From 6427c834dfd97b1f05c6659cdc7ccf010bf82fe1 Mon Sep 17 00:00:00 2001
From: 嘉渊 <wangjiaming.wjm@alibaba-inc.com>
Date: 星期一, 24 四月 2023 19:50:07 +0800
Subject: [PATCH] update
---
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py | 58 +++++++++++++++++++---------------------------------------
1 files changed, 19 insertions(+), 39 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
index c1c541b..2eb9cc8 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
@@ -1,57 +1,37 @@
+import os
+import logging
import torch
-import torchaudio
+import soundfile
+
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)
+os.environ["MODELSCOPE_CACHE"] = "./"
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]
+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]
-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 = []
+sample_offset = 0
+step = 4800 #300ms
+param_dict = {"cache": dict(), "is_final": False}
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":
+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
+ param_dict["is_final"] = True
+ rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + step],
+ 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)
print(final_result)
--
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