From 3258d2be0ad4944f0c7a359164cc25f2a32504c9 Mon Sep 17 00:00:00 2001
From: shixian.shi <shixian.shi@alibaba-inc.com>
Date: 星期二, 21 三月 2023 14:01:29 +0800
Subject: [PATCH] debugging
---
funasr/bin/asr_inference_paraformer.py | 23 ++++++++++++++++++++++-
test.py | 28 ++++++++++++++++++++++++++++
funasr/bin/asr_inference_paraformer_vad_punc.py | 1 +
3 files changed, 51 insertions(+), 1 deletions(-)
diff --git a/funasr/bin/asr_inference_paraformer.py b/funasr/bin/asr_inference_paraformer.py
index e45e575..7e159fd 100644
--- a/funasr/bin/asr_inference_paraformer.py
+++ b/funasr/bin/asr_inference_paraformer.py
@@ -43,6 +43,7 @@
from funasr.models.e2e_asr_paraformer import BiCifParaformer, ContextualParaformer
from funasr.export.models.e2e_asr_paraformer import Paraformer as Paraformer_export
from funasr.utils.timestamp_tools import ts_prediction_lfr6_standard
+from funasr.bin.tp_inference import SpeechText2Timestamp
class Speech2Text:
@@ -540,7 +541,8 @@
ngram_weight: float = 0.9,
nbest: int = 1,
num_workers: int = 1,
-
+ timestamp_infer_config: Union[Path, str] = None,
+ timestamp_model_file: Union[Path, str] = None,
**kwargs,
):
inference_pipeline = inference_modelscope(
@@ -604,6 +606,8 @@
nbest: int = 1,
num_workers: int = 1,
output_dir: Optional[str] = None,
+ timestamp_infer_config: Union[Path, str] = None,
+ timestamp_model_file: Union[Path, str] = None,
param_dict: dict = None,
**kwargs,
):
@@ -660,6 +664,15 @@
speech2text = Speech2TextExport(**speech2text_kwargs)
else:
speech2text = Speech2Text(**speech2text_kwargs)
+
+ if timestamp_model_file is not None:
+ speechtext2timestamp = SpeechText2Timestamp(
+ timestamp_cmvn_file=cmvn_file,
+ timestamp_model_file=timestamp_model_file,
+ timestamp_infer_config=timestamp_infer_config,
+ )
+ else:
+ speechtext2timestamp = None
def _forward(
data_path_and_name_and_type,
@@ -743,8 +756,16 @@
key = keys[batch_id]
for n, result in zip(range(1, nbest + 1), result):
+ # import pdb; pdb.set_trace()
text, token, token_int, hyp = result[0], result[1], result[2], result[3]
time_stamp = None if len(result) < 5 else result[4]
+ # conduct timestamp prediction here
+ if time_stamp is None and speechtext2timestamp:
+ ts_batch = {}
+ ts_batch['speech'] = batch['speech'][batch_id].squeeze(0)
+ ts_batch['speech_lengths'] = torch.tensor([batch['speech_lengths'][batch_id]])
+ ts_batch['text_lengths'] = torch.tensor([len(token)])
+ import pdb; pdb.set_trace()
# Create a directory: outdir/{n}best_recog
if writer is not None:
ibest_writer = writer[f"{n}best_recog"]
diff --git a/funasr/bin/asr_inference_paraformer_vad_punc.py b/funasr/bin/asr_inference_paraformer_vad_punc.py
index 3f57751..b0e5475 100644
--- a/funasr/bin/asr_inference_paraformer_vad_punc.py
+++ b/funasr/bin/asr_inference_paraformer_vad_punc.py
@@ -674,6 +674,7 @@
ibest_writer["time_stamp"][key] = "{}".format(time_stamp_postprocessed)
logging.info("decoding, utt: {}, predictions: {}".format(key, text_postprocessed_punc))
+ import pdb; pdb.set_trace()
return asr_result_list
return _forward
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..a2b2800
--- /dev/null
+++ b/test.py
@@ -0,0 +1,28 @@
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+'''
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
+ timestamp_model='damo/speech_timestamp_prediction-v1-16k-offline',
+ timestamp_model_revision='v1.0.3',
+ )
+
+rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
+print(rec_result)
+'''
+
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
+ vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
+ vad_model_revision="v1.1.8",
+ punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
+ punc_model_revision="v1.1.6")
+
+rec_result = inference_pipeline(audio_in='/Users/shixian/Downloads/test.wav')
+print(rec_result)
--
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