Create app.py

app.py

@@ -0,0 +1,66 @@
+import streamlit as st
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
+import torchaudio
+import torch
+import spacy
+from transformers import pipeline
+
+nlp_ner = spacy.load("en_core_web_sm")
+
+
+def ner(text):
+    doc = nlp_ner(text)
+    entities = [(ent.text, ent.label_) for ent in doc.ents]
+    return entities
+
+
+def main():
+    st.title("Text and Speech Analysis")
+
+    option = st.radio("Choose an option:", ("Upload Audio", "Enter Text"))
+
+    if option == "Upload Audio":
+        audio_file = st.file_uploader("Upload an audio file", type=["mp3", "wav"])
+
+        if audio_file is not None:
+            text_result = process_input(audio_file)
+            st.success("Audio processed successfully!")
+            st.text(text_result)
+            process_and_display_text(text_result)
+
+    elif option == "Enter Text":
+        text_input = st.text_area("Enter your text here:")
+
+        if st.button("Submit"):
+            if text_input:
+                process_and_display_text(text_input)
+
+
+def process_input(audio_input):
+    model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
+    tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h")
+    audio_input, _ = torchaudio.load(audio_input)
+    input_values = tokenizer(audio_input.squeeze().numpy(), return_tensors="pt").input_values
+
+    with torch.no_grad():
+        logits = model(input_values).logits
+
+    prediction_ids = torch.argmax(logits, dim=-1)
+    transcription = tokenizer.batch_decode(prediction_ids)[0]
+    return transcription
+
+
+def process_and_display_text(input_text):
+    summarization_pipeline = pipeline("summarization", model="facebook/bart-large-cnn")
+    ner_results = dict(set(ner(input_text)))
+    summary = summarization_pipeline(
+        input_text, max_length=150, min_length=50, length_penalty=2.0, num_beams=4, temperature=0.7
+    )
+    st.write("Named Entities")
+    st.table(ner_results)
+    st.write("Summary")
+    st.write(summary[0]["summary_text"])
+
+
+if __name__ == "__main__":
+    main()