From fae856e23d45fd27d5fd55fd036e8e3fc7b24915 Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期五, 02 六月 2023 23:00:08 +0800
Subject: [PATCH] update funasr-onnx-offline
---
funasr/bin/asr_inference_launch.py | 32 +++++++++++++++++++++++++++-----
1 files changed, 27 insertions(+), 5 deletions(-)
diff --git a/funasr/bin/asr_inference_launch.py b/funasr/bin/asr_inference_launch.py
index dbbb3ed..f84212d 100644
--- a/funasr/bin/asr_inference_launch.py
+++ b/funasr/bin/asr_inference_launch.py
@@ -600,6 +600,9 @@
if 'hotword' in kwargs:
hotword_list_or_file = kwargs['hotword']
+ batch_size_token = kwargs.get("batch_size_token", 6000)
+ print("batch_size_token: ", batch_size_token)
+
if speech2text.hotword_list is None:
speech2text.hotword_list = speech2text.generate_hotwords_list(hotword_list_or_file)
@@ -642,8 +645,10 @@
assert all(isinstance(s, str) for s in keys), keys
_bs = len(next(iter(batch.values())))
assert len(keys) == _bs, f"{len(keys)} != {_bs}"
-
+ beg_vad = time.time()
vad_results = speech2vadsegment(**batch)
+ end_vad = time.time()
+ print("time cost vad: ", end_vad-beg_vad)
_, vadsegments = vad_results[0], vad_results[1][0]
speech, speech_lengths = batch["speech"], batch["speech_lengths"]
@@ -652,17 +657,29 @@
data_with_index = [(vadsegments[i], i) for i in range(n)]
sorted_data = sorted(data_with_index, key=lambda x: x[0][1] - x[0][0])
results_sorted = []
- for j, beg_idx in enumerate(range(0, n, batch_size)):
- end_idx = min(n, beg_idx + batch_size)
+ batch_size_token_ms = batch_size_token*60
+ 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:
+ continue
+ batch_size_token_ms_cum = 0
+ end_idx = j + 1
speech_j, speech_lengths_j = slice_padding_fbank(speech, speech_lengths, sorted_data[beg_idx:end_idx])
-
+ beg_idx = end_idx
batch = {"speech": speech_j, "speech_lengths": speech_lengths_j}
batch = to_device(batch, device=device)
+ print("batch: ", speech_j.shape[0])
+ beg_asr = time.time()
results = speech2text(**batch)
+ end_asr = time.time()
+ print("time cost asr: ", end_asr - beg_asr)
if len(results) < 1:
results = [["", [], [], [], [], [], []]]
results_sorted.extend(results)
+
restored_data = [0] * n
for j in range(n):
index = sorted_data[j][1]
@@ -701,7 +718,10 @@
text_postprocessed_punc = text_postprocessed
punc_id_list = []
if len(word_lists) > 0 and text2punc is not None:
+ beg_punc = time.time()
text_postprocessed_punc, punc_id_list = text2punc(word_lists, 20)
+ end_punc = time.time()
+ print("time cost punc: ", end_punc-beg_punc)
item = {'key': key, 'value': text_postprocessed_punc}
if text_postprocessed != "":
@@ -1618,6 +1638,8 @@
return inference_uniasr(**kwargs)
elif mode == "paraformer":
return inference_paraformer(**kwargs)
+ elif mode == "paraformer_fake_streaming":
+ return inference_paraformer(**kwargs)
elif mode == "paraformer_streaming":
return inference_paraformer_online(**kwargs)
elif mode.startswith("paraformer_vad"):
@@ -1900,4 +1922,4 @@
if __name__ == "__main__":
- main()
\ No newline at end of file
+ main()
--
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