From 33d3d2084403fd34b79c835d2f2fe04f6cd8f738 Mon Sep 17 00:00:00 2001
From: 游雁 <zhifu.gzf@alibaba-inc.com>
Date: 星期三, 13 九月 2023 09:33:54 +0800
Subject: [PATCH] Merge branch 'main' of github.com:alibaba-damo-academy/FunASR add
---
funasr/bin/asr_inference_launch.py | 166 ++++++++++++++++++++++++++++++++++--------------------
1 files changed, 104 insertions(+), 62 deletions(-)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index 5740f92..cdaaefc 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -236,6 +236,7 @@
timestamp_infer_config: Union[Path, str] = None,
timestamp_model_file: Union[Path, str] = None,
param_dict: dict = None,
+ decoding_ind: int = 0,
**kwargs,
):
ncpu = kwargs.get("ncpu", 1)
@@ -255,11 +256,11 @@
if param_dict is not None:
hotword_list_or_file = param_dict.get('hotword')
export_mode = param_dict.get("export_mode", False)
+ clas_scale = param_dict.get('clas_scale', 1.0)
else:
hotword_list_or_file = None
+ clas_scale = 1.0
- if kwargs.get("device", None) == "cpu":
- ngpu = 0
if ngpu >= 1 and torch.cuda.is_available():
device = "cuda"
else:
@@ -289,6 +290,8 @@
penalty=penalty,
nbest=nbest,
hotword_list_or_file=hotword_list_or_file,
+ clas_scale=clas_scale,
+ decoding_ind=decoding_ind,
)
speech2text = Speech2TextParaformer(**speech2text_kwargs)
@@ -311,6 +314,7 @@
**kwargs,
):
+ decoding_ind = None
hotword_list_or_file = None
if param_dict is not None:
hotword_list_or_file = param_dict.get('hotword')
@@ -318,6 +322,8 @@
hotword_list_or_file = kwargs['hotword']
if hotword_list_or_file is not None or 'hotword' in kwargs:
speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file)
+ if param_dict is not None and "decoding_ind" in param_dict:
+ decoding_ind = param_dict["decoding_ind"]
# 3. Build data-iterator
if data_path_and_name_and_type is None and raw_inputs is not None:
@@ -364,10 +370,11 @@
# N-best list of (text, token, token_int, hyp_object)
time_beg = time.time()
+ batch["decoding_ind"] = decoding_ind
results = speech2text(**batch)
if len(results) < 1:
hyp = Hypothesis(score=0.0, scores={}, states={}, yseq=[])
- results = [[" ", ["sil"], [2], hyp, 10, 6]] * nbest
+ results = [[" ", ["sil"], [2], hyp, 10, 6, []]] * nbest
time_end = time.time()
forward_time = time_end - time_beg
lfr_factor = results[0][-1]
@@ -408,7 +415,7 @@
ibest_writer["rtf"][key] = rtf_cur
if text is not None:
- if use_timestamp and timestamp is not None:
+ if use_timestamp and timestamp is not None and len(timestamp):
postprocessed_result = postprocess_utils.sentence_postprocess(token, timestamp)
else:
postprocessed_result = postprocess_utils.sentence_postprocess(token)
@@ -420,7 +427,7 @@
else:
text_postprocessed, word_lists = postprocessed_result[0], postprocessed_result[1]
item = {'key': key, 'value': text_postprocessed}
- if timestamp_postprocessed != "":
+ if timestamp_postprocessed != "" or len(timestamp) == 0:
item['timestamp'] = timestamp_postprocessed
asr_result_list.append(item)
finish_count += 1
@@ -436,6 +443,7 @@
logging.info(rtf_avg)
if writer is not None:
ibest_writer["rtf"]["rtf_avf"] = rtf_avg
+ torch.cuda.empty_cache()
return asr_result_list
return _forward
@@ -561,6 +569,8 @@
if 'hotword' in kwargs:
hotword_list_or_file = kwargs['hotword']
+ speech2vadsegment.vad_model.vad_opts.max_single_segment_time = kwargs.get("max_single_segment_time", 60000)
+ batch_size_token_threshold_s = kwargs.get("batch_size_token_threshold_s", int(speech2vadsegment.vad_model.vad_opts.max_single_segment_time*0.67/1000)) * 1000
batch_size_token = kwargs.get("batch_size_token", 6000)
print("batch_size_token: ", batch_size_token)
@@ -617,17 +627,33 @@
sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
results_sorted = []
+ if not len(sorted_data):
+ key = keys[0]
+ # no active segments after VAD
+ if writer is not None:
+ # Write empty results
+ ibest_writer["token"][key] = ""
+ ibest_writer["token_int"][key] = ""
+ ibest_writer["vad"][key] = ""
+ ibest_writer["text"][key] = ""
+ ibest_writer["text_with_punc"][key] = ""
+ if use_timestamp:
+ ibest_writer["time_stamp"][key] = ""
+
+ logging.info("decoding, utt: {}, empty speech".format(key))
+ continue
+
batch_size_token_ms = batch_size_token*60
if speech2text.device == "cpu":
batch_size_token_ms = 0
- batch_size_token_ms = max(batch_size_token_ms, sorted_data[0][0][1] - sorted_data[0][0][0])
+ if len(sorted_data) > 0 and len(sorted_data[0]) > 0:
+ batch_size_token_ms = max(batch_size_token_ms, sorted_data[0][0][1] - sorted_data[0][0][0])
batch_size_token_ms_cum = 0
beg_idx = 0
for j, _ in enumerate(range(0, n)):
batch_size_token_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
- if j < n - 1 and (batch_size_token_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][
- 0]) < batch_size_token_ms:
+ if j < n - 1 and (batch_size_token_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_token_ms and (sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size_token_threshold_s:
continue
batch_size_token_ms_cum = 0
end_idx = j + 1
@@ -666,7 +692,7 @@
text, token, token_int = result[0], result[1], result[2]
time_stamp = result[4] if len(result[4]) > 0 else None
- if use_timestamp and time_stamp is not None:
+ if use_timestamp and time_stamp is not None and len(time_stamp):
postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
else:
postprocessed_result = postprocess_utils.sentence_postprocess(token)
@@ -691,7 +717,7 @@
item = {'key': key, 'value': text_postprocessed_punc}
if text_postprocessed != "":
item['text_postprocessed'] = text_postprocessed
- if time_stamp_postprocessed != "":
+ if time_stamp_postprocessed != "" or len(time_stamp) == 0:
item['time_stamp'] = time_stamp_postprocessed
item['sentences'] = time_stamp_sentence(punc_id_list, time_stamp_postprocessed, text_postprocessed)
@@ -710,6 +736,7 @@
ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)
logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
+ torch.cuda.empty_cache()
return asr_result_list
return _forward
@@ -1252,27 +1279,28 @@
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,
+ full_utt: 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.
@@ -1318,7 +1346,7 @@
format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
)
- if ngpu >= 1:
+ if ngpu >= 1 and torch.cuda.is_available():
device = "cuda"
else:
device = "cpu"
@@ -1345,6 +1373,7 @@
quantize_dtype=quantize_dtype,
streaming=streaming,
simu_streaming=simu_streaming,
+ full_utt=full_utt,
chunk_size=chunk_size,
left_context=left_context,
right_context=right_context,
@@ -1368,47 +1397,57 @@
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 + speech2text._right_ctx], 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)
+ elif speech2text.full_utt:
+ final_hyps = speech2text.full_utt_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)
@@ -1418,6 +1457,8 @@
if text is not None:
ibest_writer["text"][key] = text
+ logging.info("decoding, utt: {}, predictions: {}".format(key, text))
+ return asr_result_list
return _forward
@@ -1781,6 +1822,7 @@
group.add_argument("--ngram_weight", type=float, default=0.9, help="ngram weight")
group.add_argument("--streaming", type=str2bool, default=False)
group.add_argument("--simu_streaming", type=str2bool, default=False)
+ group.add_argument("--full_utt", type=str2bool, default=False)
group.add_argument("--chunk_size", type=int, default=16)
group.add_argument("--left_context", type=int, default=16)
group.add_argument("--right_context", type=int, default=0)
--
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