psistolar commited on
Commit
544159d
1 Parent(s): 1637cee

Add sentiment scoring to complete sonification.

Browse files
Files changed (1) hide show
  1. app.py +11 -2
app.py CHANGED
@@ -33,7 +33,9 @@ from huggingface_hub import hf_hub_download
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  print('Downloading model.')
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  model_cache_path = hf_hub_download(repo_id="psistolar/musicautobot-fine1", filename="model.pth")
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  # Default config options
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  config = default_config()
@@ -62,6 +64,8 @@ def sonify_text(text, sentiment):
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  note_names = [f"{letter.upper()}4" for letter in text.lower() if letter in musical_letters]
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  p = music21.stream.Part()
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  if sentiment == 'NEGATIVE':
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  # If negative, use TODO
@@ -99,13 +103,18 @@ def process_midi(MIDI_File, Text_to_Sonify, Randomness, Amount_of_Music_to_Add):
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  # create the model input object
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  if sonification:
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- sentiment = 'NEGATIVE'
 
 
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  item = sonify_text(Text_to_Sonify, sentiment)
 
 
 
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  else:
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  item = MusicItem.from_file(name, data.vocab)
 
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  # full is the prediction appended to the input
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- temp = Randomness / 100
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  pred, full = learner.predict(
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  item,
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  n_words=Amount_of_Music_to_Add,
 
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  print('Downloading model.')
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  model_cache_path = hf_hub_download(repo_id="psistolar/musicautobot-fine1", filename="model.pth")
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+ from transformers import pipeline
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+ classifier = pipeline("sentiment-analysis")
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  # Default config options
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  config = default_config()
 
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  note_names = [f"{letter.upper()}4" for letter in text.lower() if letter in musical_letters]
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+
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+
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  p = music21.stream.Part()
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  if sentiment == 'NEGATIVE':
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  # If negative, use TODO
 
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  # create the model input object
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  if sonification:
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+ sentiment_analysis = classifier(Text_to_Sonify)[0]
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+ sentiment = sentiment_analysis['label']
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+ score = sentiment_analysis['score']
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  item = sonify_text(Text_to_Sonify, sentiment)
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+ # the lower our confidence in the sentiment, the more randomness we inject
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+ score = max(0.25, score)
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+ temp = Randomness / (100 * score)
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  else:
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  item = MusicItem.from_file(name, data.vocab)
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+ temp = Randomness / 100
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  # full is the prediction appended to the input
 
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  pred, full = learner.predict(
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  item,
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  n_words=Amount_of_Music_to_Add,