From ebabb37754d184ba30ba2f016f44e5f82138c3c3 Mon Sep 17 00:00:00 2001
From: hnluo <haoneng.lhn@alibaba-inc.com>
Date: 星期五, 28 四月 2023 17:13:26 +0800
Subject: [PATCH] Merge pull request #442 from alibaba-damo-academy/dev_lhn
---
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py | 22 ++++++-----
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py | 39 +++++++++++++++++++
2 files changed, 51 insertions(+), 10 deletions(-)
diff --git a/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py
new file mode 100644
index 0000000..4fd4cdf
--- /dev/null
+++ b/egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py
@@ -0,0 +1,39 @@
+import os
+import logging
+import torch
+import soundfile
+
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from modelscope.utils.logger import get_logger
+
+logger = get_logger(log_level=logging.CRITICAL)
+logger.setLevel(logging.CRITICAL)
+
+os.environ["MODELSCOPE_CACHE"] = "./"
+inference_pipeline = pipeline(
+ task=Tasks.auto_speech_recognition,
+ model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
+ model_revision='v1.0.4'
+)
+
+model_dir = os.path.join(os.environ["MODELSCOPE_CACHE"], "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online")
+speech, sample_rate = soundfile.read(os.path.join(model_dir, "example/asr_example.wav"))
+speech_length = speech.shape[0]
+
+sample_offset = 0
+chunk_size = [5, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
+stride_size = chunk_size[1] * 960
+param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
+final_result = ""
+
+for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
+ if sample_offset + stride_size >= speech_length - 1:
+ stride_size = speech_length - sample_offset
+ param_dict["is_final"] = True
+ rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + stride_size],
+ param_dict=param_dict)
+ if len(rec_result) != 0:
+ final_result += rec_result['text'][0]
+ print(rec_result)
+print(final_result)
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
index 2eb9cc8..0066c7b 100644
--- 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
@@ -14,24 +14,26 @@
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')
+ model_revision='v1.0.4'
+)
model_dir = os.path.join(os.environ["MODELSCOPE_CACHE"], "damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online")
speech, sample_rate = soundfile.read(os.path.join(model_dir, "example/asr_example.wav"))
speech_length = speech.shape[0]
sample_offset = 0
-step = 4800 #300ms
-param_dict = {"cache": dict(), "is_final": False}
+chunk_size = [8, 8, 4] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
+stride_size = chunk_size[1] * 960
+param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
final_result = ""
-for sample_offset in range(0, speech_length, min(step, speech_length - sample_offset)):
- if sample_offset + step >= speech_length - 1:
- step = speech_length - sample_offset
+for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
+ if sample_offset + stride_size >= speech_length - 1:
+ stride_size = speech_length - sample_offset
param_dict["is_final"] = True
- rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + step],
+ rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + stride_size],
param_dict=param_dict)
- if len(rec_result) != 0 and rec_result['text'] != "sil" and rec_result['text'] != "waiting_for_more_voice":
- final_result += rec_result['text']
- print(rec_result)
+ if len(rec_result) != 0:
+ final_result += rec_result['text'][0]
+ print(rec_result)
print(final_result)
--
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