Create app.py

app.py

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+import gradio as gr
+import torch
+import librosa
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+
+# Load Wav2Vec2 Model
+MODEL_NAME = "facebook/wav2vec2-large-960h"
+processor = Wav2Vec2Processor.from_pretrained(MODEL_NAME)
+model = Wav2Vec2ForCTC.from_pretrained(MODEL_NAME)
+
+def transcribe(audio_file):
+    """
+    Transcribes speech from an uploaded audio file or live microphone input.
+    """
+    try:
+        # Load and convert audio to 16kHz
+        audio, rate = librosa.load(audio_file, sr=16000)
+
+        # Convert audio to tensor format for Wav2Vec
+        input_values = processor(audio, sampling_rate=16000, return_tensors="pt").input_values
+
+        # Run the model for transcription
+        with torch.no_grad():
+            logits = model(input_values).logits
+
+        # Convert predicted tokens into text
+        predicted_ids = torch.argmax(logits, dim=-1)
+        transcription = processor.batch_decode(predicted_ids)[0]
+
+        return transcription
+
+    except Exception as e:
+        return "Error processing file"
+
+# UI Build
+interface = gr.Interface(
+    fn=transcribe,  
+    inputs=gr.Audio(sources=["microphone", "upload"], type="filepath", label="Speak or Upload Audio"),
+    outputs="text",
+    title="Wav2Vec2 Speech-to-Text Transcription",
+    description="Speak into your microphone or upload an audio file to get an automatic transcription.",
+    live=True  # Real-time microphone processing
+)
+
+interface.launch(share=True)