Create app.py

app.py

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+import streamlit as st
+import torch
+import librosa
+from datasets import load_dataset
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+# (You may need to install Streamlit if you haven't  already: pip install streamlit)
+LANG_ID = "en"
+MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-english"
+
+st.title("Speech Recognition App")  # Give your app a title
+
+# Load the model and processor (do this outside the main function for efficiency)
+processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
+model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
+
+def speech_file_to_array_fn(audio_file):
+    speech_array, sampling_rate = librosa.load(audio_file, sr=16_000)
+    return speech_array
+
+def process_audio(speech_array):
+    inputs = processor(speech_array, sampling_rate=16_000, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+    predicted_ids = torch.argmax(logits, dim=-1)
+    predicted_sentence = processor.batch_decode(predicted_ids)[0]
+    return predicted_sentence
+def main():
+    uploaded_file = st.file_uploader("Choose an audio file (.wav format)", type='wav')
+
+    if uploaded_file is not None:
+        speech_array = speech_file_to_array_fn(uploaded_file)
+        predicted_sentence = process_audio(speech_array)
+
+        st.header("Prediction:")
+        st.write(predicted_sentence)
+
+if __name__ == "__main__":
+    main()