Create app.py

app.py

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+import gradio as gr
+import torch
+import torchaudio
+import torchaudio.functional as AF
+from hg_mms import Transcribe
+
+
+def transcribe(audio_file, lang_id: str):
+    print(f"audio_file={audio_file}")
+    print(lang_id)
+    freq = 16000
+    # Return the transcript.
+    transcript = ""
+    # load the auido file to tensor
+    waveform, orig_freq = torchaudio.load(audio_file.name)
+    # resample audio to 16Khz
+    if orig_freq != freq:
+        waveform = AF.resample(waveform, orig_freq, freq)
+
+    return transcriber(waveform, lang_id), audio_file.name
+
+if __name__ == "__main__":
+    transcriber = Transcribe()
+    inputs = [gr.File(), gr.Dropdown(choices=["amh", "orm", "som"])]
+    outputs = [
+        gr.Textbox(label="Transcript"),
+        gr.Audio(label="Audio", type="filepath"),
+    ]
+
+    app = gr.Interface(transcribe, inputs=inputs, outputs=outputs)
+    app.launch()