From 8689fb676d6cd28894f55c8cac43409e7bf4cd38 Mon Sep 17 00:00:00 2001
From: 凌匀 <ailsa.zly@alibaba-inc.com>
Date: 星期四, 16 二月 2023 23:08:56 +0800
Subject: [PATCH] support asr_inference_paraformer_vad_punc
---
funasr/bin/asr_inference_paraformer_vad_punc.py | 240 ++++++++++++++++++++++++++++++++---------------------------
1 files changed, 130 insertions(+), 110 deletions(-)
diff --git a/funasr/bin/asr_inference_paraformer_vad_punc.py b/funasr/bin/asr_inference_paraformer_vad_punc.py
index c4bb61b..ee36135 100644
--- a/funasr/bin/asr_inference_paraformer_vad_punc.py
+++ b/funasr/bin/asr_inference_paraformer_vad_punc.py
@@ -144,7 +144,7 @@
for scorer in scorers.values():
if isinstance(scorer, torch.nn.Module):
scorer.to(device=device, dtype=getattr(torch, dtype)).eval()
-
+
logging.info(f"Decoding device={device}, dtype={dtype}")
# 5. [Optional] Build Text converter: e.g. bpe-sym -> Text
@@ -184,12 +184,11 @@
self.encoder_downsampling_factor = 1
if asr_train_args.encoder_conf["input_layer"] == "conv2d":
self.encoder_downsampling_factor = 4
-
-
@torch.no_grad()
def __call__(
- self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None, begin_time: int = 0, end_time: int = None,
+ self, speech: Union[torch.Tensor, np.ndarray], speech_lengths: Union[torch.Tensor, np.ndarray] = None,
+ begin_time: int = 0, end_time: int = None,
):
"""Inference
@@ -215,7 +214,7 @@
else:
feats = speech
feats_len = speech_lengths
- lfr_factor = max(1, (feats.size()[-1]//80)-1)
+ lfr_factor = max(1, (feats.size()[-1] // 80) - 1)
batch = {"speech": feats, "speech_lengths": feats_len}
# a. To device
@@ -229,7 +228,8 @@
enc_len_batch_total = torch.sum(enc_len).item() * self.encoder_downsampling_factor
predictor_outs = self.asr_model.calc_predictor(enc, enc_len)
- pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], predictor_outs[2], predictor_outs[3]
+ pre_acoustic_embeds, pre_token_length, alphas, pre_peak_index = predictor_outs[0], predictor_outs[1], \
+ predictor_outs[2], predictor_outs[3]
pre_token_length = pre_token_length.round().long()
if torch.max(pre_token_length) < 1:
return []
@@ -249,7 +249,7 @@
nbest_hyps = self.beam_search(
x=x, am_scores=am_scores, maxlenratio=self.maxlenratio, minlenratio=self.minlenratio
)
-
+
nbest_hyps = nbest_hyps[: self.nbest]
else:
yseq = am_scores.argmax(dim=-1)
@@ -260,23 +260,23 @@
[self.asr_model.sos] + yseq.tolist() + [self.asr_model.eos], device=yseq.device
)
nbest_hyps = [Hypothesis(yseq=yseq, score=score)]
-
+
for hyp in nbest_hyps:
assert isinstance(hyp, (Hypothesis)), type(hyp)
-
+
# remove sos/eos and get results
last_pos = -1
if isinstance(hyp.yseq, list):
token_int = hyp.yseq[1:last_pos]
else:
token_int = hyp.yseq[1:last_pos].tolist()
-
+
# remove blank symbol id, which is assumed to be 0
token_int = list(filter(lambda x: x != 0 and x != 2, token_int))
-
+
# Change integer-ids to tokens
token = self.converter.ids2tokens(token_int)
-
+
if self.tokenizer is not None:
text = self.tokenizer.tokens2text(token)
else:
@@ -286,11 +286,13 @@
timestamp = time_stamp_lfr6_pl(us_alphas[i], us_cif_peak[i], copy.copy(token), begin_time, end_time)
results.append((text, token, token_int, timestamp, enc_len_batch_total, lfr_factor))
else:
- time_stamp = time_stamp_lfr6(alphas[i:i + 1, ], enc_len[i:i + 1, ], copy.copy(token), begin_time, end_time)
+ time_stamp = time_stamp_lfr6(alphas[i:i + 1, ], enc_len[i:i + 1, ], copy.copy(token), begin_time,
+ end_time)
results.append((text, token, token_int, time_stamp, enc_len_batch_total, lfr_factor))
# assert check_return_type(results)
return results
+
class Speech2VadSegment:
"""Speech2VadSegment class
@@ -333,6 +335,7 @@
self.device = device
self.dtype = dtype
self.frontend = frontend
+ self.batch_size = batch_size
@torch.no_grad()
def __call__(
@@ -361,56 +364,69 @@
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
- segments = self.vad_model(**batch)
+ # b. Forward Encoder streaming
+ t_offset = 0
+ step = min(feats_len, 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_send = True
+ else:
+ is_final_send = 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_send": is_final_send
+ }
+ # a. To device
+ batch = to_device(batch, device=self.device)
+ segments_part = 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
-
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,
- vad_infer_config: Optional[str] = None,
- vad_model_file: Optional[str] = None,
- vad_cmvn_file: Optional[str] = None,
- time_stamp_writer: bool = False,
- punc_infer_config: Optional[str] = None,
- punc_model_file: Optional[str] = None,
- **kwargs,
+ 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,
+ vad_infer_config: Optional[str] = None,
+ vad_model_file: Optional[str] = None,
+ vad_cmvn_file: Optional[str] = None,
+ time_stamp_writer: bool = False,
+ punc_infer_config: Optional[str] = None,
+ punc_model_file: Optional[str] = None,
+ **kwargs,
):
-
inference_pipeline = inference_modelscope(
maxlenratio=maxlenratio,
minlenratio=minlenratio,
@@ -449,63 +465,64 @@
)
return inference_pipeline(data_path_and_name_and_type, raw_inputs)
+
def inference_modelscope(
- 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,
- 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,
- output_dir: Optional[str] = None,
- dtype: str = "float32",
- seed: int = 0,
- ngram_weight: float = 0.9,
- nbest: int = 1,
- num_workers: int = 1,
- vad_infer_config: Optional[str] = None,
- vad_model_file: Optional[str] = None,
- vad_cmvn_file: Optional[str] = None,
- time_stamp_writer: bool = True,
- punc_infer_config: Optional[str] = None,
- punc_model_file: Optional[str] = None,
- outputs_dict: Optional[bool] = True,
- param_dict: dict = None,
- **kwargs,
+ 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,
+ 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,
+ output_dir: Optional[str] = None,
+ dtype: str = "float32",
+ seed: int = 0,
+ ngram_weight: float = 0.9,
+ nbest: int = 1,
+ num_workers: int = 1,
+ vad_infer_config: Optional[str] = None,
+ vad_model_file: Optional[str] = None,
+ vad_cmvn_file: Optional[str] = None,
+ time_stamp_writer: bool = True,
+ punc_infer_config: Optional[str] = None,
+ punc_model_file: Optional[str] = None,
+ outputs_dict: Optional[bool] = True,
+ param_dict: dict = 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 speech2vadsegment
speech2vadsegment_kwargs = dict(
vad_infer_config=vad_infer_config,
@@ -516,7 +533,7 @@
)
# logging.info("speech2vadsegment_kwargs: {}".format(speech2vadsegment_kwargs))
speech2vadsegment = Speech2VadSegment(**speech2vadsegment_kwargs)
-
+
# 3. Build speech2text
speech2text_kwargs = dict(
asr_train_config=asr_train_config,
@@ -539,14 +556,14 @@
)
speech2text = Speech2Text(**speech2text_kwargs)
text2punc = None
- if punc_model_file is not None:
+ if punc_model_file is not None:
text2punc = Text2Punc(punc_infer_config, punc_model_file, device=device, dtype=dtype)
if output_dir is not None:
writer = DatadirWriter(output_dir)
ibest_writer = writer[f"1best_recog"]
ibest_writer["token_list"][""] = " ".join(speech2text.asr_train_args.token_list)
-
+
def _forward(data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
output_dir_v2: Optional[str] = None,
@@ -575,7 +592,7 @@
use_timestamp = param_dict.get('use_timestamp', True)
else:
use_timestamp = True
-
+
finish_count = 0
file_count = 1
lfr_factor = 6
@@ -586,13 +603,13 @@
if output_path is not None:
writer = DatadirWriter(output_path)
ibest_writer = writer[f"1best_recog"]
-
+
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}"
-
+
vad_results = speech2vadsegment(**batch)
fbanks, vadsegments = vad_results[0], vad_results[1]
for i, segments in enumerate(vadsegments):
@@ -606,19 +623,20 @@
results = speech2text(**batch)
if len(results) < 1:
continue
-
+
result_cur = [results[0][:-2]]
if j == 0:
result_segments = result_cur
else:
- result_segments = [[result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
-
+ result_segments = [
+ [result_segments[0][i] + result_cur[0][i] for i in range(len(result_cur[0]))]]
+
key = keys[0]
result = result_segments[0]
text, token, token_int = result[0], result[1], result[2]
time_stamp = None if len(result) < 4 else result[3]
-
- if use_timestamp and time_stamp is not None:
+
+ if use_timestamp and time_stamp is not None:
postprocessed_result = postprocess_utils.sentence_postprocess(token, time_stamp)
else:
postprocessed_result = postprocess_utils.sentence_postprocess(token)
@@ -635,13 +653,13 @@
text_postprocessed_punc = text_postprocessed
if len(word_lists) > 0 and text2punc is not None:
text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
-
+
item = {'key': key, 'value': text_postprocessed_punc}
if text_postprocessed != "":
item['text_postprocessed'] = text_postprocessed
if time_stamp_postprocessed != "":
item['time_stamp'] = time_stamp_postprocessed
-
+
asr_result_list.append(item)
finish_count += 1
# asr_utils.print_progress(finish_count / file_count)
@@ -654,11 +672,13 @@
ibest_writer["text_with_punc"][key] = text_postprocessed_punc
if time_stamp_postprocessed is not None:
ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)
-
+
logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
return asr_result_list
+
return _forward
+
def get_parser():
parser = config_argparse.ArgumentParser(
description="ASR Decoding",
--
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