] = "float16",
+ streaming: Optional[bool] = False,
+ simu_streaming: Optional[bool] = False,
+ chunk_size: Optional[int] = 16,
+ left_context: Optional[int] = 16,
+ right_context: Optional[int] = 0,
+ display_partial_hypotheses: bool = False,
**kwargs,
) -> None:
"""Transducer model inference.
@@ -1327,6 +1327,7 @@
right_context: Number of frames in right context AFTER subsampling.
display_partial_hypotheses: Whether to display partial hypotheses.
"""
+ # assert check_argument_types()
if batch_size > 1:
raise NotImplementedError("batch decoding is not implemented")
@@ -1369,7 +1370,10 @@
left_context=left_context,
right_context=right_context,
)
- speech2text = Speech2TextTransducer(**speech2text_kwargs)
+ speech2text = Speech2TextTransducer.from_pretrained(
+ model_tag=model_tag,
+ **speech2text_kwargs,
+ )
def _forward(data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
@@ -1388,47 +1392,55 @@
key_file=key_file,
num_workers=num_workers,
)
+ asr_result_list = []
+
+ if output_dir is not None:
+ writer = DatadirWriter(output_dir)
+ else:
+ writer = None
# 4 .Start for-loop
- 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
+ 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")}
- assert len(batch.keys()) == 1
+ _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")}
+ assert len(batch.keys()) == 1
- try:
- if speech2text.streaming:
- speech = batch["speech"]
+ try:
+ if speech2text.streaming:
+ speech = batch["speech"]
- _steps = len(speech) // speech2text._ctx
- _end = 0
- for i in range(_steps):
- _end = (i + 1) * speech2text._ctx
+ _steps = len(speech) // speech2text._ctx
+ _end = 0
+ for i in range(_steps):
+ _end = (i + 1) * speech2text._ctx
- speech2text.streaming_decode(
- speech[i * speech2text._ctx: _end], is_final=False
- )
-
- final_hyps = speech2text.streaming_decode(
- speech[_end: len(speech)], is_final=True
+ speech2text.streaming_decode(
+ speech[i * speech2text._ctx: _end], is_final=False
)
- elif speech2text.simu_streaming:
- final_hyps = speech2text.simu_streaming_decode(**batch)
- else:
- final_hyps = speech2text(**batch)
- results = speech2text.hypotheses_to_results(final_hyps)
- except TooShortUttError as e:
- logging.warning(f"Utterance {keys} {e}")
- hyp = Hypothesis(score=0.0, yseq=[], dec_state=None)
- results = [[" ", ["<space>"], [2], hyp]] * nbest
+ final_hyps = speech2text.streaming_decode(
+ speech[_end: len(speech)], is_final=True
+ )
+ elif speech2text.simu_streaming:
+ final_hyps = speech2text.simu_streaming_decode(**batch)
+ else:
+ final_hyps = speech2text(**batch)
- key = keys[0]
- for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results):
+ results = speech2text.hypotheses_to_results(final_hyps)
+ except TooShortUttError as e:
+ logging.warning(f"Utterance {keys} {e}")
+ hyp = Hypothesis(score=0.0, yseq=[], dec_state=None)
+ results = [[" ", ["<space>"], [2], hyp]] * nbest
+
+ key = keys[0]
+ for n, (text, token, token_int, hyp) in zip(range(1, nbest + 1), results):
+ item = {'key': key, 'value': text}
+ asr_result_list.append(item)
+ if writer is not None:
ibest_writer = writer[f"{n}best_recog"]
ibest_writer["token"][key] = " ".join(token)
@@ -1438,6 +1450,8 @@
if text is not None:
ibest_writer["text"][key] = text
+ logging.info("decoding, utt: {}, predictions: {}".format(key, text))
+ return asr_result_list
return _forward
--
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