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

hnluo

2023-09-25 17179822375944c91665f98f768415025b82ef4d

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

Dev lhn

 egs_modelscope/asr/TEMPLATE/README.md

@@ -27,15 +27,18 @@
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 @@
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"
)

 egs_modelscope/asr/TEMPLATE/README_zh.md

@@ -27,15 +27,18 @@
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 @@
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"
)

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

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

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

@@ -14,7 +14,7 @@
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"
)

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

@@ -24,9 +24,12 @@
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)):

 egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/finetune.py

@@ -14,7 +14,7 @@
    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,

 egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/infer.py

@@ -11,7 +11,7 @@
        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}