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Hausa_model.py

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+import streamlit as st
+import torch
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+import librosa
+import numpy as np
+import os
+
+# Page configuration
+st.set_page_config(page_title="Hausa Speech Transcription", page_icon="🎙️")
+
+# Load model and processor
+@st.cache_resource
+def load_model():
+    st.info("Loading the transcription model, please wait...")
+    model = WhisperForConditionalGeneration.from_pretrained(
+        "therealbee/whisper-small-ha-bible-tts",
+        ignore_mismatched_sizes=True
+    )
+    processor = WhisperProcessor.from_pretrained("therealbee/whisper-small-ha-bible-tts")
+    return model, processor
+
+# Transcription function
+def transcribe_audio(audio_path, model, processor):
+    # Load and resample audio
+    audio, sampling_rate = librosa.load(audio_path, sr=None)
+    if sampling_rate != 16000:
+        audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
+
+    # Prepare inputs
+    inputs = processor(
+        audio, 
+        sampling_rate=16000, 
+        return_tensors="pt", 
+        language="ha"
+    )
+
+    # Generate transcription
+    with torch.no_grad():
+        outputs = model.generate(inputs.input_features, task="transcribe")
+
+    # Decode transcription
+    transcription = processor.batch_decode(outputs, skip_special_tokens=True)[0]
+    return transcription
+
+# Streamlit app
+def main():
+    st.title("Hausa Speech Transcription")
+    st.write("Upload a Hausa language audio file for transcription.")
+
+    # Load the model and processor
+    model, processor = load_model()
+
+    # File uploader
+    uploaded_file = st.file_uploader(
+        "Choose an audio file", 
+        type=['wav', 'mp3', 'ogg'],
+        help="Upload a Hausa language audio file."
+    )
+
+    if uploaded_file is not None:
+        # Get the file extension
+        file_extension = uploaded_file.name.split('.')[-1]
+        temp_audio_path = f"temp_audio_file.{file_extension}"
+
+        # Save the uploaded file
+        with open(temp_audio_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+
+        # Display the audio player
+        st.audio(temp_audio_path)
+
+        # Transcription button
+        if st.button("Transcribe"):
+            with st.spinner("Transcribing audio..."):
+                try:
+                    transcription = transcribe_audio(temp_audio_path, model, processor)
+                    st.success("Transcription complete!")
+                    st.write(transcription)
+                except FileNotFoundError:
+                    st.error("Audio file not found. Please try uploading again.")
+                except ValueError as ve:
+                    st.error(f"Value error: {ve}")
+                except Exception as e:
+                    st.error(f"An unexpected error occurred: {e}")
+                finally:
+                    # Clean up temporary file
+                    os.remove(temp_audio_path)
+
+# Run the app
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+torch
+transformers
+librosa
+numpy
+pydub
+soundfile