Delete main.py

main.py

@@ -1,67 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
-from utils import lang_ids
-import nltk
-nltk.download('punkt')
-
-MODEL_NAME = "openai/whisper-medium"
-BATCH_SIZE = 8
-FILE_LIMIT_MB = 1000
-
-pipe = pipeline(
-    task="automatic-speech-recognition",
-    model=MODEL_NAME,
-    chunk_length_s=30,
-    device='cpu',
-)
-
-lang_list = list(lang_ids.keys())
-
-def translate_audio(inputs,target_language):
-    if inputs is None:
-        raise gr.Error("No audio file submitted! Please upload an audio file before submitting your request.")
-
-    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": "translate"}, return_timestamps=True)["text"]
-
-    target_lang = lang_ids[target_language]
-
-    if target_language == 'English':
-         return text
-
-    else:
-        model = MBartForConditionalGeneration.from_pretrained("sanjitaa/mbart-many-to-many")
-        tokenizer = MBart50TokenizerFast.from_pretrained("sanjitaa/mbart-many-to-many")
-
-        tokenizer.src_lang = "en_XX"
-        chunks = nltk.tokenize.sent_tokenize(text)
-        translated_text = ''
-
-        for segment in chunks:
-                encoded_chunk = tokenizer(segment, return_tensors="pt")
-                generated_tokens = model.generate(
-                             
-                     **encoded_chunk,
-                     forced_bos_token_id=tokenizer.lang_code_to_id[target_lang]
-                )
-                translated_chunk = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
-                translated_text = translated_text + translated_chunk[0]
-        return translated_text
-
-inputs=[
-    gr.inputs.Audio(source="upload", type="filepath", label="Audio file"),
-    gr.Dropdown(lang_list, value="English", label="Target Language"),
-    ]
-description = "Audio translation"
-
-
-translation_interface = gr.Interface(
-    fn=translate_audio,
-    inputs= inputs,
-    outputs="text",
-    title="Speech Translation",
-    description= description
-)
-
-if __name__ == "__main__":
-    translation_interface.launch()