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https://github.com/modelscope/FunASR
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funasr1.0 modelscope
This commit is contained in:
游雁
parent
f14f9f8d15
commit
d8b586e02c
@ -5,10 +5,13 @@
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from funasr import AutoModel
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model = AutoModel(model="../modelscope_models/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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vad_model="../modelscope_models/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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punc_model="../modelscope_models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
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model = AutoModel(model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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model_revision="v2.0.0",
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vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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vad_model_revision="v2.0.0",
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punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
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punc_model_revision="v2.0.0",
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)
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res = model(input="../modelscope_models/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav", batch_size_s=300, batch_size_threshold_s=60)
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res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav", batch_size_s=300, batch_size_threshold_s=60)
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print(res)
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@ -1,27 +1,21 @@
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# download model
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local_path_root=../modelscope_models
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mkdir -p ${local_path_root}
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local_path=${local_path_root}/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch
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git clone https://www.modelscope.cn/damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
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local_path_vad=${local_path_root}/speech_fsmn_vad_zh-cn-16k-common-pytorch
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git clone https://www.modelscope.cn/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch.git ${local_path_vad}
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local_path_punc=${local_path_root}/punc_ct-transformer_zh-cn-common-vocab272727-pytorch
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git clone https://www.modelscope.cn/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch.git ${local_path_punc}
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model="damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
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model_revision="v2.0.0"
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vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
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vad_model_revision="v2.0.0"
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punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
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punc_model_revision="v2.0.0"
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python funasr/bin/inference.py \
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+model="${local_path}" \
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+vad_model="${local_path_vad}" \
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+punc_model="${local_path_punc}" \
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+input="${local_path}/example/asr_example.wav" \
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+model=${model} \
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+model_revision=${model_revision} \
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+vad_model=${vad_model} \
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+vad_model_revision=${vad_model_revision} \
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+punc_model=${punc_model} \
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+punc_model_revision=${punc_model_revision} \
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+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_vad_punc_example.wav" \
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+output_dir="./outputs/debug" \
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+device="cpu" \
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+batch_size_s=300 \
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+batch_size_threshold_s=60 \
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+debug="true" \
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+"hotword='达摩院 魔搭'"
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+batch_size_threshold_s=60
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@ -5,8 +5,8 @@
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from funasr import AutoModel
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model = AutoModel(model="../modelscope_models/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404")
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model = AutoModel(model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", model_revision="v2.0.0")
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res = model(input="../modelscope_models/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/example/asr_example.wav",
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res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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hotword='达摩院 魔搭')
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print(res)
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@ -1,14 +1,11 @@
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# download model
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local_path_root=../modelscope_models
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mkdir -p ${local_path_root}
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local_path=${local_path_root}/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404
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git clone https://www.modelscope.cn/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404.git ${local_path}
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model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404"
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model_revision="v2.0.0"
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python funasr/bin/inference.py \
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+model="${local_path}" \
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+input="${local_path}/example/asr_example.wav" \
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+model=${model} \
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+model_revision=${model_revision} \
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+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
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+output_dir="./outputs/debug" \
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+device="cpu" \
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+"hotword='达摩院 魔搭'"
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@ -5,7 +5,7 @@
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from funasr import AutoModel
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model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch")
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model = AutoModel(model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", model_revision="v2.0.0")
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res = model(input="/Users/zhifu/Downloads/modelscope_models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/example/punc_example.txt")
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res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt")
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print(res)
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@ -1,13 +1,10 @@
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# download model
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local_path_root=../modelscope_models
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mkdir -p ${local_path_root}
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local_path=${local_path_root}/punc_ct-transformer_zh-cn-common-vocab272727-pytorch
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git clone https://www.modelscope.cn/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch.git ${local_path}
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model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
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model_revision="v2.0.0"
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python funasr/bin/inference.py \
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+model="${local_path}" \
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+input="${local_path}/example/punc_example.txt" \
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+model=${model} \
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+model_revision=${model_revision} \
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+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_text/punc_example.txt" \
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+output_dir="./outputs/debug" \
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+device="cpu"
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@ -5,7 +5,7 @@
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from funasr import AutoModel
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model = AutoModel(model="../modelscope_models/emotion2vec_base")
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model = AutoModel(model="damo/emotion2vec_base", model_revision="v2.0.0")
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res = model(input="../modelscope_models/emotion2vec_base/example/test.wav", output_dir="./outputs")
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res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", output_dir="./outputs")
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print(res)
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# download model
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local_path_root=../modelscope_models
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mkdir -p ${local_path_root}
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local_path=${local_path_root}/emotion2vec_base
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git clone https://www.modelscope.cn/damo/emotion2vec_base.git ${local_path}
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#local_path=/Users/zhifu/Downloads/modelscope_models/emotion2vec_base
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model="damo/emotion2vec_base"
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model_revision="v2.0.0"
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python funasr/bin/inference.py \
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+model="${local_path}" \
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+input="${local_path}/example/test.wav" \
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+model=${model} \
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+model_revision=${model_revision} \
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+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
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+output_dir="./outputs/debug" \
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+device="cpu" \
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+debug=true
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@ -5,7 +5,7 @@
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from funasr import AutoModel
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model = AutoModel(model="../modelscope_models/speech_fsmn_vad_zh-cn-16k-common-pytorch")
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model = AutoModel(model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.0")
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res = model(input="../modelscope_models/speech_fsmn_vad_zh-cn-16k-common-pytorch/example/vad_example.wav")
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res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav")
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print(res)
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# download model
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local_path_root=../modelscope_models
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mkdir -p ${local_path_root}
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local_path=${local_path_root}/speech_fsmn_vad_zh-cn-16k-common-pytorch
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git clone https://www.modelscope.cn/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch.git ${local_path}
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model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
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model_revision="v2.0.0"
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python funasr/bin/inference.py \
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+model="${local_path}" \
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+input="${local_path}/example/vad_example.wav" \
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+model=${model} \
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+model_revision=${model_revision} \
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+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav" \
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+output_dir="./outputs/debug" \
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+device="cpu" \
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@ -5,9 +5,9 @@
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from funasr import AutoModel
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model = AutoModel(model="../modelscope_models/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
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model = AutoModel(model="damo/speech_timestamp_prediction-v1-16k-offline", model_revision="v2.0.0")
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res = model(input=("../modelscope_models/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav",
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res = model(input=("https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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"欢迎大家来到魔搭社区进行体验"),
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data_type=("sound", "text"),
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batch_size=2,
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# download model
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local_path_root=../modelscope_models
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mkdir -p ${local_path_root}
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local_path=${local_path_root}/speech_timestamp_prediction-v1-16k-offline
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git clone https://www.modelscope.cn/damo/speech_timestamp_prediction-v1-16k-offline.git ${local_path}
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model="damo/speech_timestamp_prediction-v1-16k-offline"
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model_revision="v2.0.0"
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python funasr/bin/inference.py \
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+model="${local_path}" \
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+input='["../modelscope_models/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav", "欢迎大家来到魔搭社区进行体验"]' \
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+model=${model} \
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+model_revision=${model_revision} \
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+input='["https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", "欢迎大家来到魔搭社区进行体验"]' \
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+data_type='["sound", "text"]' \
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+output_dir="../outputs/debug" \
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+device="cpu" \
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from funasr import AutoModel
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model = AutoModel(model="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
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model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revison="v2.0.0")
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res = model(input="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav")
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res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav")
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print(res)
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from funasr import AutoFrontend
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frontend = AutoFrontend(model="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
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frontend = AutoFrontend(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revison="v2.0.0")
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fbanks = frontend(input="../modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav", batch_size=2)
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fbanks = frontend(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav", batch_size=2)
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for batch_idx, fbank_dict in enumerate(fbanks):
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res = model(**fbank_dict)
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# download model
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local_path_root=../modelscope_models
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mkdir -p ${local_path_root}
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local_path=${local_path_root}/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
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git clone https://www.modelscope.cn/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
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model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
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model_revision="v2.0.0"
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python funasr/bin/inference.py \
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+model="${local_path}" \
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+input="${local_path}/example/asr_example.wav" \
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+model=${model} \
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+model_revision=${model_revision} \
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+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
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+output_dir="./outputs/debug" \
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+device="cpu" \
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@ -5,12 +5,14 @@
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from funasr import AutoModel
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model = AutoModel(model="../modelscope_models/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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model = AutoModel(model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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model_revision="v2.0.0",
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vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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vad_model_revision="v2.0.0",
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punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch",
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punc_model_revision="v2.0.0",
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)
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#vad_model="../modelscope_models/speech_fsmn_vad_zh-cn-16k-common-pytorch",
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#punc_model="../modelscope_models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
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res = model(input="../modelscope_models/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/example/asr_example.wav",
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res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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hotword='达摩院 磨搭')
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print(res)
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# download model
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local_path_root=../modelscope_models
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mkdir -p ${local_path_root}
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local_path=${local_path_root}/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch
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git clone https://www.modelscope.cn/damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch.git ${local_path}
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#local_path_vad=${local_path_root}/speech_fsmn_vad_zh-cn-16k-common-pytorch
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#git clone https://www.modelscope.cn/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch.git ${local_path_vad}
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#local_path_punc=${local_path_root}/punc_ct-transformer_zh-cn-common-vocab272727-pytorch
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#git clone https://www.modelscope.cn/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch.git ${local_path_punc}
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model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
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model_revision="v2.0.0"
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vad_model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
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vad_model_revision="v2.0.0"
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punc_model="damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
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punc_model_revision="v2.0.0"
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python funasr/bin/inference.py \
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+model="${local_path}" \
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+input="${local_path}/example/asr_example.wav" \
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+model=${model} \
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+model_revision=${model_revision} \
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+vad_model=${vad_model} \
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+vad_model_revision=${vad_model_revision} \
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+punc_model=${punc_model} \
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+punc_model_revision=${punc_model_revision} \
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+input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav" \
|
||||
+output_dir="./outputs/debug" \
|
||||
+device="cpu" \
|
||||
+"hotword='达摩院 魔搭'"
|
||||
|
||||
#+vad_model="${local_path_vad}" \
|
||||
#+punc_model="${local_path_punc}" \
|
||||
@ -20,6 +20,7 @@ from funasr.register import tables
|
||||
from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank
|
||||
from funasr.utils.vad_utils import slice_padding_audio_samples
|
||||
from funasr.utils.timestamp_tools import time_stamp_sentence
|
||||
from funasr.download.file import download_from_url
|
||||
|
||||
def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
|
||||
"""
|
||||
@ -35,7 +36,8 @@ def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
|
||||
filelist = [".scp", ".txt", ".json", ".jsonl"]
|
||||
|
||||
chars = string.ascii_letters + string.digits
|
||||
|
||||
if isinstance(data_in, str) and data_in.startswith('http'): # url
|
||||
data_in = download_from_url(data_in)
|
||||
if isinstance(data_in, str) and os.path.exists(data_in): # wav_path; filelist: wav.scp, file.jsonl;text.txt;
|
||||
_, file_extension = os.path.splitext(data_in)
|
||||
file_extension = file_extension.lower()
|
||||
@ -59,7 +61,7 @@ def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
|
||||
data_list = [data_in]
|
||||
key_list = [key]
|
||||
elif isinstance(data_in, (list, tuple)):
|
||||
if data_type is not None and isinstance(data_type, (list, tuple)):
|
||||
if data_type is not None and isinstance(data_type, (list, tuple)): # mutiple inputs
|
||||
data_list_tmp = []
|
||||
for data_in_i, data_type_i in zip(data_in, data_type):
|
||||
key_list, data_list_i = prepare_data_iterator(data_in=data_in_i, data_type=data_type_i)
|
||||
@ -68,7 +70,7 @@ def prepare_data_iterator(data_in, input_len=None, data_type=None, key=None):
|
||||
for item in zip(*data_list_tmp):
|
||||
data_list.append(item)
|
||||
else:
|
||||
# [audio sample point, fbank]
|
||||
# [audio sample point, fbank, text]
|
||||
data_list = data_in
|
||||
key_list = ["rand_key_" + ''.join(random.choice(chars) for _ in range(13)) for _ in range(len(data_in))]
|
||||
else: # raw text; audio sample point, fbank; bytes
|
||||
@ -198,13 +200,12 @@ class AutoModel:
|
||||
kwargs = self.kwargs if kwargs is None else kwargs
|
||||
kwargs.update(cfg)
|
||||
model = self.model if model is None else model
|
||||
|
||||
data_type = kwargs.get("data_type", "sound")
|
||||
|
||||
batch_size = kwargs.get("batch_size", 1)
|
||||
# if kwargs.get("device", "cpu") == "cpu":
|
||||
# batch_size = 1
|
||||
|
||||
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=data_type, key=key)
|
||||
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None), key=key)
|
||||
|
||||
speed_stats = {}
|
||||
asr_result_list = []
|
||||
@ -268,8 +269,8 @@ class AutoModel:
|
||||
batch_size = int(kwargs.get("batch_size_s", 300))*1000
|
||||
batch_size_threshold_ms = int(kwargs.get("batch_size_threshold_s", 60))*1000
|
||||
kwargs["batch_size"] = batch_size
|
||||
data_type = kwargs.get("data_type", "sound")
|
||||
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=data_type)
|
||||
|
||||
key_list, data_list = prepare_data_iterator(input, input_len=input_len, data_type=kwargs.get("data_type", None))
|
||||
results_ret_list = []
|
||||
time_speech_total_all_samples = 0.0
|
||||
|
||||
|
||||
@ -21,8 +21,6 @@ def download_fr_ms(**kwargs):
|
||||
|
||||
config = os.path.join(model_or_path, "config.yaml")
|
||||
if os.path.exists(config) and os.path.exists(os.path.join(model_or_path, "model.pb")):
|
||||
# config = os.path.join(model_or_path, "config.yaml")
|
||||
# assert os.path.exists(config), "{} is not exist!".format(config)
|
||||
cfg = OmegaConf.load(config)
|
||||
kwargs = OmegaConf.merge(cfg, kwargs)
|
||||
init_param = os.path.join(model_or_path, "model.pb")
|
||||
@ -42,10 +40,10 @@ def download_fr_ms(**kwargs):
|
||||
assert os.path.exists(os.path.join(model_or_path, "configuration.json"))
|
||||
with open(os.path.join(model_or_path, "configuration.json"), 'r', encoding='utf-8') as f:
|
||||
conf_json = json.load(f)
|
||||
config = os.path.join(model_or_path, conf_json["model"]["model_config"])
|
||||
config = os.path.join(model_or_path, conf_json["model_config"])
|
||||
cfg = OmegaConf.load(config)
|
||||
kwargs = OmegaConf.merge(cfg, kwargs)
|
||||
init_param = os.path.join(model_or_path, conf_json["model"]["model_name"])
|
||||
init_param = os.path.join(model_or_path, conf_json["model_file"])
|
||||
kwargs["init_param"] = init_param
|
||||
kwargs["model"] = cfg["model"]
|
||||
return OmegaConf.to_container(kwargs, resolve=True)
|
||||
|
||||
@ -8,7 +8,23 @@ from pathlib import Path
|
||||
from typing import Generator, Union
|
||||
|
||||
import requests
|
||||
from urllib.parse import urlparse
|
||||
|
||||
def download_from_url(url):
|
||||
result = urlparse(url)
|
||||
file_path = None
|
||||
if result.scheme is not None and len(result.scheme) > 0:
|
||||
storage = HTTPStorage()
|
||||
# bytes
|
||||
data = storage.read(url)
|
||||
work_dir = tempfile.TemporaryDirectory().name
|
||||
if not os.path.exists(work_dir):
|
||||
os.makedirs(work_dir)
|
||||
file_path = os.path.join(work_dir, os.path.basename(url))
|
||||
with open(file_path, 'wb') as fb:
|
||||
fb.write(data)
|
||||
assert file_path is not None, f"failed to download: {url}"
|
||||
return file_path
|
||||
|
||||
class Storage(metaclass=ABCMeta):
|
||||
"""Abstract class of storage.
|
||||
|
||||
@ -11,6 +11,7 @@ from funasr.train_utils.device_funcs import to_device
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from funasr.models.ct_transformer.utils import split_to_mini_sentence, split_words
|
||||
from funasr.utils.load_utils import load_audio_text_image_video
|
||||
|
||||
from funasr.register import tables
|
||||
|
||||
@ -219,10 +220,10 @@ class CTTransformer(nn.Module):
|
||||
**kwargs,
|
||||
):
|
||||
assert len(data_in) == 1
|
||||
|
||||
text = load_audio_text_image_video(data_in, data_type=kwargs.get("kwargs", "text"))[0]
|
||||
vad_indexes = kwargs.get("vad_indexes", None)
|
||||
text = data_in[0]
|
||||
text_lengths = data_lengths[0] if data_lengths is not None else None
|
||||
# text = data_in[0]
|
||||
# text_lengths = data_lengths[0] if data_lengths is not None else None
|
||||
split_size = kwargs.get("split_size", 20)
|
||||
|
||||
tokens = split_words(text)
|
||||
|
||||
@ -188,9 +188,12 @@ class MonotonicAligner(torch.nn.Module):
|
||||
text_postprocessed, time_stamp_postprocessed, _ = postprocess_utils.sentence_postprocess(token, timestamp)
|
||||
result_i = {"key": key[i], "text": text_postprocessed,
|
||||
"timestamp": time_stamp_postprocessed,
|
||||
}
|
||||
# ibest_writer["token"][key[i]] = " ".join(token)
|
||||
ibest_writer["timestamp_list"][key[i]] = time_stamp_postprocessed
|
||||
ibest_writer["timestamp_str"][key[i]] = timestamp_str
|
||||
}
|
||||
results.append(result_i)
|
||||
|
||||
if ibest_writer:
|
||||
# ibest_writer["token"][key[i]] = " ".join(token)
|
||||
ibest_writer["timestamp_list"][key[i]] = time_stamp_postprocessed
|
||||
ibest_writer["timestamp_str"][key[i]] = timestamp_str
|
||||
|
||||
return results, meta_data
|
||||
@ -10,29 +10,13 @@ import time
|
||||
import logging
|
||||
from torch.nn.utils.rnn import pad_sequence
|
||||
try:
|
||||
from urllib.parse import urlparse
|
||||
from funasr.download.file import HTTPStorage
|
||||
import tempfile
|
||||
from funasr.download.file import download_from_url
|
||||
except:
|
||||
print("urllib is not installed, if you infer from url, please install it first.")
|
||||
# def load_audio(data_or_path_or_list, fs: int=16000, audio_fs: int=16000):
|
||||
#
|
||||
# if isinstance(data_or_path_or_list, (list, tuple)):
|
||||
# return [load_audio(audio, fs=fs, audio_fs=audio_fs) for audio in data_or_path_or_list]
|
||||
#
|
||||
# if isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list):
|
||||
# data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list)
|
||||
# data_or_path_or_list = data_or_path_or_list[0, :]
|
||||
# elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point
|
||||
# data_or_path_or_list = np.squeeze(data_or_path_or_list) #[n_samples,]
|
||||
#
|
||||
# if audio_fs != fs:
|
||||
# resampler = torchaudio.transforms.Resample(audio_fs, fs)
|
||||
# data_or_path_or_list = resampler(data_or_path_or_list[None, :])[0, :]
|
||||
# return data_or_path_or_list
|
||||
|
||||
|
||||
def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs: int = 16000, data_type=None, tokenizer=None):
|
||||
|
||||
def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs: int = 16000, data_type="sound", tokenizer=None):
|
||||
if isinstance(data_or_path_or_list, (list, tuple)):
|
||||
if data_type is not None and isinstance(data_type, (list, tuple)):
|
||||
|
||||
@ -47,16 +31,22 @@ def load_audio_text_image_video(data_or_path_or_list, fs: int = 16000, audio_fs:
|
||||
|
||||
return data_or_path_or_list_ret
|
||||
else:
|
||||
return [load_audio_text_image_video(audio, fs=fs, audio_fs=audio_fs) for audio in data_or_path_or_list]
|
||||
return [load_audio_text_image_video(audio, fs=fs, audio_fs=audio_fs, data_type=data_type) for audio in data_or_path_or_list]
|
||||
if isinstance(data_or_path_or_list, str) and data_or_path_or_list.startswith('http'):
|
||||
data_or_path_or_list = download_from_url(data_or_path_or_list)
|
||||
if isinstance(data_or_path_or_list, str) and os.path.exists(data_or_path_or_list):
|
||||
data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list)
|
||||
data_or_path_or_list = data_or_path_or_list[0, :]
|
||||
if data_type is None or data_type == "sound":
|
||||
data_or_path_or_list, audio_fs = torchaudio.load(data_or_path_or_list)
|
||||
data_or_path_or_list = data_or_path_or_list[0, :]
|
||||
# elif data_type == "text" and tokenizer is not None:
|
||||
# data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
|
||||
elif isinstance(data_or_path_or_list, str) and data_type == "text" and tokenizer is not None:
|
||||
data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
|
||||
elif isinstance(data_or_path_or_list, np.ndarray): # audio sample point
|
||||
data_or_path_or_list = np.squeeze(data_or_path_or_list) # [n_samples,]
|
||||
elif isinstance(data_or_path_or_list, str) and data_type is not None and data_type == "text" and tokenizer is not None:
|
||||
data_or_path_or_list = tokenizer.encode(data_or_path_or_list)
|
||||
else:
|
||||
pass
|
||||
# print(f"unsupport data type: {data_or_path_or_list}, return raw data")
|
||||
|
||||
if audio_fs != fs and data_type != "text":
|
||||
resampler = torchaudio.transforms.Resample(audio_fs, fs)
|
||||
@ -107,19 +97,3 @@ def extract_fbank(data, data_len = None, data_type: str="sound", frontend=None):
|
||||
data_len = torch.tensor([data_len])
|
||||
return data.to(torch.float32), data_len.to(torch.int32)
|
||||
|
||||
def download_from_url(url):
|
||||
|
||||
result = urlparse(url)
|
||||
file_path = None
|
||||
if result.scheme is not None and len(result.scheme) > 0:
|
||||
storage = HTTPStorage()
|
||||
# bytes
|
||||
data = storage.read(url)
|
||||
work_dir = tempfile.TemporaryDirectory().name
|
||||
if not os.path.exists(work_dir):
|
||||
os.makedirs(work_dir)
|
||||
file_path = os.path.join(work_dir, os.path.basename(url))
|
||||
with open(file_path, 'wb') as fb:
|
||||
fb.write(data)
|
||||
assert file_path is not None, f"failed to download: {url}"
|
||||
return file_path
|
||||
Loading…
Reference in New Issue
Block a user