Upload app.py

app.py

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+import gradio as gr
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+import torchaudio
+
+def translate(audio):
+    model_id_asr = "openai/whisper-medium"
+    processor_asr = WhisperProcessor.from_pretrained(model_id_asr)
+    model_asr = WhisperForConditionalGeneration.from_pretrained(model_id_asr)
+    forced_decoder_ids = processor_asr.get_decoder_prompt_ids(language="tamil", task="translate")
+    input_features = processor_asr(audio["audio"]["array"], sampling_rate=audio["audio"]["sampling_rate"], return_tensors="pt").input_features
+    predicted_ids = model_asr.generate(input_features,forced_decoder_ids=forced_decoder_ids)
+    transcription = processor_asr.batch_decode(predicted_ids, skip_special_tokens=True)
+    return transcription[0]
+
+def speech_to_speech_translation(audio_filepath):
+    waveform, sampling_rate = torchaudio.load(audio_filepath)
+    if sampling_rate != 16000:
+      resampler = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)
+      waveform = resampler(waveform)
+      sampling_rate = 16000
+    audio_dict = {
+        "audio": {
+            "array": waveform.numpy(),
+            "sampling_rate": sampling_rate
+        }
+    }
+    translated_text = translate(audio_dict)
+    return translated_text
+
+demo = gr.Blocks()
+
+mic_translate = gr.Interface(
+    fn=speech_to_speech_translation,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs="text",allow_flagging="never")
+
+with demo:
+    gr.TabbedInterface([mic_translate], ["Local Tamil Translator"])
+demo.launch(debug=True, share=False)