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update paraformer online README

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haoneng.lhn 2023-09-25 16:55:12 +08:00
parent d7e2259ccf
commit c45b899706
2 changed files with 14 additions and 8 deletions
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11
egs_modelscope/asr/TEMPLATE/README.md
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@ -27,15 +27,18 @@ print(rec_result)
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
model_revision='v1.0.6',
model_revision='v1.0.7',
update_model=False,
mode='paraformer_streaming'
)
import soundfile
speech, sample_rate = soundfile.read("example/asr_example.wav")
chunk_size = [5, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
chunk_size = [0, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size,
"encoder_chunk_look_back": encoder_chunk_look_back, "decoder_chunk_look_back": decoder_chunk_look_back}
chunk_stride = chunk_size[1] * 960 # 600ms、480ms
# first chunk, 600ms
speech_chunk = speech[0:chunk_stride]
@ -55,7 +58,7 @@ 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-online',
model_revision='v1.0.6',
model_revision='v1.0.7',
update_model=False,
mode="paraformer_fake_streaming"
)

11
egs_modelscope/asr/TEMPLATE/README_zh.md
View File

@ -27,15 +27,18 @@ print(rec_result)
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
model_revision='v1.0.6',
model_revision='v1.0.7',
update_model=False,
mode='paraformer_streaming'
)
import soundfile
speech, sample_rate = soundfile.read("example/asr_example.wav")
chunk_size = [5, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention
decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention
param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size,
"encoder_chunk_look_back": encoder_chunk_look_back, "decoder_chunk_look_back": decoder_chunk_look_back}
chunk_stride = chunk_size[1] * 960 # 600ms、480ms
# first chunk, 600ms
speech_chunk = speech[0:chunk_stride]
@ -55,7 +58,7 @@ 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-online',
model_revision='v1.0.6',
model_revision='v1.0.7',
update_model=False,
mode="paraformer_fake_streaming"
)