Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+import numpy as np
+from transformers import pipeline
+import torch
+
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+transcriber = pipeline("automatic-speech-recognition", model="mahimairaja/whisper-base-tamil", \
+                        chunk_length_s=15, device=device)
+transcriber.model.config.forced_decoder_ids = transcriber.tokenizer.get_decoder_prompt_ids(language="ta", task="transcribe")
+
+def transcribe(audio):
+    return transcriber(audio)["text"]
+
+TITLE = "ASR for ALL - Democratizing Tamil"
+
+demo = gr.Blocks()
+
+mic_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs="text",
+    title=TITLE,
+)
+
+file_transcribe = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(source="upload", type="filepath"),
+    outputs="text",
+    examples=[
+        "assets/tamil-audio-01.mp3",
+        "assets/tamil-audio-02.mp3",
+        "assets/tamil-audio-03.mp3",
+        "assets/tamil-audio-04.mp3",
+    ],
+    title=TITLE,
+)
+
+
+with demo:
+    gr.TabbedInterface(
+        [mic_transcribe, file_transcribe],
+        ["Real Time Transcription", "Audio File", ]
+        )
+
+demo.launch(share=True)