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Update app.py
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app.py
CHANGED
@@ -2,17 +2,19 @@ import gradio as gr
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import whisper
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from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
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whisper_model = whisper.load_model("base.en")
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translation_model = MBartForConditionalGeneration.from_pretrained("SnypzZz/Llama2-13b-Language-translate")
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tokenizer = MBart50TokenizerFast.from_pretrained("SnypzZz/Llama2-13b-Language-translate", src_lang="en_XX")
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def
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# Transcribir
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transcription = whisper_model.transcribe(audio_file, language="english")["text"]
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# Traducir
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model_inputs = tokenizer(transcription, return_tensors="pt")
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generated_tokens = translation_model.generate(
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**model_inputs,
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@@ -21,29 +23,38 @@ def transcribe_and_translate(audio_file, target_language):
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translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
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return translated_text.strip("[]' ")
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with gr.Blocks() as app:
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gr.Markdown("## Transcripci贸n y Traducci贸n de Audio")
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#
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with gr.Row():
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language_dropdown = gr.Dropdown(
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["de_DE", "es_XX", "fr_XX", "sv_SE", "ru_RU"],
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label="Selecciona el idioma de traducci贸n",
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value="es_XX"
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)
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# Bot贸n y salida de texto traducido
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with gr.Row():
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translate_button = gr.Button("Transcribir y Traducir")
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translation_output = gr.Textbox(label="Texto Traducido")
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# Configuraci贸n
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translate_button.click(
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inputs=[audio_input, language_dropdown],
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outputs=translation_output
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)
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import whisper
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from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
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## Cargar modelos de Whisper
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whisper_model = whisper.load_model("base.en")
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## Cargar modelos de MBart
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translation_model = MBartForConditionalGeneration.from_pretrained("SnypzZz/Llama2-13b-Language-translate")
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tokenizer = MBart50TokenizerFast.from_pretrained("SnypzZz/Llama2-13b-Language-translate", src_lang="en_XX")
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## Funci贸n para transcribir y traducir el audio
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def transcribe_translate(audio_file, target_language):
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# Transcribir audio con Whisper (aqu铆 se usa la variable whisper_model)
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transcription = whisper_model.transcribe(audio_file, language="english")["text"]
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# Traducir texto a idioma seleccionado (aqu铆 se usa translation_model y tokenizer)
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model_inputs = tokenizer(transcription, return_tensors="pt")
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generated_tokens = translation_model.generate(
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**model_inputs,
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translated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
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return translated_text.strip("[]' ")
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## Interfaz de Gradio
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# Est谩 creado en filas para "organizar" la distribuci贸n de cada caja
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with gr.Blocks() as app:
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# T铆tulo
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gr.Markdown("## Transcripci贸n y Traducci贸n de Audio")
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# Primera fila -> Input de audio y elecci贸n del idioma
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with gr.Row():
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# Audio
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audio_input = gr.Audio(label="Subir o grabar audio en **ingl茅s**", sources=["upload", "microphone"], type="filepath")
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# Elecci贸n de idioma
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language_dropdown = gr.Dropdown(
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["de_DE", "es_XX", "fr_XX", "sv_SE", "ru_RU"],
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label="Selecciona el idioma de traducci贸n",
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value="es_XX"
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)
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# Segunda fila -> Bot贸n y salida de texto traducido
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with gr.Row():
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# Boton
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translate_button = gr.Button("Transcribir y Traducir")
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# Caja de texto (output)
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translation_output = gr.Textbox(label="Texto Traducido")
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# Configuraci贸n bot贸n
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translate_button.click(
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transcribe_translate,
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inputs=[audio_input, language_dropdown],
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outputs=translation_output
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)
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##Iniciar aplicacion
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app.queue().launch()
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