debugging

.gitignore

@@ -0,0 +1 @@
+temp.wav

app.py

@@ -1,33 +1,33 @@
 import gradio as gr
-# from transformers import Wav2Vec2Processor
-# from transformers import AutoModelForCTC
-# from conversationalnlp.models.wav2vec2 import Wav2Vec2Predict
-# from transformers import Wav2Vec2Processor
-# from transformers import AutoModelForCTC
-# from conversationalnlp.models.wav2vec2 import ModelLoader
-# from conversationalnlp.utils import *
-# import soundfile as sf
-# import os
+from transformers import Wav2Vec2Processor
+from transformers import AutoModelForCTC
+from conversationalnlp.models.wav2vec2 import Wav2Vec2Predict
+from transformers import Wav2Vec2Processor
+from transformers import AutoModelForCTC
+from conversationalnlp.models.wav2vec2 import ModelLoader
+from conversationalnlp.utils import *
+import soundfile as sf
+import os
 
 """
 run gradio with 
 >>python app.py
 """
 
-# audiosavepath = r"C:\Users\codenamewei\Documents\nlp-meeting-data\gradio-inference"
+audiosavepath = os.getcwd()
 
-# pretrained_model = "codenamewei/speech-to-text"
+pretrained_model = "codenamewei/speech-to-text"
 
-# processor = Wav2Vec2Processor.from_pretrained(
-#     pretrained_model, use_auth_token=True)
+processor = Wav2Vec2Processor.from_pretrained(
+    pretrained_model, use_auth_token=True)
 
-# model = AutoModelForCTC.from_pretrained(
-#     pretrained_model,
-#     use_auth_token=True)
+model = AutoModelForCTC.from_pretrained(
+    pretrained_model,
+    use_auth_token=True)
 
-# modelloader = ModelLoader(model, processor)
+modelloader = ModelLoader(model, processor)
 
-# predictor = Wav2Vec2Predict(modelloader)
+predictor = Wav2Vec2Predict(modelloader)
 
 
 def greet(audioarray):
@@ -38,15 +38,15 @@ def greet(audioarray):
         -6934528], dtype=int32))
     <class 'tuple'>
     """
-    # audioabspath = os.path.join(
-    #     audiosavepath, customdatetime.getstringdatetime() + ".wav")
-    # # WORKAROUND: Save to file and reread to get the array shape needed for prediction
-    # sf.write(audioabspath, audioarray[1], audioarray[0])
-
-    # print(f"Audio at path {audioabspath}")
-    # predictiontexts = predictor.predictfiles([audioabspath])
-    # outputtext = predictiontexts["predicted_text"][-1] + \
-    #     "\n" + predictiontexts["corrected_text"][-1]
+    audioabspath = os.path.join(audiosavepath, "temp.wav")
+
+    # WORKAROUND: Save to file and reread to get the array shape needed for prediction
+    sf.write(audioabspath, audioarray[1], audioarray[0])
+
+    print(f"Audio at path {audioabspath}")
+    predictiontexts = predictor.predictfiles([audioabspath])
+    outputtext = predictiontexts["predicted_text"][-1] + \
+        "\n" + predictiontexts["corrected_text"][-1]
 
     return outputtext
 

requirements.txt

@@ -1 +1,4 @@
-gradio
+gradio
+conversationalnlp
+transformers
+SoundFile