Create asr.py

asr.py

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+import streamlit as st
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import torchaudio
+
+# Load the model
+@st.cache_resource
+def load_model():
+    processor = WhisperProcessor.from_pretrained("233-Yorozuya/dl_whisper_model")
+    model = WhisperForConditionalGeneration.from_pretrained("233-Yorozuya/dl_whisper_model")
+    return processor, model
+
+processor, model = load_model()
+
+st.title("ASR with Fine-Tuned Whisper")
+st.write("Upload an audio file for transcription:")
+
+# File upload
+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)
+