From 4dc3a1b011e1e72eb737417b8e0e0bec7a7e3a6e Mon Sep 17 00:00:00 2001
From: aky15 <ankeyu.aky@11.17.44.249>
Date: 星期二, 21 三月 2023 15:12:21 +0800
Subject: [PATCH] resolve conflict
---
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py | 57 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 57 insertions(+), 0 deletions(-)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py
new file mode 100644
index 0000000..c1c541b
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py
@@ -0,0 +1,57 @@
+import torch
+import torchaudio
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+from modelscope.utils.logger import get_logger
+import logging
+logger = get_logger(log_level=logging.CRITICAL)
+logger.setLevel(logging.CRITICAL)
+
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
+ model_revision='v1.0.2')
+
+waveform, sample_rate = torchaudio.load("waihu.wav")
+speech_length = waveform.shape[1]
+speech = waveform[0]
+
+cache_en = {"start_idx": 0, "pad_left": 0, "stride": 10, "pad_right": 5, "cif_hidden": None, "cif_alphas": None}
+cache_de = {"decode_fsmn": None}
+cache = {"encoder": cache_en, "decoder": cache_de}
+param_dict = {}
+param_dict["cache"] = cache
+
+first_chunk = True
+speech_buffer = speech
+speech_cache = []
+final_result = ""
+
+while len(speech_buffer) >= 960:
+ if first_chunk:
+ if len(speech_buffer) >= 14400:
+ rec_result = inference_pipeline(audio_in=speech_buffer[0:14400], param_dict=param_dict)
+ speech_buffer = speech_buffer[4800:]
+ else:
+ cache_en["stride"] = len(speech_buffer) // 960
+ cache_en["pad_right"] = 0
+ rec_result = inference_pipeline(audio_in=speech_buffer, param_dict=param_dict)
+ speech_buffer = []
+ cache_en["start_idx"] = -5
+ first_chunk = False
+ else:
+ cache_en["start_idx"] += 10
+ if len(speech_buffer) >= 4800:
+ cache_en["pad_left"] = 5
+ rec_result = inference_pipeline(audio_in=speech_buffer[:19200], param_dict=param_dict)
+ speech_buffer = speech_buffer[9600:]
+ else:
+ cache_en["stride"] = len(speech_buffer) // 960
+ cache_en["pad_right"] = 0
+ rec_result = inference_pipeline(audio_in=speech_buffer, param_dict=param_dict)
+ speech_buffer = []
+ if len(rec_result) !=0 and rec_result['text'] != "sil":
+ final_result += rec_result['text']
+ print(rec_result)
+print(final_result)
--
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