Create app.py

app.py

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+import streamlit as st
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+from transformers import pipeline
+import librosa
+import torch
+from spleeter.separator import Separator
+from pydub import AudioSegment
+from IPython.display import Audio
+import os
+import accelerate
+
+
+# steamlit setup
+st.set_page_config(page_title="Sentiment Analysis on Your Cantonese Song",)
+st.header("Cantonese Song Sentiment Analyzer")
+input_file = st.file_uploader("upload a song in mp3 format", type="mp3") # upload song
+if input_file is not None:
+   st.write("File uploaded successfully!")
+   st.write(input_file)
+else:
+   st.write("No file uploaded.")
+button_click = st.button("Run Analysis", type="primary")
+
+
+# load song
+#input_file = os.path.isfile("test1.mp3")
+output_file = os.path.isdir("")
+
+
+# preprocess and crop audio file
+def audio_preprocess(file_name = '/test1/vocals.wav'):
+   # separate music and vocal
+   separator = Separator('spleeter:2stems')
+   separator.separate_to_file(input_file, output_file)
+
+
+   # Crop the audio
+   start_time = 60000  # e.g. 30 seconds, 30000
+   end_time = 110000  # e.g. 40 seconds, 40000
+
+
+
+
+   audio = AudioSegment.from_file(file_name)
+   cropped_audio = audio[start_time:end_time]
+   processed_audio = cropped_audio
+   # .export('cropped_vocals.wav', format='wav') # save vocal audio file
+   return processed_audio
+
+
+# ASR transcription
+def asr_model(processed_audio):
+   # load audio file
+   y, sr = librosa.load(processed_audio, sr=16000)
+
+
+   # ASR model
+   MODEL_NAME = "RexChan/ISOM5240-whisper-small-zhhk_1"
+   processor = WhisperProcessor.from_pretrained(MODEL_NAME)
+   model = WhisperForConditionalGeneration.from_pretrained(MODEL_NAME, low_cpu_mem_usage=True)
+
+
+   model.config.forced_decoder_ids = None
+   model.config.suppress_tokens = []
+   model.config.use_cache = False
+
+
+   processed_in = processor(y, sampling_rate=sr, return_tensors="pt")
+   gout = model.generate(
+       input_features=processed_in.input_features,
+       output_scores=True, return_dict_in_generate=True
+   )
+   transcription = processor.batch_decode(gout.sequences, skip_special_tokens=True)[0]
+
+
+   # print result
+   print(f"Song lyrics = {transcription}")
+
+
+   return transcription
+
+
+
+
+# sentiment analysis
+def senti_model(transcription):
+
+
+   pipe = pipeline("text-classification", model="lxyuan/distilbert-base-multilingual-cased-sentiments-student")
+   final_result = pipe(transcription)
+   display = f"Sentiment Analysis shows that this song is {final_result[0]['label']}. Confident level of this analysis is {final_result[0]['score']*100:.1f}%."
+   print(display)
+   return display
+
+
+   # return final_result
+
+
+
+
+# main
+def main():
+
+
+   processed_audio = audio_preprocess(input_file)
+   transcription = asr_model(processed_audio)
+   final_result = senti_model(transcription)
+   st.write(final_result)
+
+
+   if st.button("Play Audio"):
+       st.audio(audio_data['audio'],
+                   format="audio/wav",
+                   start_time=0,
+                   sample_rate = audio_data['sampling_rate'])
+
+
+
+
+if __name__ == '__main__':
+   if button_click:
+       main()