Create app.py

app.py

@@ -0,0 +1,49 @@
+import gradio as gr
+import soundfile as sf
+import numpy as np
+import tempfile
+import torchaudio
+from transformers import AutoModel
+
+# Load ASR Model
+def load_model():
+    return AutoModel.from_pretrained("ai4bharat/indic-conformer-600m-multilingual", trust_remote_code=True)
+
+model = load_model()
+
+def process_audio(audio, language, decoding_method):
+    if isinstance(audio, tuple):  # Recorded audio
+        sample_rate, data = audio
+        temp_wav = tempfile.NamedTemporaryFile(delete=False, suffix=".wav")
+        sf.write(temp_wav.name, data, sample_rate)
+        audio_path = temp_wav.name
+    else:  # Uploaded file
+        audio_path = audio
+    
+    # Load and resample audio
+    wav, sr = torchaudio.load(audio_path)
+    target_sample_rate = 16000
+    if sr != target_sample_rate:
+        resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sample_rate)
+        wav = resampler(wav)
+    
+    # Perform ASR with selected decoding method
+    transcription = model(wav, language, decoding_method)
+    
+    return transcription
+
+iface = gr.Interface(
+    fn=process_audio,
+    inputs=[
+        gr.Audio(source="microphone", type="numpy"),
+        gr.Audio(source="upload"),
+        gr.Dropdown(["hi", "ta", "bn", "mr", "te", "gu", "kn", "ml", "pa", "ur"], label="Select Language"),
+        gr.Radio(["ctc", "rnnt"], label="Decoding Method")
+    ],
+    outputs="text",
+    title="Multilingual ASR with Indic-Conformer",
+    description="Record or upload an audio file, select a language and decoding method, and transcribe it using the AI4Bharat Indic-Conformer model."
+)
+
+if __name__ == "__main__":
+    iface.launch()