Changed App UI, added transcription and translation buttons

app.py

@@ -53,42 +53,91 @@ def detect_language(audio_file):
     print(f"Detected language: {max(probs[0], key=probs[0].get)}")
     return max(probs[0], key=probs[0].get)
     
-if submit_button and uploaded_files is not None:
-    st.write("Files uploaded successfully!")
+# if submit_button and uploaded_files is not None:
+#     st.write("Files uploaded successfully!")
 
-    for uploaded_file in uploaded_files:
-        # Display file name and audio player
+#     for uploaded_file in uploaded_files:
+#         # Display file name and audio player
 
-        st.write(f"**File name**: {uploaded_file.name}")
-        st.audio(uploaded_file, format=uploaded_file.type)
+#         st.write(f"**File name**: {uploaded_file.name}")
+#         st.audio(uploaded_file, format=uploaded_file.type)
 
-        # Transcription section
-        st.write("**Transcription**:")
+#         # Transcription section
+#         st.write("**Transcription**:")
 
-        # Read the uploaded file data
-        waveform, sampling_rate = ta.load(uploaded_file.getvalue())
-        resampled_inp = ta.functional.resample(waveform, orig_freq=sampling_rate, new_freq=SAMPLING_RATE)
+#         # Read the uploaded file data
+#         waveform, sampling_rate = ta.load(uploaded_file.getvalue())
+#         resampled_inp = ta.functional.resample(waveform, orig_freq=sampling_rate, new_freq=SAMPLING_RATE)
 
-        input_features = processor(resampled_inp[0], sampling_rate=16000, return_tensors='pt').input_features
+#         input_features = processor(resampled_inp[0], sampling_rate=16000, return_tensors='pt').input_features
 
-        if task == "translate":
+#         if task == "translate":
             
-            # Detect Language 
-            lang = detect_language(input_features)
-            with open('languages.pkl', 'rb') as f:
-                lang_dict = pickle.load(f)
-            detected_language = lang_dict[lang]
-
-            # Set decoder & Predict translation
-            forced_decoder_ids = processor.get_decoder_prompt_ids(language=detected_language, task="translate")
-            predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
-        else:
-            predicted_ids = model.generate(input_features)
-        # decode token ids to text
-        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
-        for i in range(len(transcription)):
-            st.write(transcription[i])
-        # print(waveform, sampling_rate)
-        # Run transcription function and display
-        # import pdb;pdb.set_trace()
-        # st.write(audio_data.getvalue())
+#             # Detect Language 
+#             lang = detect_language(input_features)
+#             with open('languages.pkl', 'rb') as f:
+#                 lang_dict = pickle.load(f)
+#             detected_language = lang_dict[lang]
+
+#             # Set decoder & Predict translation
+#             forced_decoder_ids = processor.get_decoder_prompt_ids(language=detected_language, task="translate")
+#             predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
+#         else:
+#             predicted_ids = model.generate(input_features)
+#         # decode token ids to text
+#         transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+#         for i in range(len(transcription)):
+#             st.write(transcription[i])
+#         # print(waveform, sampling_rate)
+#         # Run transcription function and display
+#         # import pdb;pdb.set_trace()
+#         # st.write(audio_data.getvalue())
+
+
+
+if submit_button and uploaded_files is not None:
+    # Initialize a list to store detected languages
+    detected_languages = []
+
+    for uploaded_file in uploaded_files:
+        # Read the uploaded file data
+        waveform, sampling_rate = ta.load(BytesIO(uploaded_file.read()))
+
+        # Resample if necessary
+        if sampling_rate != SAMPLING_RATE:
+            waveform = ta.functional.resample(waveform, orig_freq=sampling_rate, new_freq=SAMPLING_RATE)
+
+        # Detect language
+        detected_language = detect_language(waveform, SAMPLING_RATE)
+        detected_languages.append(detected_language)
+
+    # Display each uploaded file with its detected language and an audio player
+    for i, uploaded_file in enumerate(uploaded_files):
+        col1, col2 = st.columns([1, 3])  # Two columns, one for the player, one for the buttons
+
+        with col1:
+            st.write(f"**File name**: {uploaded_file.name}")
+            st.audio(BytesIO(uploaded_file.getvalue()), format=uploaded_file.type)
+            st.write(f"**Detected Language**: {detected_languages[i]}")
+
+        with col2:
+            # Add Transcription and Translation buttons
+            if st.button(f"Transcribe {uploaded_file.name}"):
+                # Transcription process
+                input_features = processor(waveform[0], sampling_rate=SAMPLING_RATE, return_tensors='pt').input_features
+                predicted_ids = model.generate(input_features)
+                transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+                for line in transcription:
+                    st.write(line)
+
+            if st.button(f"Translate {uploaded_file.name}"):
+                # Translation process
+                with open('languages.pkl', 'rb') as f:
+                    lang_dict = pickle.load(f)
+                detected_language_name = lang_dict[detected_languages[i]]
+
+                forced_decoder_ids = processor.get_decoder_prompt_ids(language=detected_language_name, task="translate")
+                predicted_ids = model.generate(input_features, forced_decoder_ids=forced_decoder_ids)
+                translation = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+                for line in translation:
+                    st.write(line)