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Merge pull request #442 from alibaba-damo-academy/dev_lhn

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update streaming paraformer recipe
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hnluo 2023-04-28 17:13:26 +08:00 committed by GitHub
commit ebabb37754
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2 changed files with 51 additions and 10 deletions
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39
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py Normal file
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@ -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)

20
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py
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@ -14,24 +14,26 @@ 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')
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']
if len(rec_result) != 0:
final_result += rec_result['text'][0]
print(rec_result)
print(final_result)