Update app.py

app.py

@@ -1,13 +1,13 @@
 import streamlit as st
 from transformers import WhisperProcessor, WhisperForConditionalGeneration
 import torchaudio
+from io import BytesIO
 
 # Load the model
 @st.cache_resource
 def load_model():
-    processor = WhisperProcessor.from_pretrained("233-Yorozuya/dl_whisper_model", use_auth_token=True)
-    model = WhisperForConditionalGeneration.from_pretrained("233-Yorozuya/dl_whisper_model", use_auth_token=True)
-
+    processor = WhisperProcessor.from_pretrained("233-Yorozuya/dl_twi_asr")
+    model = WhisperForConditionalGeneration.from_pretrained("233-Yorozuya/dl_twi_asr")
     return processor, model
 
 processor, model = load_model()
@@ -19,17 +19,26 @@ st.write("Upload an audio file for transcription:")
 audio_file = st.file_uploader("Choose an audio file", type=["wav", "mp3", "ogg"])
 
 if audio_file:
-    # Load and preprocess audio
-    audio, rate = torchaudio.load(audio_file)
-    audio = torchaudio.transforms.Resample(orig_freq=rate, new_freq=16000)(audio)
-    inputs = processor(audio[0].numpy(), sampling_rate=16000, return_tensors="pt")
-
-    # Perform inference
-    with st.spinner("Transcribing..."):
-        predicted_ids = model.generate(inputs.input_features)
-        transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
-
-    # Display result
-    st.subheader("Transcription")
-    st.write(transcription)
-    
+    try:
+        # Convert uploaded file to bytes
+        audio_bytes = BytesIO(audio_file.read())
+        audio, rate = torchaudio.load(audio_bytes)
+        audio = torchaudio.transforms.Resample(orig_freq=rate, new_freq=16000)(audio)
+
+        # Preprocess the audio
+        inputs = processor(audio[0].numpy(), sampling_rate=16000, return_tensors="pt")
+
+        # Specify the language (Asanti Twi)
+        model.config.forced_decoder_ids = None  # Disable forced language
+
+
+        # Perform inference
+        with st.spinner("Transcribing..."):
+            predicted_ids = model.generate(inputs.input_features)
+            transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+
+        # Display result
+        st.subheader("Transcription")
+        st.write(transcription)
+    except Exception as e:
+        st.error(f"An error occurred: {e}")