speech_recognition/FunASR
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Merge pull request #978 from alibaba-damo-academy/dev_lhn

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Dev lhn
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hnluo 2023-09-25 16:58:27 +08:00 committed by GitHub
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7 changed files with 23 additions and 14 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] #[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"
)

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"
)

2
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo.py
View File

@ -4,7 +4,7 @@ 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"
)

2
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo_online.py
View File

@ -14,7 +14,7 @@ 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='v1.0.6',
model_revision='v1.0.7',
update_model=False,
mode="paraformer_streaming"
)

7
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/demo_online_v2.py
View File

@ -24,9 +24,12 @@ speech, sample_rate = soundfile.read(os.path.join(model_dir, "example/asr_exampl
speech_length = speech.shape[0]
sample_offset = 0
chunk_size = [0, 8, 4] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
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
stride_size = chunk_size[1] * 960
param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size, "encoder_chunk_look_back": 4, "decoder_chunk_look_back": 1}
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}
final_result = ""
for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):

2
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/finetune.py
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@ -14,7 +14,7 @@ def modelscope_finetune(params):
ds_dict = MsDataset.load(params.data_path)
kwargs = dict(
model=params.model,
model_revision='v1.0.6',
model_revision='v1.0.7',
update_model=False,
data_dir=ds_dict,
dataset_type=params.dataset_type,

2
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py
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@ -11,7 +11,7 @@ def modelscope_infer(args):
model=args.model,
output_dir=args.output_dir,
batch_size=args.batch_size,
model_revision='v1.0.6',
model_revision='v1.0.7',
update_model=False,
mode="paraformer_fake_streaming",
param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt}