From 17e8f5b889be2ad31608b5203dc5fbc5fd5c0f8a Mon Sep 17 00:00:00 2001
From: nichongjia-2007 <nichongjia@gmail.com>
Date: 星期四, 20 七月 2023 21:26:58 +0800
Subject: [PATCH] Merge branch 'main' of https://github.com/alibaba-damo-academy/FunASR
---
funasr/bin/asr_inference_launch.py | 16 +++++++---------
1 files changed, 7 insertions(+), 9 deletions(-)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index 10f8e50..36c6d76 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -370,7 +370,7 @@
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]
@@ -439,6 +439,7 @@
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
@@ -564,6 +565,7 @@
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 = kwargs.get("batch_size_token", 6000)
print("batch_size_token: ", batch_size_token)
@@ -646,8 +648,7 @@
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]) < speech2vadsegment.vad_model.vad_opts.max_single_segment_time:
continue
batch_size_token_ms_cum = 0
end_idx = j + 1
@@ -730,6 +731,7 @@
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
@@ -1327,7 +1329,6 @@
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")
@@ -1339,7 +1340,7 @@
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"
@@ -1370,10 +1371,7 @@
left_context=left_context,
right_context=right_context,
)
- speech2text = Speech2TextTransducer.from_pretrained(
- model_tag=model_tag,
- **speech2text_kwargs,
- )
+ speech2text = Speech2TextTransducer(**speech2text_kwargs)
def _forward(data_path_and_name_and_type,
raw_inputs: Union[np.ndarray, torch.Tensor] = None,
--
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