Upload app.py

app.py

@@ -0,0 +1,42 @@
+from transformers import pipeline
+import gradio as gr
+import torch
+
+device = 'cuda' if torch.cuda.is_available else 'cpu'
+
+model = pipeline(
+    "automatic-speech-recognition", 
+    device=device, 
+    model='openai/whisper-large-v3',
+    chunk_length_s=30,
+    generate_kwargs={"task": "transcribe"}
+)
+
+def transcribe_audio(mic=None, file=None, return_timestamps=False):
+    if mic is not None:
+        audio = mic
+    elif file is not None:
+        audio = file
+    else:
+        return "You must either provide a mic recording or a file"
+        
+    gr.Info(f"Device: {device}") 
+    with torch.no_grad():
+        result = model(audio, return_timestamps=return_timestamps, batch_size=8)
+        if return_timestamps:
+            return result['chunks']
+        else:
+            return result['text']
+        
+
+gr.Interface(
+    fn=transcribe_audio,
+    inputs=[
+        gr.Audio(sources=["microphone"], type="filepath"),
+        gr.Audio(sources=["upload"], type="filepath"),
+        gr.Checkbox(label="Add timestamps?")
+    ],
+    outputs="text",
+).launch()
+
+