chunking

app.py

@@ -11,38 +11,35 @@ fs = project.get_feature_store()
 
 dataset_api = project.get_dataset_api()
 
-dataset_api.download("Resources/titanic/images/latest_titanic.png", overwrite=True) # change link 
-# dataset_api.download("Resources/images/deadImage.png", overwrite=True) # change link 
-
+dataset_api.download("Resources/titanic/images/latest_titanic.png", overwrite=True)  # change link
 
 # pipe = pipeline(model="fimster/whisper-small-sv-SE") # change model
 # pipe = pipeline(model="ayberkuckun/whisper-small-sv-SE")
-pipe = pipeline(model="openai/whisper-small")
+pipe = pipeline(model="openai/whisper-small", task="automatic-speech-recognition", chunk_length_s=30)
 
 
 def transcribe(url):
     selected_video = YouTube(url)
 
     try:
-        audio = selected_video.streams.filter(only_audio=True, file_extension='mp4')[0]
+        audio = selected_video.streams.filter(only_audio=True)[0]
     except:
         raise Exception("Can't find an mp4 audio.")
 
-    audio.download(filename="audio.mp4")
+    audio.download(filename="audio.mp3")
 
-    speech_array, sr = librosa.load("audio.mp4", sr=16000)
+    speech_array, _ = librosa.load("audio.mp3", sr=16000)
 
-    output = pipe(speech_array[:sr*30])
+    output = pipe(speech_array)
 
     return output["text"]
 
 
 iface = gr.Interface(
-    fn=transcribe, 
+    fn=transcribe,
     inputs=gr.Textbox("https://www.youtube.com/watch?v=n9g12Xm9UJM", label="Paste a YouTube video URL"),
-    outputs=[gr.Textbox(label="Only the first approximately 30 sec will be transcripted"),
-            gr.Image("latest_titanic.png", label="Model Scores")],
-              # gr.Image("deadImage.png", elem_id="predicted-img", label="Model Scores")],
+    outputs=[gr.Textbox(label="Transcription"),
+             gr.Image("latest_titanic.png", label="Model Scores")],
     title="Whisper Small Swedish",
     description="Realtime demo for Swedish speech recognition using a fine-tuned Whisper small model.",
     allow_flagging="never"