Update app.py

app.py

@@ -12,16 +12,18 @@ import nltk
 from nltk import tokenize
 nltk.download('punkt')
 import spacy_streamlit
+from datasets import load_dataset
+from transformers import pipeline
 
 
 st.title('Audio-to-Text')
 
 audio_file = st.file_uploader('Upload Audio' , type=['wav' , 'mp3','m4a'])
 
-st.title( 'Please select any of the NLP tasks')
+st.subheader( 'Please select any of the NLP tasks')
 
 
-if st.button('Trascribe Audio'):
+if st.button('Audio Transcription'):
     if audio_file is not None:
         processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
         model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")  
@@ -37,7 +39,6 @@ if st.button('Trascribe Audio'):
         st.error('please upload the audio file')
 
 
-
 if st.button('Summarize'):
         processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
         model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")  
@@ -48,10 +49,10 @@ if st.button('Summarize'):
         text = processor.batch_decode(predicted_ids)
         summary_list = [str(sentence) for sentence in text]
         result = ' '.join(summary_list)
-        summarize = pipeline("summarization")
+        summarize = pipeline("summarization" , model='facebook/bart-large-cnn')
         st.markdown(summarize(result)[0]['summary_text'])
 
-if st.button('sentiment-analysis'):
+if st.button('Sentiment Analysis'):
         processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
         model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")  
         speech, rate = librosa.load(audio_file, sr=16000)
@@ -64,7 +65,25 @@ if st.button('sentiment-analysis'):
         nlp_sa = pipeline("sentiment-analysis")
         st.markdown(nlp_sa(result))
 
-if st.button('Name'):
+
+if st.button('Audio Classification'):
+        processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
+        model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")  
+        speech, rate = librosa.load(audio_file, sr=16000)
+        input_values = processor(speech, return_tensors="pt", padding="longest", sampling_rate=rate).input_values
+        logits = model(input_values).logits
+        predicted_ids = torch.argmax(logits, dim=-1)
+        text = processor.batch_decode(predicted_ids)
+        summary_list = [str(sentence) for sentence in text]
+        result = ' '.join(summary_list)
+        dataset = load_dataset("anton-l/superb_demo", "er", split="session1")
+        classifier = pipeline("audio-classification", model="superb/wav2vec2-base-superb-er")
+        labels = classifier(dataset[0]["file"], top_k=5)
+        st.markdown(labels)
+
+
+
+if st.button('Name Entity Recognition'):
         processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
         model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")  
         speech, rate = librosa.load(audio_file, sr=16000)
@@ -93,7 +112,7 @@ source_lang = st.selectbox("Source language",['English'])
 target_lang = st.selectbox("Target language",['German','French'])
 
 
-if st.button('Translate'):
+if st.button('Translate'): 
         processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
         model = HubertForCTC.from_pretrained("facebook/hubert-large-ls960-ft")  
         speech, rate = librosa.load(audio_file, sr=16000)
@@ -106,7 +125,7 @@ if st.button('Translate'):
         prefix = 'translate '+str(source_lang)+' to '+str(target_lang)
         sentence_token =  tokenize.sent_tokenize(result)
         output = tokenizer([prefix+sentence for sentence in sentence_token], padding=True, return_tensors="pt")
-        translated_id = model1.generate(output["input_ids"], attention_mask=output['attention_mask'], max_length=100)
+        translated_id = model1.generate(output["input_ids"], attention_mask=output['attention_mask'], max_length=10000)
         translated_word = tokenizer.batch_decode(translated_id, skip_special_tokens=True)
         st.subheader('Translated Text')
         st.write(' '.join(translated_word))