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Runtime error
Update app.py
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app.py
CHANGED
@@ -184,7 +184,7 @@ var INLINE_JSON='''
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html_seeker2=''';
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update();
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</script>'''
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-
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model_name = "voidful/wav2vec2-xlsr-multilingual-56"
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model0 = pipeline(task="automatic-speech-recognition",
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model=model_name)
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@@ -196,17 +196,16 @@ model2 = pipeline(task="automatic-speech-recognition",
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model_name = "ghofrani/common8"
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model1 = pipeline(task="automatic-speech-recognition",
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model=model_name)
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import json
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def predict_fa(speech,model):
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text = model2(speech,return_timestamps="word" )
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elif model== "ghofrani/common8":
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text = model1(speech,return_timestamps="word" )
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elif model== "voidful/wav2vec2-xlsr-multilingual-56":
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text = model0(speech,return_timestamps="word" )
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text={"text": "\u0627\u06cc\u0646\u0627\u0646 \u06a9\u0631\u0627\u0644\u0627\u0644 \u0648 \u06a9\u0648\u0631\u0646\u062f \u0648 \u0644\u0632\u0627 \u0627\u0632 \u06af\u0645\u0631\u0627\u0647\u06cc \u0628\u0647 \u0631\u0627\u0647 \u0628\u0627\u0632 \u0646\u0645\u06cc\u06a9\u0631\u062f\u0646\u062f", "chunks": [{"text": "\u0627\u06cc\u0646\u0627\u0646", "timestamp": [0.0, 0.72]}, {"text": "\u06a9\u0631\u0627\u0644\u0627\u0644", "timestamp": [0.92, 1.6]}, {"text": "\u0648", "timestamp": [1.72, 1.74]}, {"text": "\u06a9\u0648\u0631\u0646\u062f", "timestamp": [1.9, 2.54]}, {"text": "\u0648", "timestamp": [2.76, 2.78]}, {"text": "\u0644\u0632\u0627", "timestamp": [2.88, 3.16]}, {"text": "\u0627\u0632", "timestamp": [3.4, 3.5]}, {"text": "\u06af\u0645\u0631\u0627\u0647\u06cc", "timestamp": [3.64, 4.3]}, {"text": "\u0628\u0647", "timestamp": [4.6, 4.68]}, {"text": "\u0631\u0627\u0647", "timestamp": [4.78, 5.12]}, {"text": "\u0628\u0627\u0632", "timestamp": [5.3, 5.58]}, {"text": "\u0646\u0645\u06cc\u06a9\u0631\u062f\u0646\u062f", "timestamp": [5.68, 7.14]}]}
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return [text['text'],json.dumps(text),html_seeker+json.dumps(text)+html_seeker2]
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html_seeker2=''';
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update();
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</script>'''
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+
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model_name = "voidful/wav2vec2-xlsr-multilingual-56"
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model0 = pipeline(task="automatic-speech-recognition",
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model=model_name)
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model_name = "ghofrani/common8"
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model1 = pipeline(task="automatic-speech-recognition",
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model=model_name)
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+
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import json
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def predict_fa(speech,model):
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if model== "SLPL/Sharif-wav2vec2":
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text = model2(speech,return_timestamps="word" )
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elif model== "ghofrani/common8":
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text = model1(speech,return_timestamps="word" )
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elif model== "voidful/wav2vec2-xlsr-multilingual-56":
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text = model0(speech,return_timestamps="word" )
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return [text['text'],json.dumps(text),html_seeker+json.dumps(text)+html_seeker2]
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